So the room’s takeaway was clean: top-of-funnel CTV doesn’t work for us.

Except that test never asked a question about CTV.

Count what was actually new

Let me total up what was new in that “one test.”

A new creative concept, and only one of them.

A new format, a brand focused ad instead of the usual direct-response, USP-led style.

A new channel.

A new objective on that channel.

A new funnel structure

A new measurement instrument nobody had used before

That was never one test. It was a bunch stacked on top.

So the only thing we can confidently say is “This combination of things - did not show results on this test”

When we already have preconceived ideas like “CTV doesn’t work” these tests cement that belief. Now, I’m not a CTV advocate, not by a long shot, but I don’t think this test was anywhere near that conclusion.

A winner is a joint event

When you get a winning ad or channel, you did not prove a variable. You proved a combination.

The product, the angle, the creator, the offer, the format, the hook, all of it lined up on the same ad. The win is the overlap, not any one piece of it.

Which means your probability of hitting on any given test is basically the product of the odds on each variable you’re holding.

P(win) ≈ P(variable 1 is right) × P(variable 2 is right) × P(variable 3 is right)...

If a variable is proven, its term is close to 1. It barely drags the number down. If a variable is new, its term is well under 1, and it multiplies everything else.

Most people get a winner and then keep exactly one ingredient from it. Same product, brand-new everything else.

You need to know which ingredients are the most important.

Not all variables weigh the same

The variables carry very different weight. A change in product or angle can decide the whole ad. A change in audio quality? Less important.

If you made me rank them, top to bottom, it looks something like this:

1. Product

2. Angle

3. Creator

4. Offer

5. Key selling points

6. Filming Location

7. Ad Length / CTA

8. Audio Quality/Lighting (*more expensive =/= better here)

We’ve run regressions across a lot of these variables but it’s different account to account and can change over time as well (Getting a A+ creator or offer may jump it up the list)

So the real rule isn’t “change one thing.” or even “only make one big change at a time” (impossible) It’s know when you are changing multiple important variables and evaluate appropriately)

Quick Example

New creative on Meta, everything else proven, hits at something like 15%, with maybe a 5% shot at a real, big winner. Those are my numbers from my own accounts.

Now stack that YouTube test back up. New creative, call it 15% (generous for new). On a new channel, my gut says maybe 30% that top-of-funnel YouTube is even a fit. On a new strategy, maybe 10% you get an untested strategy right on the first swing. The channel and strategy odds are pure priors, not measured, so don’t hold me to them. But run the multiplication:

Under half a percent for any kind of win. For a big winner, swap in the 5% and you’re at 0.15%.

You ran a test with a sub-one-percent chance of a real result, it failed, and you filed it under “the channel doesn’t work.” Do that too often and you end up over concentrated.

Mistake 1: Changing too much & Learning too quick

Sometimes you have to change a bunch of variables. There are local maxima that you find through iterations, and absolute maxima that you find through jumping and changing a bunch of things

Small iterations can help you climb your local hill, but you have to make a big jump to find a new hill that’s even taller:

Let’s imagine the axes here are channel and ad type for the X and Z axes, and the Y axis is total performance

If you’re on the small hill, let’s call that Google. Then certain keywords and angles might work

But if you want to jump to the bigger hill, let’s call it Facebook ads. You may need a completely different selling point and ad type to make that work.

Don’t hear me say that changing a lot of variables is a bad idea and you should never do it. Rather, you just need to understand that if you’re going to change multiple variables at a time, your probability of success is low. You need to take multiple swings at bat before you make any conclusive decisions about any of those individual variables.

Mistake 2: changing too little & Giving Up

The second concern is making too small a change. Either making a change on a variable that doesn’t matter (like the background color of a static ad) which has very little impact and is not likely to change. If you’re running it on Facebook, it’s going to get grouped as the same ad anyway. It won’t get spend, and it will just fatigue the original winner faster if you force it.

Do your best to understand the most important variable when you find a winning concept. Then do tests where you keep that and change other variables, or keep all the other variables and change only that.

Control Variables, but only the most important ones. Marketing doesn’t have time for scientific rigor.

When you hold the proven variables

Even when you do hold the heavy variables, the hit rate moves a lot.

In one big account, I looked at creatives that called out a specific version of the product, tied to a partner most people would recognize. The product, the offer, and that selling point stayed fixed; we varied only the execution and the creator.

Reading the dashboard out loud, they still only had a 30% hit rate. Release the selling point, hold just product and offer: I think around 15%, still strong. Release everything, the account baseline across all products and all selling points: about 11%.

I’ve watched the same thing from the other side. Take two or three top ads, push them out to ten or twenty creators with one instruction: make your version of one of these. One or two win, the other eight or nine I don’t go back to. The concept is proven, so the only new variable is the creator & execution, which is exactly what I’m trying to isolate.

Why a low bar is the right bar

First, most of marketing operates on a power law, the top 1% drives the bulk of the performance. (i.e. not a standard distribution)

Second is that creative fatigue and competitors copy you.

You need to stay in front of everyone, which means you need to be constantly coming up with new ideas, new concepts, and new angles to beat that fatigue and stay novel.

If you’re constantly increasing the bar on yourself, you won’t see an increase in the percentage of hits because your overall quality will be going up. Instead, you’ll be raising the bar as you go for either higher spend per ad or higher return on ad spend with those ads.

That constant increasing of the bar of performance is what scales a company

What I actually do

Pin one, vary the other. But only for important stuff that is worth controlling. (Again this is not the rigor of a scientific test, we still need to move fast here). OR if you vary multiple variables, test multiple times (So if you vary creator + angle but test 3-4 angles and none hit? Then I feel good moving on from the creator but not the angles).

Port proven assets into new channels. If you want to test CTV, don’t build a brand-new asset on an untested angle for it. Take something already converting and keep as many of the learnings as possible.

When you can’t isolate, sequence. Instead of teasing four confounded variables out of one messy test, run 20% off versus 30% off. If 20 wins, next round is 20 versus gifts. One variable per round, winner carries forward. Slower is better than confused.

Run explore and exploit as two portfolios. When the hit rate drops and old ads have fatigued, pull back to proven variables and rebuild the base. When it feels healthy again, spend it back down on net-new.

My rule of thumb: at $100k a month, 70% new concepts and 30% variations. Past $1M a month you can go 50/50. Never below 50% new.

Change the metric when you change the heavy variables. A brand-new pain point is not going to convert at your six-month-old CAC on day one. If the thumbstop and engagement rates are in line with your BAU creative, and CPA is within 30%, then keep it running and optimize the page while the audience warms up.

Read aggregates, not verdicts. Tag everything MECE, one and only one answer per field: static or video, what’s the hook, what’s the selling point, what’s the problem. Then roll it up. You’ll find a whole category, say educational awareness ads, is carrying you, which is a real finding. One ad dying is not.

Learn from individual wins, learn from aggregate losses.

The certainty isn’t late, it’s unavailable

A founder told me recently he was frustrated he couldn’t find where he had legs, couldn’t figure out the next ad to produce. The honest answer is that you never get there. The moment you know exactly what the next winning ad is, the bar goes up, you scale spend 50%, and now the bar is higher and the challenge is one level harder. The certainty you’re waiting for is what moves the bar and now you are in uncharted land again. Congratulations.

Considering testing a new channel?

Write down everything you are changing and evaluate the probability of success first.

Then produce the right number of tests to give a fair chance of winning BEFORE you start.

Cheers,

Curtis

Attributed to John Wanamaker, the Philadelphia department store magnate. It’s one of the most quoted lines in advertising.

The same line gets pinned on Lord Leverhulme (the British soap baron who built Unilever), and sometimes on Henry Ford or JC Penney, depending on who’s telling it. Ogilvy quoted it in Confessions of an Advertising Man back in 1963 and attributed it to “Lord Leverhulme, and John Wanamaker after him.” Jeremy Bullmore checked the archives, found nothing, and decided it's all probably folklore. Its important enough that people want to think and believe it must have come from someone really smart.

Whoever said it was pointing at the oldest problem in the business: some of your budget is always getting wasted, and it’s not easily transparent.

If you’ve been reading my newsletter for a while, you know I love attribution, and if you haven’t, you should read this one. It’s still my favorite things that I’ve ever written.

2. “The only advantage I want is a starving crowd.” (Gary Halbert)

Gary Halbert was a famous direct mail copywriter. He wrote The Boron Letters, a series of letters to his son Bond, from a federal prison cell in 1984, which is a story in itself.

He sets this one up with a thought experiment. Imagine you and I both run a hamburger stand and we’re competing to sell the most burgers. What advantage would you want?

People usually say better meat, a great location, lower prices, a secret sauce.

Halbert says sure, take all of that. He only wants one thing for himself: a starving crowd.

It’s a simple idea that’s easy to forget: demand does most of the heavy lifting. You can have sharp creative and a clean landing page, but if the people seeing it don’t really want the product, it’s a slog.

1. Pick a market that’s growing

2. Pick a product that has (or could have) demand

3. THEN make a better product

4. THEN market it well

Don’t try to do it backward.

Don’t market something people don’t want (and don’t ask me to do it either).

3. “The aim of marketing is to know the customer so well the product sells itself.” (Peter Drucker)

Drucker wrote this in Management back in 1973, and it pairs nicely with Halbert.

Halbert says find a hungry crowd. Drucker says get to know that crowd so well you barely have to sell to them.

He’s basically saying the goal isn’t cleverer ads. It’s understanding the customer well enough that the product mostly speaks for itself.

In an ad account, the best-performing creative usually isn’t the most creative. It’s the ad that describes the customer’s problem back to them so accurately they feel understood.

How do you find it? It comes from reviews, DMs, support tickets, and the actual words people use when they explain why they bought. CALL YOUR CUSTOMERS.

4. “If it doesn’t sell, it isn’t creative.” (David Ogilvy)

Ogilvy didn’t really come up with this line. He credits the Benton & Bowles agency.

Either way, the idea holds up. An ad’s main job is to sell. If it’s gorgeous but doesn’t sell, it didn’t do the thing it was there to do. If it’s a little ugly and sells, it worked.

The easy trap is falling for a concept because it’s clever. Clever ads make a founder + a team feel ‘cool’ but unless you are selling feeling cool somehow it probably won’t move the needle.

Just don’t make the opposite mistake of saying “if it’s not tracked by a clear click it didn’t sell” that is also not true. Fix your attribution, then fix your creative.

5. “Your brand is what other people say about you when you’re not in the room.” (Jeff Bezos)

Remember that half of your budget you can’t quite measure? A good chunk of it lives here. There is a reason I put this right after #4, these quotes need to live side by side in your brain.

Your brand is the stuff people say about you when there’s no pixel around. The word of mouth, the recommendation in a group chat, the “oh yeah, I’ve heard of them” that quietly makes your next ad convert a little better.

Attribution is never going to fully catch that. Your last-click report can’t see the friend who vouched for you over dinner.

For anyone running paid, I focus on ads that do three things at once.

1. Drive New Purchases (We know they are compelling)

2. Drive New Reach (Not just retargeting)

3. Speak to selling points (Not just promos)

Think about it. Of the people who watch 15s of a Meta ad (thruplay) 99%-99.5% won’t purchase (at least directly). If NC ROAS is similar, pick the ad that uses that 15s to speak selling points to new people NOT promos to the same people over and over.

Why these five stuck around

These are the things that don’t change.

• Find demand.

• Know the customer.

• Figure out how to sell.

• Build a good brand.

• Realize that you can’t measure perfectly.

If this was useful, send it to your team or share on Social!

Share

Best,

Curtis

Sources

• Bullmore / archives (“no such reference”): Marketing Society, “Why it’s time to say goodbye to IKTHTMISOAIW” at marketingsociety.com/the-library/why-it’s-time-say-goodbye-ikthtmisoaiw

• Wanamaker attribution history: Quote Investigator at quoteinvestigator.com/2022/04/11/advertising/

• Ogilvy citing the line in Confessions + Bullmore context: Conversable Economist at conversableeconomist.com/2018/07/25/half-the-money-i-spend-on-advertising-is-wasted-and-the-trouble-is-i-dont-know-which-half/

• Ogilvy / Benton & Bowles origin: Doug Garnett’s Blog at dsgarnett.wordpress.com/2014/04/07/the-best-ideas-come-as-jokes-but-the-best-quotes-are-sourced/

• Halbert, “starving crowd” (The Boron Letters, Ch. 6): bagerbach.com/books/boron-letters/

• Drucker verified sources (Management / The Essential Drucker): Hammock at hammock.com/marketing/favorite-peter-drucker-quotes-about-customers-and-marketing/

I check three charts before anything else. They answer three questions, in order:

1. Do you have clear winners that are scaled? (Concentration/Optimization)

2. Are you producing enough winners, and are they winning at a healthy rate? (Hit rate)

3. Do those winners last, or do they burn out fast? (Creative half-life)

Run all three and a gap almost always shows up in one of them. Even if it’s not the key thing causing your issues, you’ll still likely find some learning to push into.

One note before we start: this is our internal dash, but the charts below come from demo data, which are realistic examples, but the numbers will not tie out across the three images.

1. Concentration: How Much Rides on Your Best Ads

What it is: take every ad that spent in a window, rank them by spend, and look at what share of total spend the top slice controls.

What you will almost always see: the top 10% of ads should be north of 75% of spend. In a lot of accounts it is higher than that.

Your goal is to balance concentration. Around 40-50% on the top 1% and 75% on the top 10% is good concentration in my experience. It means Meta is doing its job. The platform runs on a power law, where a small number of ads produce most of the results, and delivery concentrates budget into the ads that clear the bar. Those top ads genuinely outperform, so you want them taking the largest share of spend. Flat, even distribution is the warning sign, because it usually means the algorithm has not found clear winners to scale into, or you are manually spreading budget across mediocre ads.

If you are seeing 70-75% of spend on the top 1% of ads, that is also a warning sign that you are over-concentrated and at risk of fast fatigue.

The reason this chart goes first is that once you accept a handful of ads carry the account, everything else follows.

You are not building a big library of decent ads. You are continuously producing the few that Meta will pour money into, and then replacing them as they fade.

2. Hit Rate: Are You Producing Enough Winners

Each month is a bar chart. The dark color is the hits, and the gold line is the percentage rate

What it is: of all the ads you launched, what percentage became winners? The cleanest way to define a winner is by spending (assumes good media buying). An ad is a hit if Meta gave it real budget, meaning its spend cleared some multiple of your median (10x median is what I use).

Why define a winner by spend instead of ROAS or CPA? Because Meta’s own budget allocation is the least noisy signal you have. If the algorithm poured spend into an ad, it found the ad worth scaling. You do not have to untangle attribution to know the platform voted for it. Plus Scalable ROAS is more important than early ROAS.

This chart answers two questions: are you launching enough ads to produce the winners you need, and are they hitting at a healthy rate.

The benchmark: a healthy monthly hit rate sits around 10% to 15%.

• Below 10%: you are producing too much. You are flooding the account with ads, most of which never get off the ground, and diluting your own signal.

• Above 15%: you are producing too little, or without enough diversity. If a high share of what you launch ends up getting spend, you usually are not giving the algorithm enough candidates to choose from, so you end up forcing budget into ads instead of letting the best ones earn it against a wide field.

Read the level and the trend together. If you were scaling well at 10% hit rate and then it dropped to 5%, focus on the drop, not the absolute number.

Then read it against volume. Because so few ads carry the account and winners fade (chart three), you need enough absolute hits every month to replace what is churning and add more on top. A perfect hit rate on too few launches still will not produce enough winners to grow.

* one important caveat is that this chart naturally drifts down. For example, if you are in the middle of August, there are some ads that you’ve launched that are not yet hits and will become hits in the next few weeks as they spend more. You can either adjust this chart to look at the first 7 days of spend, or just get familiar with how the month progresses over time as it matures

3. Creative Half-Life: Do Your Winners Last

Each band is a creative vintage, the month it first spent. Every cohort peaks and then decays.

Drill into one vintage to see its half-life. This January cohort launched in January and kept bleeding spend for months after.

What it is: stack daily spend by the month each ad first spent (its vintage), and watch how each month’s cohort rises and then decays. Click into a single vintage and you can trace exactly how long that batch of creative held up.

What you will see: creative fatigues. Every cohort peaks and fades. Nothing spends forever.

The benchmark I use: creative half-life is roughly 60 to 75 days. Put another way, expect about 25% to 30% of a cohort’s spend to churn each month.

Say you spend $100,000 on a set of ads this month. To hold the same CPA next month, you should expect only $70,000 to $75,000 from that same set, because a chunk of it will have fatigued. To keep CPA flat, you have to replace the $25,000 to $30,000 that fell off. To grow, you have to replace it and add more on top of that.

That is the treadmill, and it is why hit rate and volume are never one-time efforts. You are not filling a library, you are refilling a leaking bucket.

What to do here: drill into your cohorts and find the ads that outlasted the half-life. Study what made them durable, then reverse-engineer the system that produced them, not the exact ad.

Putting It Together:

If you are struggling to scale, this is the scan:

1. Concentration: is spend actually concentrating into a few winners? If it is flat, Meta does not have clear winners to scale into or you have a structure issue.

2. Production: are you launching enough, hitting in the 10% to 15% band, and are those winners sustaining?

3. Durability: are your winners lasting 60 to 75 days, or dying faster? (i.e. you need more creative for any number of reasons)

When you find the gap, go back and diagnose it directly:

• Which month dropped off?

• What were you producing that month versus your best months?

• What is different, both in output (how much you shipped, and how much of it was variations versus net-new concepts) and in systems (how you were researching, briefing, and iterating)?

A couple of common causes worth checking.

You might be running too many variations. If you have already researched a concept heavily and squeezed it hard, more variations of it hit diminishing returns fast. That is often the culprit when output is high but hit rate is falling.

Or you might be running too many net-new concepts, spraying fresh ideas without building on what actually worked.

Either way, the fix is not to copy your old winners line for line. It is to understand the system that produced them (the research, the angle-finding, the process) and re-run that system. The winners were an output. The system is the thing you rebuild.

This is why drilling in matters. Click into a strong month and study the top ads and the creators behind them. Click into a weak month and ask what you were making and why it did not land. You learn far more from the structure of a good month next to a bad one than from any single ad.

Hope this helps!

Curtis

The problem is the freshest formats are usually the most expensive to make. A new UGC angle you can brief in an afternoon. But an animated, Paper-style ad, the hand-cut paper-collage look, has always meant a motion designer, a timeline, and a real invoice.

I made a Claude skill, Script to Paper Style, that does the whole thing in one shot. You point it at a brand and a direction, it does the research, makes an animated ad, and hands it back. One session, no tool-hopping, a new format to throw into testing.

It’s free. The download’s here if you just want it. The next paragraphs are worth skimming if you do test it, though, you need to plug in your own API key.

Example output below as well.

Fair warning up front: this isn’t going to replace your UGC or your studio work, and it’s not trying to. A good editor will be able to improve on it. The goal is that it accelerates your speed to completion.

It all happens inside Claude

The thing I like about it: you don’t leave the chat, and you don’t glue five tools together.

You’re the director, Claude’s the crew. You give it the brand and the direction, it researches the angle, picks a paper metaphor for each beat, generates the frames, animates them, and hands you a finished vertical file. Research, render, done, in one pass.

That’s the whole pitch on “one shot.” Usually “AI video” means bouncing between a script tool, an image tool, a video tool, and an editor, stitching it together yourself. Here you steer, the scripts handle the plumbing, and an ad comes back.

How it works

Under the hood it’s five steps, and Claude runs all of them.

Split the script into beats. A 30-second ad is usually five, one visual idea and one line of voice-over each.

Each beat becomes a paper-collage still (Gemini’s image model, the one people call nano-banana). One frame, one metaphor, no text on it.

Each still gets animated into a 4 to 6 second clip with Veo 3.1, using the still as the first frame. The image sets the composition, Veo adds the motion.

Each line of voice-over runs through Gemini’s text-to-speech.

Then ffmpeg stitches the clips, drops the voice on top, ducks the sound effects under it, and spits out one MP4.

Two ways to run it: let it generate the voice for a finished ad, or skip that and take a fast-cut b-roll bed you narrate yourself.

The only part that costs real money is the video. Veo bills per clip and each one takes a minute or two, so a five-clip ad is five Veo calls. Stills and voice are pennies.

What it actually spits out

Is it perfect? No. Could an editor improve it? Yes.

Did it take 15 minutes in the background for a novel format and a decent ad? Yes.

Should you test it? I think so.

Why the one-shot is actually testable

Novelty is a cheap lever most accounts ignore. Meta rewards fresh creative. In a feed full of talking heads and statics, something that looks completely different buys you a beat of attention you weren’t getting.

More in an account is good.

What you need to run it

The skill’s free. Running it isn’t quite, because it calls Google’s API and the video step bills per clip. Figure a few dollars of Veo for a full ad, not a subscription. Generate one clip first to see the cost before you fire off all five.

You’ll need Node to run the scripts, ffmpeg for the audio (brew install ffmpeg on a Mac), and the skill dropped into Claude Code, which drives everything.

HOW TO GET YOUR API KEY

Go to Google AI Studio at aistudio.google.com/apikey and make an API key (free and often comes with credits to start). Drop it in a .env file in the project folder, one line: GEMINI_API_KEY=your_key_here. That’s it. The scripts read it from there.

Then hand Claude the brand and the angle and let it go.

Go test something new

That’s the whole thing. It’s free, it’s fast, and it makes a format your account probably doesn’t have.

It won’t win every test. Nothing does. But it’s 15 minutes to put something different in front of your audience, and that’s usually worth a slot.

Grab it here. It’s the third skill in the series. Go make something and test it.

Use this list to make more ads, not less.

The Hook (Your First 3 Seconds)

The open is a separate product from the rest of the ad. Spend lots of time here. Think about engagement. Test a lot. But focused on qualifying people over entertaining them.

1. Lead with the problem, not the product. Name the exact pain in the first line before you show what you sell.

2. Get motion into the first 8 frames. A static product shot loses. A zoom, a swipe, a hand entering the frame wins.

3. Put on-screen text in the first second that names a problem or makes a claim. “I stopped buying foundation” beats “New from Brand X.”

4. Open on a human face. Faces lift thumbstop roughly 30% over product-only openers, per Meta’s own Creative Hub guidance.

5. Try a curiosity gap or open loop. Start something the viewer has to keep watching to close.

6. Ask a direct question that triggers a silent yes or no. The internal answer is engagement before anyone clicks.

7. Use a pattern interrupt: street interview, subway clip, behind-the-scenes. Break the visual rhythm of the feed so it reads like anything but an ad.

8. Hook first, brand later. Hold the logo until 5-8 seconds in. Nobody stops scrolling for your logo.

9. On a static, put your strongest element in the top third. That’s the part that survives the crop and the half-second glance.

10. Read hook rate as a triage metric, not a trophy. Under 15-20% is invisible, 25-30% is solid for cold, 30%+ earns scale, and Reels run 5-10 points higher than Feed. Rework anything under 25%. Over that - just focus on qualifying. Don’t aim for 80%.

11. Cut the dead first second from your losers. Most of them waste a logo or a slow zoom before anything happens, and you’re paying for it.

Formats & Pattern Interrupts

12. Master the four foundational formats before anything trendy: UGC testimonials, statics, short video, carousels. These should be 70-80% of your library.

13. Don’t sleep on statics. They drive 60+% of conversions on a lot of accounts, produce faster, and iterate easier than video.

14. Try the Snapchat-style 3-second lead-in. A quick, raw, real-moment open makes the whole ad feel organic instead of produced. It came up as a top pattern in a creative call this week.

15. Run skits and relatable in-car testimonials. Both are consistently top performers right now. Put them in every test batch.

16. Use multi-frame, fast-cut scenes with one unexpected stop moment. A seasonal product ad I broke down this week won by stacking quick scenes around a single visual surprise, then a clear CTA.

17. Test the pet-led hook: a dog POV, an unexpected pet, a “my lab drinks my mushroom coffee” angle. High uniqueness that still lands the benefit.

18. Spin one winning angle into 15-20 variations before you reinvent. Your winners are worth exhausting, not abandoning. Just make them visually different so they hit different audiences.

19. Recut your long-form winners tighter. An old 90-second testimonial that’s fatiguing often comes back recut to ~40 seconds for current pacing. Have extra footage? Do the opposite!

20. Save carousels for education and comparison products. Card 1 hooks with a problem, the middle builds tension, the product and CTA wait for the last card.

21. Build 9:16 first, then 4:5. Vertical covers Reels, Stories, and Feed.

22. Match the format to the audience’s emotional state. For an anxiety product, the format has to evoke the anxious moment while still feeling fresh.

Production: Lo-Fi, Polished & AI

23. Default to low-fidelity, phone-shot, creator-driven content. Smartphone-quality beats studio about 80-90% of the time in my experience.

24. Remember low-fi means low-fidelity, not low-effort. The selling points, the editing, and the creator still have to be sharp. Sloppy is not a style.

25. Know why polished usually loses. High production signals “this is an ad,” and viewers skip ads. Reserve studio for the categories that reward it: luxury, complex demos, some fashion.

26. Test captions - sometimes they outperform sometimes they dont. Test by category not for the whole brand (organic content may perform better without it for example).

27. Keep DR videos to 15-30 seconds. Past that you’re paying for watch time that rarely converts cold.

28. Use AI where it’s actually good: short hooks, product animation, voiceovers. That’s where it earns its keep today.

29. Don’t ship a fully AI-generated ad without a human editor. They don’t scale clean, they get copied fast, and the comments flag them.

30. Layer AI on top of real production, don’t replace it. The hybrid (AI generates, humans refine, AI scales) is what’s outperforming today, not pure AI.

31. Great place to use AI is background generation, image expansion, text variations. One brand saw +45% ROAS from AI backgrounds alone at zero added cost.

Messaging, Copy & Offers

32. Talk to one person. “You” beats “customers,” and a message built for one reader lands harder than one built for a market.

33. Lead with the benefit, not the feature. What it does for them, before what it is.

34. Be specific. “Lost 23 lbs” beats “lose weight.” Specificity is the cheapest credibility you can buy.

35. Stack multiple selling points on screen. A dog supplement ad I reviewed this week won by calling out competitors, listing benefits on-screen, and adding personal testimony.

36. Use “because” to give a reason. People comply far more often when a reason follows, even a simple one. Don’t make a claim you could be backing with a “because.”

37. Address the top objection inside the ad. Name the doubt and answer it on screen before they think it.

38. Power words work, until they don’t. Free, new, proven, limited, instant, guaranteed all pull, but stack seven of them and the reader is numb by the time you reach the CTA. Use one, not all of them. Make it convincing not cheap.

39. Use review language verbatim. Your customers already wrote your best copy. Lift their exact words.

40. Make the offer visible inside the creative. Show the dollar-value deal and the bundle math on screen. Don’t make people read to find the value.

41. Use “free” where it’s honest, and check the law where it isn’t. It’s one of the most persuasive words but also regulated.

42. Give one clear instruction, not three. One imperative CTA: shop now, get started today. Say it on screen and out loud.

43. Add a risk reducer next to the CTA: Afterpay, free shipping, money-back. Remove the last objection at the moment of decision.

44. Give gifts that feel high quality - cheap gifts can be a drag on performance. Cost =/= high value.

Proof & Trust

45. Lead with social proof in skeptical categories. When trust is the barrier, a customer’s words open the ad better than yours do.

46. Use real testimonials in real settings. The whole reason UGC works is that it’s hard to fake. Real customers over actors, every time.

47. Demonstrate the result. Show the transformation or the product working. Don’t just claim it.

48. Run whitelist and partnership ads from real, credible pages. Ads from an actual persons page beat fabricated publisher pages.

49. Test out partnership ads for as many as you can - founder ads, employee ads can work as well!

50. Use credible logos and press as trust shorthand. “As seen in” does quiet work in a half-second glance.

51. Put proof in the body as evidence, not as a second intro. One hook, then proof that backs the promise, not two openings competing.

52. Let the creator’s authentic voice carry believability. The moment it sounds scripted, the trust evaporates.

53. Show the product in use, not sitting on a table. Context beats catalog.

Structure, Pacing & Hold Rate

54. Track hold rate, not just hook rate. Two ads can share a 30% hook and post a 6x ROAS gap because one held 22% of viewers to the CTA and the other held 4%. In 2026 a hook is required, the second 3-15 seconds (thruplay) is where the money is.

    

55. Find the cut where viewers leave and put a pattern interrupt there. Diagnose the dead zone before you reshoot the whole thing.

56. Use the arc: hook, problem, solution, proof, CTA. After you name the pain, move straight to proof. Don’t meander.

57. One core idea per ad. Don’t split the viewer’s attention. Confused buyers don’t buy.

58. Earn each next second. Every beat has to justify the one before it or you lose the viewer.

59. Put your strongest proof right before the CTA. Send them into the decision on the highest note.

60. Cut dead air. Fast transitions, no lulls. The pace itself is a retention tool.

61. End on the offer and a single action. The last frame is the one they act on, so make it the offer, not your logo.

62. Design the first frame as if it were the whole ad. If it can’t sell on its own, the rest of the ad rarely gets seen.

Creators & Briefing

63. Use the hybrid brief: share your top 3-5 ads plus the 3-5 prioritized selling points, then ask for authentic variations. Control plus freedom is where breakout ads come from.

64. Loosen the brief as the creator earns trust. Winners come from creators you let run, not creators you script.

65. Match the creator to the persona, not the follower count. Fit beats reach for performance creative.

66. Be cautious with expensive influencers unless the fit is tight.

67. Consolidate down, not up. Three to five higher-quality creators plus one DR agency beats a long tail of small ones. Test wide then consolidate down once you find the winners. Repeat, don’t keep them all rolling.

68. Run partnership ads from the creator’s own handle. (Yes this is worth repeating)

69. Source creators continuously, and run a small paid test before you scale any of them. Don’t bet budget on an unproven voice.

70. Brief on the emotional state, not just the product. Tell the creator the feeling to evoke and let them find the words.

71. Circulate winning creator cuts internally. A great hook from one brand is a starting point for another. Build the library.

Differentiation & Feeding the Algorithm

72. Be different from your own past ads. Similar creatives compete in the auction and trigger sequencing that quietly kills both.

73. Be different from your competitors. Novelty to the viewer is what lowers your CPMs.

74. Put the hours where the leverage is. If creative is 70-80% of performance, your team should spend its time there, not rerunning budgets.

75. Make it entertaining or genuinely engaging. A strong hook rate alone can pull CPMs down 30-40%, and engagement earns organic reach that compounds your paid scale.

76. Run 8-15 conceptually different ads, not headline swaps. The algorithm penalizes redundancy now.

77. Match big-spend creatives to a big audience and a top product. A niche SKU can’t sustain a million-dollar creative. Top-funnel has to speak broadly to justify the spend.

78. Vary before you repeat across platforms. Running the identical cut everywhere accelerates fatigue and sequencing penalties.

79. Start broad (10M+ interest audiences) and let Advantage+ expand. If you’re still building 500K detailed stacks, you’re over-constraining the algorithm. Turn on Advantage+ creative features while you’re at it, they average +22% ROAS.

80. Treat your best organic post as a paid ad you haven’t run yet. The organic-paid line is gone. Ask your team to forward the organic posts beating Meta and test them.

Testing, Scaling & Portfolio

81. Launch enough volume for the top 1% to emerge. Don’t optimize the median ad. Creative returns are fat-tailed, and a handful of breakout winners carry the whole account.

82. Don’t kill near-winners too fast. Because the returns are fat-tailed, the ad that looks mediocre on day three is sometimes the one that scales. Give it room.

83. Test 3-5 hooks per concept and let thumbstop rate pick. The cheapest way to find a winner is to vary the open, not rebuild the whole ad.

84. Aggregate before you judge. Single-ad reads are noisy. Wait for 10-12 purchases per concept before you decide on something important like an angle.

85. Kill at 2-3x target CPA with no purchase. Don’t nurse a clear loser hoping it turns.

86. For top-funnel creatives, tolerate 10-20% over CPA target when the qualitative signals (comments, watch time, engagement) say product fit. Pull back around 40-50% over.

87. Overscale your proven top-funnel winners. The best ones lift overall account performance 10-30%, not just their own line.

88. Force-test your under-spent, high-efficiency ads. Meta starves some good creatives. Pull 6-10 of them (look back 90 days, include paused), drop them in a dedicated ABO set for 5-7 days, then cut and scale. If one won’t scale, the cause is usually one of three: not enough data, sequencing, or decay during scale, and each has a different fix.

89. Volume tends to pay. Shipping 10+ fresh creatives a week often cuts CPA 20-40% over time, because you’re giving the tail more shots at a breakout.

90. Study your longest-running ads. Longevity is a winner signal. The ad that’s been live a year is telling you what your account actually wants more of.

Fatigue & Measurement

91. Run the production math: one new ad per $3,000 in monthly spend. At $150K/mo that’s ~50 new ads a month just to stay even, and 50-100+ active.

92. Watch the fatigue signals: CTR under 1%, a 20%+ week-over-week CTR drop, frequency over 3.0 on prospecting, or CPA rising with nothing else changed.

93. Don’t over-rely on a single hero creative. Double down AND diversify.

94. Use publisher whitelisting to revive fatigued creative.

95. Don’t read creative performance off click tracking alone.

96. Use the right tool for the question. MTA for ad and campaign-level creative decisions, MMM and incrementality for channel-level allocation. They answer different things.

97. Tag your creative consistently. A clean taxonomy beats a clever one, make it functional

98. Build the promo playbook outside black friday. Put ~20-30% of spend on dedicated promo ads (mostly your top performers with promo banners) and update the site copy for urgency. Use seasonal hooks beyond Black Friday: Memorial Day, Fourth of July, even a birthday-month angle.

99. Treat the landing page as part of the creative. Run PDP copy and CTA tests measured on conversion rate and revenue per visitor.

100. Trust the data over your taste. Your winners often won’t look “good” to you. Ugly ads beat polished more often than anyone wants to admit, and the longest-running, highest-spending ad in your account is usually the proof. Ship the thing the numbers like.

That’s the list. There will be exceptions to them all, thats the joy of marketing, hope it helps!

Share

These were the requirements.

1. Answer quickly, and accurately.

2. Live data: Updated every day in the morning

3. Track performance across different attribution models

4. Be able to review engagement metrics for each creative.

5. See the creatives itself as a link that I can view and embed

6. Descriptions of the creatives that are searchable

7. Use AI tags for all creatives. (type, format, angle, hook, etc.) that are MECE

8. Facial tracking recognition to group by creator

But the more data I added the more AI hallucinated as I was building.

What finally fixed it wasn’t a better prompt or automation system. It was two things working together, and the combination is the whole point of this post.

I put the data into a real database with a clean schema, and I let Claude Code write actual SQL against it (sending reports to slack) instead of asking a model to eyeball a pile of exports.

Then I built a visual analysis for repeat queries or where text answers didn’t cut it.

That’s the unlock. The AI stops guessing because it can write queries to isolate specific data instead of parsing large data sets as an LLM and pair that with a dashboard to see the results, and I can now ask almost anything about the account and get a real response.

I built this around the individual ad creative as the unit. Each ad carries its own lifecycle, its spend and revenue split into new versus returning customers, a transcript of what it says, a description of what it shows, who is on screen in it, and how it decays day by day. The data gets in either through a CSV importer (old way + for testing) that auto-detects Northbeam, TripleWhale, or Meta exports, and an automated pipeline that pulls on a schedule and generates the AI tags.

That’s the plumbing.

Here is what the database really looks like:

Claude has instructions on how each table works and is used and so when I ask a question, it filters down to the right data first then it pulls specific data, and then qualitatively analyzes with just the right data. No context window issue, no hallucination.

With that said, here are some example questions we are building on.

Example Question 1: Which rung is the leak

I can pull one ad, or a whole category, and see the full path from impression to purchase. Play, 3-second hook, holds at 25, 50, 75, 95, and 100 percent, click, landing-page view, add-to-cart, checkout, purchase. It flags the single biggest leak, weighted by spend.

This is the difference between a guess and a diagnosis. A soft ROAS number tells you an ad is underperforming. The funnel tells you why. Great hook and no completion is a creative problem. Great completion and no add-to-cart is a landing-page or offer problem. Opposite fixes. You can only tell them apart if you kept every rung, and the SQL is what makes “show me the leak across these 40 ads” a five-second question.

In platform visual:

Question 2: Which specific creator actually drives results

The account knows who is on screen in every ad, with the timestamp of their first appearance in video. So I can ask “how do ads featuring this exact person perform” across the entire account and get a straight answer.

We still tag ads by creators in the name often, but this is helpful for accounts with 100’s of creators or inconsistent historical tagging.

Creators are one of the biggest levers you have, and “which one is actually working” is important.

Question 3: Whether to even trust Meta’s ratings

I can correlate Meta’s own quality rankings against actual ROAS and CPA and get a verdict, per account: predicts, no signal, or inverted.

(To be honest, not a ton of insight but fun to analyze!)

Most importantly: Ask it anything, at once

Every one of those used to be a separate spreadsheet.

Now I open the database, talk to it in plain language, and say “go to this brand, pull the top ads in this category, tell me what’s working and what the selling points are, then send me the three best.” It writes the SQL, runs it, and comes back with a real answer.

I can move more quickly, dive deeper, and answer more this way.

I still have to validate that it answered the right question, that the tests were set up right, and that the underlying data is right.

But that was true of spreadsheets as well. Bad data in = bad answers out.

Want to build it?

Ask Claude to help you build each piece. Just dump this article into Claude to start. That is genuinely how I would do it.

set up a daily Meta Graph API pull. Use Supabase as your database and a Supabase-based dashboard. You can build the dashboard with Claude, and use Claude Code to interact with both Supabase via MCP and the dashboard to update it.

Meta app set up is annoying but totally doable.

What to skip for now: transcriptions, visual descriptions, facial tagging, AI tags, & Northbeam or Triplewhale imports for different models.

Add those in later.

The middle version (add the creative intelligence). Layer in the AI tags so every ad is classified the same way without you typing it, and add transcripts and visual descriptions so the creative becomes searchable by what it says and shows. This is where you can start asking questions about angles and hooks across hundreds of ads, not just spend and clicks.

The full version (everything talking to everything). Add facial recognition to group by creator, and pipe in Northbeam or TripleWhale so the model is reasoning about incremental revenue, not just in-platform numbers.

You could skip all of this and just use an existing tool as well. That is a legitimate choice, and if that is your preference, there are plenty of good ones.

Just don’t split the middle and point claude at a huge spreadsheet. That’s where you will run into issues.

Liked this post? Share it with a friend or on Social! Thanks!

Share

Chase’s line: AI made content cheap, and now all the cheap content sounds the same.

His take is that brand voice is the one thing AI can’t copy at scale, because it comes from a point of view, not a prompt. He lays out three ways to protect it.

Read the full post on LinkedIn →

2. Marissa Rodriguez: evaluation is the new bottleneck, not execution

Chase says protect the voice. Marissa pushes on the harder question: can you even tell which AI output is right?

Anyone can generate ten versions of an ad in a minute. The rare skill now is knowing which one is actually good, and being able to explain why.

The strategists worth the most aren’t the fastest at making things. They’re the ones with taste sharp enough to evaluate what will work.

Read the full post on LinkedIn →

3. Marissa Rodriguez: stop asking what AI saves you, ask what you’d attempt if it were easy

Most founders ask how much AI can save them. Marissa’s better question: what would you attempt if this were easy?

The savings question keeps you doing the same work for less. The other one opens up work you used to write off as too expensive to bother with.

Not everything you do should be automated / but plenty of things you want to do no can be…

Read the full post on LinkedIn →

4. Michael Kaminsky (Recast): your geo tool is an error tradeoff in disguise

Michael ran 32,000 simulated geo-experiments through the four main open-source tools, then color-coded which results actually contained the true lift.

I’ll let the post speak for itself. I keep thinking about this one...

Read the full post on LinkedIn →

5. Zach Sherrod (Northbeam): the bucket that lies to you

Straight from Northbeam: when you move off last-click, your “Organic + Direct” bucket drops by around 74 percent. Not because organic vanished. Because paid was driving most of it the whole time, and last-click kept handing the credit to the wrong line.

Read the full post on LinkedIn →

6. Charlie de Thibault: a chunk of your budget isn’t underperforming, it’s doing nothing

Charlie’s claim: 20 to 30 percent of your conversion budget probably isn’t underperforming. It’s marginally a profit loser.

Great post on a simple way to think about marginal impact.

Read the full post on LinkedIn →

7. Charlie de Thibault: pushing attributed ROI up every year quietly kills growth

When leadership demands marketing-attributed ROI climb every single year, you eventually starve the spend that was feeding everything else.

Combined with above, know your target, measure well, and then stick to them.

Read the full post on LinkedIn →

8. Taylor Holiday (CTC): the creative quality barbell

Taylor framed creative quality as a barbell. On one end, a $40k produced video. On the other, something filmed on a phone. The $40k video lost to the phone footage.

His point: for a $3 to $10M DTC brand, the middle is the worst place to live. Polished enough to be expensive, not raw enough to feel real, and it loses on both counts.

I really agree with this one.

Read the full post on LinkedIn →

9. Joanna Wallace: a bad editor wrecks a creative team faster than a bad strategist

Joanna’s contrarian one. The fastest way to destroy creative team morale isn’t a bad strategist. It’s a bad editor.

A weak strategist points the team at the wrong target. A bad editor kills good work, rewards safe work, and teaches everyone to stop taking swings. One is a wrong direction. The other rots the engine.

If evaluation is the real skill, then the person doing the evaluating is the highest-leverage seat on the team, and the easiest one to get wrong.

Read the full post on LinkedIn →

10. Preston Rutherford (Chubbies): marginal CAC, not blended CAC

Preston wrote a CEO and CMO dialogue that teaches the difference between marginal and blended CAC - super fun read and again a really clear way of describing the concept.

Read the full post on LinkedIn →

11. Preston Rutherford (Chubbies): Brand vs. Performance is the wrong fight

From the lesson of Chubbies’ nine-figure acquisition. The argument everyone has, Brand versus Performance, is mostly a distraction. The real driver sits underneath both.

As someone who is heavy performance focused I really like learning from Preston.

Read the full post on LinkedIn →

The team and the customer

12. Joanna Wallace: hire creative strategists with agency reps

Joanna makes the case for hiring creative strategists with an agency background over a brand background, every time. Her logic: agency reps are grad school. You see fifty brands, fifty problems, fifty failures, in the time a brand-side hire sees one.

I’m biased, I’ve done both. But the breadth point is real. Just make sure it's not an agency with 40 accounts on each person.

Read the full post on LinkedIn →

13. Eli Weiss: Loyalty is a feeling, not a points balance

Eli’s distinction: a loyalty program and a loyalty strategy are not the same thing. The most loyal customers share one trait. The program makes them feel something, not just earn something.

He may be biased but he has a great point.

Read the full post on LinkedIn →

Quick Summary

Read these back to back and a pattern shows up. Almost every one is about the gap between what a number says and what’s actually true.

Voice versus generic output.

Evaluation versus execution.

What you’d attempt versus what you’d save.

Real lift versus tool error.

Paid versus the credit it gets.

A budget working versus a budget that just exists.

The dashboard improving versus the business growing.

Marginal versus blended.

Brand-versus-performance versus what actually compounds.

A good editor versus a safe one.

Feeling versus points.

That’s the whole game right now. The tactics are mostly known. The edge is in seeing the real number under the convenient one.

Happy Friday!

Curtis

(p.s. please do follow all these people, all great educators)

A strong offer is almost always the reason.

I don’t like talking about offers since it’s a dangerous funnel.

But once you have a great product, great ads, strong media buying, and a website that converts with CRM locked and loaded?

Offer structure can be a huge multiplier on top of that performance when done right.

Sponsored by UpPromote

If you are running offers you need to be VERY CAREFUL who you pick as your affiliate partner.

The most common issue: a brand hands promo codes to anyone with a following, the codes leak to Honey and coupon Reddits, and existing customers find them at checkout. That’s the Bed Bath & Beyond problem, running on autopilot. You pay commission on revenue that was already walking through the door.

The deeper miss is targeting. Every ad dollar you spend has logic behind it: demographics, interests, lookalikes, intent. Then you recruit affiliates by DMing whoever looks big enough. That’s untargeted traffic, and then everyone acts surprised the program is flat.

A real program fixes both. Per-affiliate trackable codes, fraud detection, automated payouts, and partners actually matched to your niche. Run it right and affiliates can drive 30-40% of total revenue. Run it wrong and you pay 5-10% on sales you’d have made anyway.

@UpPromote is the Shopify-native platform I point brands to. 250,000+ stores, $1B in tracked affiliate GMV.

They also built a free tool that does the targeting work for you. Paste your store URL, and it identifies your niche and recommends the 5 partner types most likely to convert for your category, plus outreach templates, in under a minute. The same logic you apply to ads, pointed at partner recruitment.

If you’re running offers and want to make sure you’re paying commission on new revenue, not existing customers, the tool is free and takes under a minute: https://upmote.link/5142ab

Why offers move numbers like nothing else

The mechanism is simple: an offer change touches every impression in the account at the same time. A creative test touches one ad.

When a creative hits, one ad takes off. It pulls spend, carries the account for a while, and eventually fatigues. When an offer hits and is added to your landing page, everything lifts at once.

We’ve seen this drop CPA by 78% at higher spend within a week in some cases.

The second reason offers hit: your evergreen ads fill the funnel, and the offer closes it. At any moment there’s a pool of people who have seen your ads, mostly agree with you, and just haven’t had a reason to act today. The promo collects that demand.

The math is roughly: 99% of viewers don't click, and 95% of clicks don’t convert. Adjust for frequency and impressions that scroll fast (no engagement) and you still get 99% of awareness never buying.

And it compounds on Meta specifically. More conversions in a short window means more signal for the algorithm, faster cash conversion, and better optimization data. The promo doesn’t just convert the funnel. It makes the whole account smarter for a week.

One caveat that applies to every offer win: cheap conversions that don’t hold up downstream aren’t a win. If it’s lead gen, track your close rate. If it’s ecom, track your LTV on promos.

Subscribe now

How to pick the offer

The first thing I ask on a new account: tell me your offer in one sentence, the way a customer would repeat it to a friend.

“$0 down, fast installation” is an offer. “Up to 25% off select styles with code SUMMER25” is forgettable and bland. I’m looking for the first: something specific, memorable, and compelling.

The second thing I pull is the Meta Ad Library for the top five competitors. Your offer competes with whatever sits next to it in the feed, not with whatever you ran last quarter.

From there, the routing comes down to one question: what’s actually stopping the purchase? Every construct exists to kill a specific objection. Name the objection out loud, in the customer’s words, and the offer mostly picks itself.

“It costs too much.” Price is the objection, so price might be the lever. Percent off is the workhorse, and it wins across the board but easts into margin.

Price is the easy one: price is the first thing people say and the last one you should test.

For example, you may be able to increase the value first before a discount. Giving the customer a gift they value at 25% of the order might cost you 5 points of margin. A 20% discount costs you the full 20, and the customer values it less if they think you raised the price to just discount it.

That asymmetry is why gift with purchase is my preference vs discounts (or do both), and the default for subscription brands: perceived value goes up, price expectations stay untouched. Theme the gift to the moment (free gifts for dad in June) and it does double duty as creative. Just make sure the gift is something people would buy on its own or makes the original product more usable. A throwaway gift reads as clutter, not value.

“It’s a big commitment.” High ticket, installation, a long contract. The objection isn’t the number, it’s starting, so terms beat discounts: $0 down, no interest, no payments, free demo.

“What if it doesn’t work for me?” Skepticism is the objection, and you can’t discount your way out of it. The construct is risk reversal: a money-back guarantee, a free trial, a sample size, painless returns. Make it specific: “90 days, no questions” beats “satisfaction guaranteed” every time. Model in a return-rate assumption here. It’s a nonzero cost, but as long as the product is good, it’s better than the CVR hit of not having it.

“I’ll get to it later.” Nothing is blocking the purchase. People mostly agree with you and just have no reason to act today. You don’t need a new offer. You need urgency on the one you have, and the urgency has to be real: tie it to a calendar moment, or to actual scarcity, a kicker that runs while stock lasts. One rule: don’t publicize an end date unless it’s inside the next couple of days. A deadline two weeks out is not a deadline in today’s world.

“I don’t need it.” Stop. No offer fixes this one. If people don’t want the product at full attention, a discount just buys cheaper indifference. Improve the product or find the right audience. Don’t try to offer-hack this. It will just result in bad reviews, low LTV, or returns.

Then decide: one-off or system? If you’ll run offers all year, build one evergreen high-value construct and re-skin it per moment instead of inventing a new offer every holiday. Nine-figure brands run essentially the same offer all year and re-wrap it for each moment: small tweaks or products but same core idea. Same economics, new urgency, and the unit math always works.

Whichever construct you land on, four checks before anything launches:

Stack before you deepen. The cheapest improvements remove friction instead of cutting price: a payment term, a free-shipping threshold, a guarantee, a bonus unit.

Deepening is the expensive move. Make it your last one.

Condition the offer to lift AOV or LTV. Free shipping earns its keep when it’s gated: on subscriptions, or on two-plus units.

A good offer doesn’t just close the sale, it improves the order. If it doesn’t raise AOV or start a longer relationship, it’s margin out the door.

Do the depth math before launch, not during. A 30% discount doesn’t cost you 30%. It comes out of contribution. Take a $100 order with $35 in landed costs: you keep $65. At 30% off you collect $70 and keep $35. The discount cut contribution nearly in half, so your allowable CAC drops with it. Set the in-window CAC target from that math and hand it to your media buyer on day one.

Aim it at the first order. A sitewide discount pays a lot of people who were buying anyway. First-order constructs, the welcome bundle, the starter kit, the new-customer gift, point every dollar of margin give at acquisition. You can’t gate perfectly on Meta, but you can design the offer so its value concentrates on order one.

When to run it

The big holidays all work: Memorial Day, 4th of July, Labor Day, Black Friday, Thanksgiving, Christmas, New Year’s.

Then there are the in-betweens. Spring sale. Summer sale. Most brands chain them: Mother’s Day into Memorial Day into a summer sale into the 4th of July and back into summer. Seasonal language fills the gaps when there’s no actual holiday.

Brand moments work too. If you started your company in February or one of your founders has a birthday away from a holiday, that’s a great time to add another promo.

One timing detail most brands get backwards: promos convert better in the few days after the holiday than before it.

A 4th of July sale on July 1st says you have time. The same sale on July 7th says this might be the last day. Every day past the holiday adds urgency, right up to the point where it’s obviously never ending.

So don’t kill the promo on the holiday. The strongest days are usually still ahead of you.

How to run it

The mechanics matter as much as the offer itself:

Cap promo ads at 20-30% of account spend (by ad not landing page). Never 100%. More on why below.

Two formats are my favorites. Promo statics, plus your top evergreen videos with a scrolling banner on top that says “25% off for the 4th.” Add in dedicated video promos if you can.

80% iterations, 20% new. Pull last year’s holiday winners and rerun them. They reactivate year over year more often than you’d expect. Promo creative requires way less creative diversity in my experience.

Accept the fatigue. Promo ads die 3 to 14 days after the holiday and the account goes back to baseline. That’s fine.

Load the funnel first. In the week before a sale, push DR ads to fill the top of the funnel. Run a bit less efficient than normal (and more efficient during the sale). That awareness has to come from somewhere.

Building the calendar

When, what, and how are useless as trivia. Here’s the assembly order I’d run with a brand starting from zero:

1. Anchor the majors. Pick the four to six proven holidays that fit your category. Health brands don’t skip New Year’s. Nobody should skip Black Friday unless you are REI.

2. Add your brand moments. Birthday month, product anniversary, the category’s natural season.

3. Fill the gaps with seasonal frames. Spring sale, summer sale, fall. Depends how often you want to have some kind of promo. Subscription-heavy brands should run promos more often since initial AOV is less important than LTV, so they should chain these in.

4. Assign one offer construct per slot. Most slots are re-skins of the evergreen offer. Vary the wrapper, and keep it fun.

5. Set windows, not dates. Promo ads start about two weeks before the holiday and run a week past it for most big brands

6. Pre-build both copy variants. An evergreen version and a short-dated version of every asset, written before launch.

7. Tag everything for next year. Name promo ads by holiday so next June you can pull this June’s winners in five minutes.

The concerns are real

Up top I called offers a dangerous funnel, and I meant it. I philosophically hate the idea of going promo heavy. And I still push for promos every holiday, on every brand, because the data keeps winning the argument.

The way to hold both is to take the objections seriously. There are four that matter.

The impression problem. Every dollar on Meta buys an impression, and an impression that says “25% OFF” is an impression that didn’t say why your product matters. Go 100% promo and you’re paying premium holiday CPMs to communicate a price instead of a selling point. That’s the real reason for the 20-30% cap and the banner-on-winner format. The proof points keep running. The offer just closes.

The Bed Bath & Beyond problem. Train customers to only buy with a coupon and you’ve permanently repriced your product. The fix is the evergreen offer. When your everyday offer is already high value, a holiday promo is a re-skin, not a markdown, and you never teach anyone to wait for the markdown.

The hangover. Promos pull demand forward. Budget for the dip after the sale or you’ll spend a week diagnosing a problem that doesn’t exist.

The opt-out tax. Holidays raise CPMs whether you participate or not. If everyone else is running Memorial Day offers and you aren’t, you pay holiday auction prices without holiday conversion rates. Worse, the shopper who just bought your competitor’s deal isn’t buying your full-price product this week. Sitting out isn’t neutral. People won’t remember that you sat out.

The practical takeaway

Six moves, in order:

1. Confirm the fundamentals first. Product, creative engine, media buying, a site that converts. The offer multiplies what’s working. It doesn’t fix what isn’t.

2. Look at your past wins. One ad winning is creative. Everything winning at once is the offer.

3. Name the objection in the customer’s words and let it route you: price, commitment, risk, or timing. Then draft five variants of the construct before your next creative sprint.

4. Cap dedicated promo ads at 20-30% of spend. Banners on proven winners, 80% iterations, rerun last year’s promos.

5. Build the calendar once: anchor the majors, add your brand moments, fill the gaps with seasonal frames, and set windows instead of dates.

6. Load the funnel the week before the sale. Budget for the hangover the week after.

Most operators will test 200 ways to say it before testing one change to what they’re offering.

This month, two words cut a brand’s CPA 78%. The framework above is how you find yours.

If this was useful, send it to someone who has a good offer (or doesn’t). Thanks!

Share

Prime Day 2026 runs June 23 to 26.

Amazon moved the whole event up to June this year after nearly a decade of July dates.

My first job inside marketing was actually Amazon-specific. So I have a lot of Prime Day experience.

My biggest learning? the brands that win Prime Day built their demand months ago, and you can’t change that now. The job this weekend isn’t to build a huge Prime Day. It’s to not mess it up and capture what you built in the past.

Start With What You’re NOT Doing

The fastest way to blow the next four days is trying something new you’ve never tested on huge spend levels.

No new creative concepts. You don’t have time to test, and running untested concepts on the highest-traffic days of the summer is a good way to end up with an unprofitable ROAS.

No optimization strategies you’ve never run. Prime Day is not the moment to try click campaigns, new bidding setups, or anything you haven’t already validated at normal spend.

Friday: Three Decisions (About 2 Hours)

Decision 1: Which channel gets the better deal?

If your site evergreen promo beats your Amazon deal, you split conversions and confuse buyers. If Amazon undercuts your site, your DTC offer looks weak all week.

Or, third, run a “prime time” sale on your site matching the Amazon offer.

Most brands let both channels run whatever was already planned and find out Tuesday that they’re competing with themselves.

Pick a winner on purpose. If your site unit economics are better, give a better site offer (just make it a GWP or offer bar so it's not scrapeable, as long as it's not identical SKUs, Amazon can’t match it back). If you are trying to build Amazon reviews, give a better Amazon offer.

Decision 2: Spend to your inventory, not your ambition.

It’s too late to inbound more FBA units. Count what’s actually sellable and pace your spend against that number.

If units are tight, spend steadily across four days instead of blowing the budget on day one and going dark.

Decision 3: Raise your budgets now.

Prime Day traffic will drain a normal daily budget by mid-morning. Then your ads sit dark during the highest-intent hours.

Raise Amazon campaign caps today. If you’re pushing Meta traffic to Amazon, decide that number today too. That way you have a plan and don’t panic-adjust.

The Weekend: Creative and Email (About 3 Hours)

Badge your winners.

Take your top 3 to 5 ads and put a Prime Day banner on them. That’s it. Your winners already work, they just need a deal flag.

Then knock out a handful of quick Prime Day statics. Product shot, deal callout, urgency line. Make the offer and the product clear.

Don’t edit your live winners, because editing resets learning on the ads currently working. These should be net new ads set up, then paused after Prime Day.

Segment your list

An email to everyone can send your best customers to Amazon, where you lose the subscription, the data, and the margin.

Example segmentation:

Active subscribers & recent buyers never see a Prime Day email. They already pay you regularly. Leave them alone.

Lapsed customers (no recent purchase, not subscribed) get sent to Amazon. They weren’t coming back to your site anyway. A Prime Day purchase from a dead segment is found money.

Then schedule everything now: a launch-day send, a mid-event nudge, a last-chance email Friday. Write the post-event “missed the deal” email this weekend too, scheduled for Saturday the 27th.

Fix your links.

One thing on every ad and email pointing to Amazon.

First, attribution links, so you collect Amazon’s 10% Brand Referral Bonus. It’s free margin so claim it.

Start the push Saturday, not Tuesday.

Amazon Attribution counts conversions for 14 days after the click. Traffic you send this weekend still gets credited when those people convert at deal prices on Tuesday.

Weekend clicks are also cheaper than event-day clicks, because every brand in your category piles into the auction Tuesday morning. So if you’re pushing Meta traffic to Amazon, start Saturday. Not at full budget levels, but a small “get ready” test is worthwhile.

Break it into its own campaign so you can measure it post-window and decide if it's worth increasing or cutting next year.

Monday: Keep the gas on.

The instinct says Prime Day steals from your site, so pull back DTC spend and let Amazon have the week. I recommend the opposite.

Buying intent rises everywhere during Prime Day. People are in shopping mode, and site conversion often lifts during the event even when you’re not pushing traffic there. Pausing your regular Meta campaigns means pulling out of the market during one of the biggest shopping windows of the summer, and resetting learning on the way out.

Leave your DTC campaigns running.

Then verify everything actually renders.

Monday is for checking. The coupon clips. The strikethrough price shows. Subscribe & Save settings are what you think they are. Redirects resolve to the right listings. Scheduled emails point at the right segments.

And check what the final stacked price actually is. A Prime Day coupon that stacks with your Subscribe & Save discount can put you at 40%+ off without anyone deciding that. Check for yourself.

And set your day-of cadence.

Pick three check-in windows for Tuesday: a morning budget check, a midday pacing look, an evening scale-or-kill call. Decide the schedule Monday so Tuesday is execution instead of refreshing dashboards all day.

The Whole Thing on One Page

Friday: pick which channel wins (or run a parallel offer on purpose), pace spend to inventory, raise budgets.

Weekend: badge your top ads, quick statics launched as new ads, segment the list (protect subscribers and recent buyers, push 90+ day lapsed to Amazon), schedule every send including post-event, attribution links and redirects on everything, and start the Meta-to-Amazon push Saturday to load the 14-day attribution window.

Monday: leave DTC running, verify everything renders including the final stacked price, set the day-of cadence.

Tuesday: 12:05 a.m. check, then execute.

The brands that win Prime Day built their demand in March and April. This is just the final checklist.

Did I miss anything? Hit reply and let me know!

Share

Here are the data highlights:

Brands like SHEIN (96.6%), Calvin Klein (93.4%), On Running (85.1%), Obvi (82.1%), and Fabletics (82.5%) are running heavily image-weighted accounts. These are not small brands hedging because they cannot afford video production. These are the largest Meta spenders in DTC.

This does not mean video is less important. Glossier runs 57% video. Honeylove runs 84% video. Both are successful. The point is that static is not a secondary format. If you haven’t cracked static ads or think they could perform better, that is why I’m writing this.

Quick note from Vinay. Curtis is about to hand you the system: 12 formats, 5 axes, one SKU turned into 20+ distinct ads. A healthy account runs 6 to 8 formats and ships 12 to 15 genuinely new statics a week.

Now look at your team and ask who’s actually producing that.

That’s the gap. The strategy above is right. The problem is a person cutting statics at 30 to 60 minutes each can’t hold 6 to 8 formats across 5 axes and still refresh weekly. So accounts quietly collapse back to 2 to 3 formats and one angle, and the algorithm reads it as sameness.

That’s what Notch fixes. Not a clip generator. An autonomous marketer that ships finished, Meta-ready ads across the formats Curtis just mapped. Paste a product URL → Product Hero, Social Proof, Before & After, UGC-Native, statics and video. Point it at a winner → it rebuilds the concept across new formats and personas, not 20 lazy iterations. Feed it your top ad → it branches genuinely distinct variations before fatigue.

Diversity, not just volume. The exact bar Curtis set.

And it pairs with a human, it doesn’t replace one. Your team directs and edits. Notch carries the production load they physically can’t.

This newsletter was brought to you by Notch, the autonomous marketer that ships winning ads. Built by ex-Meta folks behind Facebook Lead Ads and Instagram ads, raised $8M, now running creative for 4,000+ businesses. Readers get 30% off any annual plan → Sign up or book a custom onboarding call with founder Vinay.

Why the algorithm now rewards static diversity

Meta’s Andromeda update changed how creative gets evaluated and served. The algorithm now uses visual recognition to assess creative diversity across your account.

Real creative diversity now means genuine visual, format, and messaging variation. And static images are where you produce that diversity fastest and cheapest. PLUS those placements on Meta are cheaper on a CPM basis.

A static ad takes 30-60 minutes to produce. A video takes 4-8 hours. A small team can produce 15-25 static variations per week. The same team produces 3-5 videos.

AI changes this, but most people I talk to are still seeing better performance from strategically using AI to augment ad production, not to fully outsource it. End-to-end AI production creates volume. Proper AI usage should create efficient quality output.

Statics are cheaper to make, cheaper to place on Meta, and can test diverse angles quickly. Despite having less information density than videos, they still consistently perform in top DTC accounts.

The 12 static formats every DTC brand needs

Here is the system. Twelve distinct formats, mapped to the funnel stage where each does its best work. A healthy account has active ads in at least 6-8 of these at any given time.

01. Product Hero (BOF)

Clean product photography on a neutral or branded background. The product is the entire message.

Works because it eliminates cognitive load for warm audiences. They already know what you sell. The fastest path to conversion is removing everything between them and the product.

Best for fashion, accessories, beauty, home goods. Especially effective on retargeting.

What to test: solid brand color background vs. lifestyle context vs. pure white.

Dr. Squatch | High Impression Ad

02. Social Proof / Testimonial (MOF, BOF)

Customer quotes, star ratings, review counts, expert endorsements paired with product imagery.

Works because when a prospect sees thousands of five-star reviews or a quote from someone who sounds like them, it short-circuits evaluation. The brain defaults to “if that many people trust it, I probably can too.”

Best for supplements, skincare, health, subscriptions. Anything with a trust gap.

What to test: customer review vs. expert endorsement vs. press mention.

Ridge Wallet | High Impression Ad

03. Before & After (MOF)

Split-frame visual showing the problem state versus the result after using the product.

Works because it makes the transformation tangible. The “before” triggers problem recognition. The “after” triggers aspiration. The product becomes the bridge.

Best for skincare, haircare, supplements, fitness, cleaning products. Obvi has built their entire static strategy around this.

What to test: horizontal split vs. vertical split vs. slider-style overlay.

Obvi | High Impression Ad

04. Feature Callout (MOF)

Product image with annotated benefit callouts pointing to specific features.

Works because annotation feels informational, not promotional. The viewer processes it like content. The arrows and callout boxes create a natural reading path.

Best for tech accessories, supplements with ingredient stories, apparel with functional features. Huel excels at this with clean ingredient breakdowns.

What to test: 3 callouts vs. 5 vs. full ingredient breakdown.

Dr. Squatch | High Impression Ad

05. Us vs. Them / Comparison (TOF, MOF)

Side-by-side or chart-style comparison between your product and the competition, the old way, or the category default.

Works by anchoring your product against something the viewer already dislikes. They do not need to evaluate yours in isolation. They only need to agree that yours is better than what they are currently using.

Best for any product displacing an incumbent. Ridge built their brand on this format and has never stopped running it.

What to test: generic category (”old way”) vs. implied competitor vs. direct name.

Ridge Wallet | High Impression Ad

06. UGC-Native / Screenshot (TOF)

Designed to look like organic social content. Selfies, text message screenshots, comment replies, casual phone photos.

Works by bypassing the “ad filter.” After years of scrolling past polished brand creative, people have developed unconscious pattern recognition for ads. UGC-Native earns the half-second of extra attention that separates scroll from click.

Best for beauty, skincare, fashion, food. Glossier has mastered this.

What to test: selfie + text overlay vs. screenshot of texts vs. comment reply.

Glossier | High Impression Ad

07. Grid / Collage / Mood Board (TOF, MOF)

Multiple images arranged in a grid. Pinterest-board energy combining product, lifestyle, UGC, and texture.

Works because grids trigger completeness. The brain wants to scan every cell, which increases dwell time. Each cell can target a different objection or desire.

Best for fashion, jewelry, home goods, beauty with diverse product lines.

What to test: all product vs. product + lifestyle vs. product + UGC.

Glossier | High Impression Ad

08. Offer / Promo (BOF)

Bold typography, discount codes, urgency language, clear CTAs. The conversion closer.

Works through loss aversion. “30% off ends tonight” triggers a fear of missing the deal that is often stronger than the desire for the product itself.

Best for any ecommerce brand running promotions. Especially powerful for the “Four Peaks” strategy of four major sales events per year.

Obvious: Black Friday / New Year / Memorial Day

Less Obvious: Spring Sale / Brand Birthday / Founder Birthday / BIS Sale / Cultural Event Sale

What to test: countdown urgency vs. scarcity vs. exclusivity.

Ridge Wallet | High Impression Ad

09. Ugly / Lo-Fi / MS Paint (TOF)

Deliberately unpolished creative. Handwritten text, crude drawings, mismatched fonts, screenshot energy. The anti-ad.

Works because it violates expectations. In a feed full of polished brand creative, something that looks like it was made in MS Paint creates genuine pattern interruption. The brain registers it as “not an ad.”

Best for brands with a strong established identity who can afford to break it. Also excellent for early-stage brands where authenticity matters more than polish.

What to test: MS Paint text vs. handwritten on paper vs. phone notes screenshot.

Dirty Dog Farm | High Impression Ad

10. Data / Stat Callout (MOF)

One powerful number or data point as the hero of the image. The statistic IS the creative.

Works because specificity feels more trustworthy than qualitative claims. “11,000+ five-star reviews” or “2 million wallets sold” creates instant credibility for the analytical buyer.

Best for supplements, review-heavy brands, subscriptions, products with clinical backing.

What to test: social proof number vs. product spec vs. outcome number.

Obvi | High Impression Ad

11. Meme / Culture (TOF)

Trending meme formats, pop culture references, humor-forward creative with brand messaging layered in.

Works because the brain processes it as entertainment first, advertising second. This earns not just attention but engagement: likes, comments, shares. Those social signals feed back into the algorithm and earn cheaper distribution.

Best for brands with humor-forward identity and a social team that moves fast. Dr. Squatch uses this consistently as a TOF entry point.

What to test: trending template vs. brand-specific recurring joke vs. reaction image.

Dr. Squatch | High Impression Ad

12. Listicle / “Reasons Why” (MOF)

Text-heavy format listing 3-5 benefits, reasons, or ingredients. A mini landing page in a single image.

Works because lists are scannable. The numbered structure promises a specific amount of information and creates a completeness loop. Stacks multiple proof points into one impression.

Best for supplements, meal replacements, tech products, subscriptions. Huel uses this to stack nutritional benefits.

What to test: 3 reasons (cleaner) vs. 5 reasons (more comprehensive).

Huel | High Impression Ad

The 5-axis creative diversity framework

Format diversity is necessary but not sufficient. Genuine creative diversity on Meta lives across five axes. Some formats require some consistent overlap but this is my general framework:

Axis 1: Format. Which of the 12 you are running. Most brands default to 2-3. Top performers maintain 6-8 in rotation.

Axis 2: Visual style. Polished brand creative, lo-fi UGC, editorial photography. Running the same visual treatment across every format is still creative sameness.

Axis 3: Messaging angle. Pain point, aspiration, social proof, curiosity, humor, education. Each angle attracts a different segment.

Axis 4: Offer type. Free gift, percentage discount, dollar-off, BOGO, no offer. Dr. Squatch leads promos with free bags. Other brands never mention price. Test both.

Axis 5: Audience persona. Ridge runs the same wallet targeted at minimalists, travelers, tech workers, and gift-givers with completely different creative for each. Same product, different person in the ad, different context. One SKU becomes 20+ distinct ads.

The practical takeaway: if your account has format diversity but the same visual treatment, same angle, same persona across all of them, the algorithm still reads it as sameness.

Brand teardowns: five DTC accounts to study

Five DTC brands. How they build their static systems. What you can steal.

Dr. Squatch: The Volume Machine

Men’s personal care. Estimated $220-280M revenue. Acquired by Unilever in 2025.

Format mix: Heavy on Meme/Culture, Offer/Promo, and Ugly/Lo-Fi for TOF. Product Hero and Social Proof for conversion. Three-way split between humor-driven TOF, education-driven MOF, and urgency-driven BOF.

What to steal: Volume and iteration velocity. They test more creative than almost any brand in DTC and maintain a consistent brand voice that is humor-forward, masculine, and self-aware. They turn one celebrity partnership (Sydney Sweeney, Mike Tyson, Alan Ritchson) into dozens of format variations.

The opportunity if I were running it: They are extremely video-heavy. Scaling static production to match would open real headroom. Specifically more Data Callout formats built around their ingredient story, more Grid/Collage showing the expanding product line, and more UGC-Native since the audience skews young.

Ridge Wallet: The Premium Product Showcase

EDC accessories. ~$200M annual revenue, $300M+ cumulative wallet sales. Expanded from wallets into rings, backpacks, and luggage.

Format mix: Primarily Product Hero, Us vs. Them, and Feature Callout. Premium product photography on clean backgrounds. Side-by-side comparisons (slim Ridge vs. bulky traditional wallet). Material and color showcases. Offer/Promo around new product launches drives incremental ROAS above 4x.

What to steal: Making a simple product category feel premium and diverse. A wallet is a wallet, but Ridge sustains diversity through materials (aluminum, titanium, carbon fiber, Damascus steel), colors (20+ options), and use cases. Their comparison ads are best-in-class because no copy is needed to understand the value gap.

The opportunity if I were running it: They are very product-focused, which is their ceiling. More Social Proof formats built around the 2M+ wallets sold stat, UGC-Native showing real people using the wallet in context, and Listicle formats. The brand is strong enough to support Ugly/Lo-Fi as pattern interrupt.

Glossier: The Community-Driven Approach

Beauty and skincare. The brand that proved community-generated content could outperform traditional beauty advertising. 42.8% image in our dataset (one of the more video-leaning DTC brands).

Format mix: Centers on UGC-Native, Grid/Collage, and Social Proof. Unfiltered selfies, community mood boards, customer review statics that feel indistinguishable from organic Instagram posts. Product Hero exists but is secondary to people-first creative.

What to steal: Their visual identity is so consistent that even “unbranded” UGC immediately reads as Glossier. That is the mark of a brand system working correctly. They build campaigns around customer content, hitting social proof, diversity representation, and feed-native formatting all at once.

The opportunity if I were running it: They are heavy on TOF and MOF but lighter on BOF conversion creative. More direct Offer/Promo formats, Feature Callout ads explaining ingredient science (they have credibility to go deeper), and Comparison ads (natural approach vs. heavy makeup) would all earn a real test.

Obvi: The Bold Transformation Play

Collagen and weight management supplements. 82.1% image in our dataset. One of the most static-heavy supplement brands on Meta. One of the few openly using AI-generated imagery in static ads.

Format mix: Heavy on Before & After, Offer/Promo, and Data Callout. Bold, colorful, emotionally charged transformation ads. Social Proof statics with high-energy testimonials. Seasonal promos with strong urgency. Bright pinks, purples, bold typography.

What to steal: They have made Before & After their own by keeping it fun and aspirational rather than clinical or shame-based. The “before” is treated with empathy, not embarrassment. The “after” is framed as confidence, not just physical change. In a category where transformation messaging easily becomes problematic, this tonal choice is critical.

Huel: The Education-First Native

Nutritionally complete meal replacement. 67.8% image in our dataset. Highly systematized static creative.

Format mix: Feature Callout, Listicle, and UGC-Native are the core. Annotated ingredient breakdowns are some of the cleanest in DTC. A distinctive “reply to comment” UGC-Native format addresses common objections (”is it actually worth the money?”) in a native, authentic-feeling way.

What to steal: Education that feels native. The reply-to-comment format takes a real customer question and answers it in accessible, non-corporate language. Disarms skepticism by acknowledging the objection rather than pretending it does not exist. Feature Callout ads lead with outcomes (”complete nutrition in 2 minutes”) rather than specs.

The opportunity if I were running it: They play it safe visually. Everything is clean, branded, on-template. More Ugly/Lo-Fi, Meme/Culture (meal prep humor, “what I eat in a day” parodies), and bolder Before & After showing the convenience transformation (messy kitchen + 15 supplements vs. one Huel shake) would push them past their template ceiling.

The 5-minute account audit

Run these ten questions against your current ad account.

1. Are you running ads in at least 6 of the 12 static formats?

2. Do you have both polished and lo-fi visual styles active?

3. Are you testing at least 3 different messaging angles?

4. Do you have ads running with AND without a promotional offer?

5. Are you targeting at least 2 distinct audience personas with different creative?

6. Have you refreshed your oldest running static in the last 4 weeks?

7. Do you have at least one “ugly” or meme-format ad in rotation?

8. Is your static-to-video ratio at least 50/50 by ad count?

9. Are you launching 5+ genuinely new statics per week?

10. Can you name the top-performing format in your account right now?

Score yourself:

8-10 yes: Diversity is strong. Focus on iteration speed.

5-7 yes: Gaps exist. Use the format matrix to identify what is missing.

0-4 yes: Creative diversity is likely your biggest performance bottleneck.

The takeaway

Static images are 55.6% of all ads across the 106 brands we analyzed. Among DTC brands specifically, that number climbs to 64.8%.

There are some brands scaling successfully with 100% or 95% video. But if that’s you, it’s worth occasionally testing new statics to see if you can get one to hit.

The brands winning on Meta in 2026 are running both video and static 90%+ of the time. But the thing that lets them test fast, iterate daily, and maintain genuine creative diversity is a systematic static production process. Twelve formats. Five axes. Six to eight formats active at any time. Twelve to fifteen new statics per week.

When you find a winner, the job is not to crank out 20 more iterations of the same thing. It is to repurpose the winning messaging across genuinely new creative formats. A winning video hook becomes a static headline. A winning static becomes a carousel. A winning testimonial becomes a Data Callout.

Creative diversity beats creative volume. Every time.

Thanks again to Notch for sponsoring the newsletter. I met with them and was very impressed by their products. It's an autonomous marketer that ships winning ads. Built by ex-Meta folks behind Facebook Lead Ads and Instagram ads, raised $8M, now running creative for 4,000+ businesses. Readers get 30% off any annual plan → Sign up or book a custom onboarding call with founder Vinay.

Share

After years of running these accounts, my opinions on what works have hardened. Some tactics I used to defend I now think are a waste of time. Some I dismissed I now consider table stakes.

Im sure ill change my mind on some next year again!

So I built a tier list.

F Tier is “stop wasting time on this.”

S Tier is “this is what compounds.”

If you disagree with where something landed, email me!

F Tier: Stop Doing These

Engagement objectives. You are paying Meta to optimize for the cheapest metric on the platform. Likes don’t buy your product. Unless you are hyper-focused on utilizing these correctly (and using the low-quality ones a good way’s a good way to burn money.

Frequency capping. Every hour you spend tuning frequency caps is an hour you didn’t spend making a new ad. If your audience is fatiguing, the answer is more creative, not less delivery on the creative you have.

D Tier: Mostly a Waste

Lookalike audiences. Meta’s algorithm finds your buyers better than any seed list you’ll ever build. Lookalikes felt like magic in 2018. In 2026 they’re a worse version of adv+ or broad targeting.

Retargeting campaigns. Most retargeting is showing ads to people who were going to buy anyway. The incremental lift on a clean lift test is often very low. If you run these, don’t measure them on click basis (unless they are underperforming on clicks, which we often see, then definitely cut them).

C Tier: Fine, But Stop Obsessing

The CBO vs ABO debate. The honest answer: it depends on whether your media buyer is actually in the account. If they are, ABO gives you more control and lower CPAs. If they aren’t in the account every day, don’t run ABO. Either way, the structure isn’t what’s holding your account back. Its utilizing it well for the product and creative you have that is.

Advantage+ Creative enhancements. Letting Meta auto-crop your ads and overlay random text is often really bad. And many of them just don’t perform well.

Heavy offer-led creative. Promo ads work. They convert. The problem is they train your customers to wait for the next sale. If 70% of your creative is “20% off,” its going to hurt long term growth. I generally target 70 non promo / 30 promo.

B Tier: Solid, Use Them

Interest targeting. (Im going to get hate for this one) Still works on launches when you don’t have data yet. Broad and Advantage+ Audiences outperform it once you have scale. Find an audience of a few million and turn on Advantage+ Audiences, it works like a seed into broad and outperforms on most tests we run.

UGC ads. Table stakes now. Every brand runs UGC. The bar has moved from “do we have any” to “do we have creators who can actually sell on camera.” The format is B Tier because everyone has it. Your creator roster and selling points is what determines whether it actually works.

Static image ads. Fast to produce, easy to iterate, still convert at scale. AI image tools have made these even more efficient. We use designers who use AI to move faster. Most ecom should run 20-30% spend on statics.

Cost caps and bid caps. Great for protecting CAC. The tradeoff is they throttle delivery when you want to scale. I use them most when the performance is volatile.

Whitelisting and partnership ads. Useful format. The lift over standard UGC is real but overhyped, otherwise this would be A tier. If a creator already converts well as a non-whitelisted ad, expect a 10 to 30% lift, not a 2x though. Focus on creators with real names in their handles OR problem specific handles. Worse for generic non person handles.

Long-form video (60s+). 15-30 second ads usually win for DTC. But when a long-form ad rips, it really rips. They tend to be founder stories, documentary-style ads, or product demos where the length is the value. Worth testing!

A Tier: This Is Where Scale Happens

Advantage+ Shopping Campaigns (ASC). Almost every brand should run ASC. Especially low-SKU brands selling to a consistent customer base. Feed it creative, set your budget, and get out of the way. Track performance vs your other campaigns and allocate budget.

Creative diversification. More concepts equals more shots on goal. The brands scaling fastest ship 100+ new ads a month. Not 100 versions of the same concept. 100 different ideas. If your team is shipping 10 ads a month, your ceiling is built in.

Big creative swings. The ads that 10x your account are never the safe ones. Ugly, weird, unexpected. The ad you almost killed in review. Every account I’ve scaled past $1M/month had at least one ad that the brand team initially hated.

Landing page testing. Massively overlooked. Creative testing gets all the attention, but a landing page change can move CVR and doesn’t fatigue. The reason brands ignore it: it’s harder, slower, and requires dev resources. The reason it’s A Tier: the upside is enormous.

S Tier: The Two Things That Compound

After 8 years of running these accounts, only two things consistently compound across every brand I’ve worked with.

A creative testing framework. Not a vague “we test creative.” A system. Specific volume targets, a defined testing window, a kill rule, a scale rule, a rotation schedule. The brands with a real framework outpace the ones without one by 3-5x on creative output and scale much faster.

Founder story ads. Especially when your founder is also your ICP. Nothing I have ever tested outperforms a founder who can sell on camera. The trust signal is impossible to replicate with UGC. The conversion rates are 30-50% higher in my accounts, the ads last longer, and the lift across the whole account is real.

The catch: most founders can’t sell on camera. The ones who can have a permanent advantage.

What did I miss? What did I rank wrong? Reply to this email and tell me where I’m off. Every tier list exists to be argued with.

The theme I saw in brands spending $1m/month+?

Every account I look at is different. Different team structures, different creative pipelines, different attribution stacks, different stages of brand maturity. Across consulting and auditing close to half a billion in total ad spend reviewed, I want to share some themes.

But these aren’t a checklist. Checklists work for the binary, settled stuff. Tracking. Attribution setup. Pixel events. Things where the answer is “this is set up” or “it isn’t.”

They fall apart for the most important pieces.

First, almost every brand I’ve audited has an “affiliate program.” Almost none of them actually have one that works.

What they have is a discount code distribution channel.

The pattern I see over and over: brand hands promo codes to anyone who asks → codes leak to Honey, RetailMeNot, coupon Reddits → existing customers find them at checkout → brand pays commission on revenue that was already coming through.

That’s basically just a tax on your conversion rate.

A real program has per-affiliate trackable codes, fraud detection, multiple commission structures, a creator marketplace, and automated payouts.

Brands doing affiliate right pull 15-40% of total revenue from it. Brands doing it wrong pay 5-10% commission on revenue that was already going to come through the door.

But here’s what most brands miss: a properly tracked affiliate program doesn’t just generate revenue on its own. It becomes your best creative testing lab. You get real purchase data on which creators, which angles, and which content formats actually convert, before you put a single dollar of ad spend behind them.

Brought to you by UpPromote, the Shopify-native affiliate platform powering 150K+ DTC brands. $1B+ in tracked GMV. [Check out UpPromote →]

Now onto the core themes I see.

1. The Power Law of Ad Spend

People often ask “what structure should I use” and I’m happy to share what we do 90% of the time (ABO for testing & scaling / ASC for scaling, tweaking based on account specific tests).

But a better question is “when I test a structure, what tells me it is working?”

3 things

1. Good ads get through testing (breaking ads out later doesn’t produce winners)

2. Fatigued ads get cut (they don’t keep spending forever)

3. Best ads get scaled.

In every Meta account I audit, 5-10% of the ads do 60-80% of the productive work. That part is consistent across scaling accounts. The goal is getting those ads scaled up and then down once they fatigue.

The above chart is dummy data from our internal dash. An example of an account where good ads were overspent and wasted money (ads deep in the red). And other good ads were never scaled enough (deep green). Breaking out tests creates new winners in this case.

Two symptoms show up in the same account:

Roughly 30% of ads are statistically dead but still spending. Past the point where the math says they’ll hit target. Still running. Still spending. Adset/campaign is still live and forcing spend. No new winners for Meta to rotate to so they just keep chugging along.

Roughly 5% of ads are early winners getting starved. Low CPA, low spend, sitting there doing nothing while breakeven ads scale by inertia.

If you are testing, cutting, and scaling ads within your structure then keep it.

I’ve seen it work well in 100% CBO, 100% ASC, a mix of all three. My point is that outcome is more important than structure. I’ve seen bad accounts with every structure and good accounts with every structure.

It depends on the product, the optimization strategy, and the ads. You need all three to align here to get a strong account outcome.

The goal is to get more winners through learning and pull back on fatigued ads before they waste spend. Not to check the box on the “right structure”

2. The Wall Between Media Buying and Creative

One super common disconnect: Target around “producing more ads” but then creative ships ads. Media buying runs them. Nobody is closing the loop between the two.

When a creative wins, what happens next?

Three things should happen

1. Scaling (new tests within Meta + new budget)

2. Iterations (how can we build on this with new creators/formats/angles)

3. Learning (discussion in the team)

The result is a creative pipeline that produces compound learning.

If you don’t do this, you are just repeating the same process over and over. Your team needs to be improving and learning. That’s what keeps setting the bar higher, and puts you in front of your competitors.

It’s important to note that if you don’t optimize your strongest winners it’s hard to tell what to lean on. (Do you iterate on High ROAS or High spend ads??) Once you have an account where fatigued ads are cut and great ads are scaled, this process becomes much easier.

But the media buying team should be involved. There is nuance to which ads are driving top of funnel, which are scaling the fastest, which are seeing fatigue in the last 2 days. Those things all need to be communicated to the creative team.

The highest scaling accounts have a quick weekly cadence here. But they know when to build off a winner and when to wait for data.

3. Creative Diversity Across 5 Axes

Most accounts I audit have “creative volume” as their KPI. That’s great, but meaningless without Creative Performance and Creative Diversity.

Meta’s algorithm rewards genuine creative variation more than it rewards quantity, and most accounts solve volume before diversity.

5 axes I find are consistently important:

Format. Static, video, carousel, story, collection, catalog, dynamic product ads, UGC, lifestyle, founder POV, problem-solution, testimonial.

Visual style. Polished brand creative, lo-fi UGC, editorial photography, mixed media, meme format. Running the same visual treatment across every format is still a form of creative sameness.

Messaging angle. Pain point, aspiration, social proof, curiosity, humor, education. Each angle attracts a different segment of your potential audience.

Offer type. Free gift with purchase, percentage discount, dollar-off, BOGO, no offer. You should be testing across multiple offer constructions even if you have a primary one.

Audience persona. Same product, different person in the ad, different landing page. One SKU can become 20+ distinct ads if you build it around different personas.

Most high scaling accounts are concentrated on one of these dimensions and diverse on the rest. I rarely see brands that are truly diverse across ALL of them and creating excellent ads. But, if you are concentrated across multiple of these - work hard to start diversifying as much as possible, it reduces your fatigue and increases the volume you can scale.

Don’t force diversity, test diversity and lean into what works. Key difference, it’s fine to have some concentration just not too much.

4. Attribution as a Decision Problem, Not a Tech Problem

The standard attribution conversation goes like this: numbers don’t match between Meta and Triple Whale. Meta says 200 purchases, Triple Whale says 140, Shopify says 175. The team spends weeks trying to figure out which one is “right.” They want to report something clean and easy and set KPIs to know if they “hit” or “miss”.

The key thing to understand is that no single attribution model is perfect and can be relied on. Forcing one is bad for the business.

If you use MTA, it undercounts view-through, long-term attribution impact, and omni channel halo.

If you use an MMM, you can’t optimize quickly enough, and you risk a poorly calibrated model.

The best brands know their North Star metric: total customers acquired per dollar or aMER (acquisition MER). They measure channel performance based on total impact, not just clicks. Within each channel, they use click-based tracking tools to ensure the right ads are getting the right spend, either by setting up the Meta algorithm or optimizing on top of it.

They also have all their tracking set up correctly, and they understand the unit economics and LTV numbers. That means they know how to optimize and what numbers to look for.

Too often, brands focus on which single tool they should trust. The better approach is to understand the web of tools and where each one works and where it does not.

5. Okay, Fine, A Short Checklist

I know I just spent too many words telling you not to audit your account like a checklist.

There are a few things that are worth checking anyway.

• Do you have your Pixel set up correctly with deduped CAPI events?

• Is your Conversions API sending hashed email and phone, plus IP and fbp/fbc cookies?

• Are you testing partnership ads (whitelisted creator content)?

• Are you testing ad copy variations, or are you testing landing pages? (Both work. Pick one as your primary lever.)

• Have you evaluated Advantage+ Enhancements and made an explicit decision about which ones to enable?

If you can answer “yes, intentionally” to all five, you get a gold star. ⭐

If you can’t, fix those first.

The Practical Takeaway

Be careful about following the “right system” the “right number of ads” the “right attribution tool” and work to understand the gaps in each. This is the theme I’m seeing across the biggest accounts. It’s not perfection but reliable systems and strong thinking:

4 things

1. Your structure gets good ads scaling and bad ads paused

2. Your creative team compounds learnings

3. Your creative production is expanding ideas not narrowing in

4. Your attribution is nuanced not mono-focused.

Subscribe now

Credit to Hugo - CMO of sassy saints who inspired this idea - if you want the full podcast from him ill share it at the end!

In this model direct creative cost runs about 3-5% of spend. A comparable agency runs closer to 7-15%, in my experience. That’s a nice benefit but not even the main one.

The interesting part is the moat that it builds (again shout out to Hugo for illustrating this clearly)

The 2026 AI Problem

Anyone can spin up 50 static variants in a Claude window. Anyone can generate decent imagery, decent hooks, decent copy. The barrier to making a passable ad collapsed somewhere around mid-2025. “Decent” is the new minimum bar now.

AI ‘UGC’ is getting better and sometimes outperforms standard UGC. You might be scaling an AI ad right now!

But the moat on that is $0. Any brand with any budget can copy that ad in an hour for their own brand. Meta ad library now sorts by impressions, look-a-likes pop up immediately. That causes quicker ad fatigue.

It means that layer of your account is now a commodity. Your competitor’s intern with a $20 Claude subscription can ship your top performer today if it’s a generic AI winner.

That doesn’t mean it can’t perform (I’ve seen it work) but it isn’t a moat you can build on.

Standard Model:

Brand pays for creative. Creator pulled from a roster, briefed, video edited, delivered.

The creator gets paid and then might repeat the next month with a different brand.

So many brands I know are stuck in this loop of deliver → reset and they are trying to compound instead. The question today is how we get to a compounding process.

But you may say “no my agency is excellent, they take the top performers and pull them in and they get weekly scripts! They analyze data and improve each week”

Perfect: Keep them (really), but also, take that and put it on steroids. That is the creator group.

Take your top creators, pull them into a group (slack, telegram, whatsapp, whatever), and start coaching instead of scripting. The cost stays flat. The output gets weirder, more specific, more aligned with what actually scales in the account.

Cost Breakdown

Most creators charge a flat fee per video of roughly $200-300 which often comes out to around 3% of spend depending on scale.

Or once you get comfortable I recommend a model of 5% of spend.

The first is easier to get started, finance isn’t concerned. Creators get clear payments, etc.

The latter is better aligned. If a creator wants to make 20 ads in a day? Awesome, test them in a group and if they have 3 winners that scale, they get paid!

Either way it should include the following:

1. Lifetime usage of edited ads

2. B roll rights (No % of spend costs)

3. Partnership access for all ads from day 1

What Compounds (And Why It’s a Moat)

We evaluate a new creator (before they join the group). They make 1-2 ads, one of them is decent. We started them on the top product with our top USP. Best shot at success. Not bad, not stellar.

Then they join the group.

After a few weeks they hit their stride and start producing more winners. People recognize their ads in the comment as if they are an influencer (Which is great, many selling points, many angles, that is good for the brand) it builds familiarity and comfort.

After a few more weeks they start getting creative: What if we tried X or Y or a new product?

It’s distributed creative strategy.

That’s the moat.

An AI can give you 50 variants of an ad concept in 60 seconds. It can’t give you the angle a creator developed over three months of paying attention to your brand’s actual customers. The AI doesn’t know what it’s like to use a product for months. It can’t personally try out your newest launch. AI UGC can only replicate authenticity at best. And it can’t execute on an angle that you never would have suggested in the first place.

The Partnership Ad Tailwind

If you were at Meta Performance Marketing Summit this year or if you’ve talked to a Meta rep in 2026 you’ve heard them pushing Partnership ads.

It’s the format where the creator’s handle appears on the ad and you whitelist their handle to run it from their account. Reps have told me these ads are getting actively favored by Meta’s algorithm right now.

Partnership ads feel more organic in the feed, which keeps users on platform longer. Meta wins, the creator wins, the brand wins. Everybody is incentivized to push that format.

In my experience ~70% of the time partnership ads outperform standard ads. It’s just hard to manage when you are using new creators every week.

We request whitelisting from every creator in our groups as a condition of working together. That gives us a stable pool of partnership-eligible accounts at all times.

The Playbook / Set up

Sourcing. Most of my top creators came from Billo, were past customers, or actors on backstage. Work with as many creators as you can, wherever you can find them, find the ones that resonate with your brand and pull them in!

Group size. Scales with spend. A brand spending $50K/month doesn’t need more than 5 to 7 active creators. A brand spending $500K+ should run 20+ as you scale (start small). The constraint is cost and keeping the group updated with feedback and approving new ads quickly or giving feedback.

Comms channel. One channel per brand, managed by our team. Ideas drop weekly. Creators can see each other’s work as we share top performers. This is the powerful part. They all want to get a ping that their ad was a winner.

Review workflow. Slack works fine to start. We eventually built an internal tool we call Ad Review that we use in our groups. Creators upload assets, AI reviews them immediately for a first pass, the team reviews and gives feedback in one place, captions automatically added (music coming soon), approved ads uploaded quickly, payments get tracked for creators easily + analytics on common mistakes in ads. Removes the back-and-forth scroll problem that big Slack channels create. Not required to run a creator group, but worth knowing the bottleneck is real and there are ways to engineer past it.

Pay model. Mix of flat and performance. Flat rate per asset to make the economics predictable for the creator. Performance bonuses tied to ads that scale past a threshold. Or % of spend once everyone is comfortable. Best creators end up making meaningfully more than they would on standard UGC rates, which is the loop you want. That keeps them loyal and you can scale quicker and save money testing bad ads. Win win.

Where This Doesn’t Work

A creator group is not a fix for a brand whose UGC was never working. It amplifies what’s already there. If your account has historically performed on statics and your video UGC has never scaled, this won’t change that. Different problem, different solution.

Four places I would not set this up:

Static-heavy brands. If your hero creative is product-on-white and you can’t remember the last UGC video that scaled, you don’t have a creator group problem. You have a format problem.

Brands under $50K monthly spend. The management overhead doesn’t pencil. You’re better off running standard Billo tests until you’re large enough to support the group. Just keep going back to those top creators!

Brands with no internal owner. Someone has to run the Slack. If it’s not your team, it has to be ours. If neither, don’t start.

Brands that need volume over authenticity. Some categories optimize for sheer asset throughput. If you’re testing 200 ads a week and authenticity is not your differentiator, then you can skip this.

The Layer Underneath

Most of the conversation about AI and DTC right now is about workflow. How fast can you generate concepts. How quickly can you spin up variants. How efficient can you make the production layer.

I often talk about a target of 1 ad for every $3k in spend (or 1 concept for $10k in spend). You need to not over optimize for this metric though. Quality first, volume second.

If you are tempted to AI-ify everything to hit the goal then this is for you.

The brands that own their creator relationships are building something AI can’t take from them and Meta is actively boosting.

If you build this out let me know!

As promised, here is the podcast from Hugo, worth a watch!

If this was useful, send it to your team or share on LinkedIn with your favorite graphic and tag me! Best compliment you can give!

Share

I get asked all the time “what makes a good ad” here is my best advice.

First, here is the order I prioritize the data:

Scale. Then efficiency. Then diagnostics.

Once we establish the right evaluation flow we can talk evaluation specifics.

Don’t start with high ROAS:

Here’s what too many people do.

They open Ads Manager. They sort by ROAS. They look at the top ad. They look at hook rate. They look at hold rate. They look at CTR. They form an opinion about why it worked.

Then they do the same for the bottom ad and form an opinion about why it failed.

The problem: the bottom ad spent $200. The top ad spent $400. Neither has enough data to support an opinion about anything. Those conversions might have been flukes or early audiences.

You’re reading tea leaves at this point.

Here is the order of evaluation first before you start looking at ad specifics.

Layer 1: Scale

Before you look at any other metric, ask one question.

Did this ad get enough spend for the result to mean anything?

The threshold varies by account size, but my general rule: an ad needs to clear roughly 5-10x your CPA target before any of its numbers tell you anything. If your CPA target is $50, you want $250-$500 minimum on an ad before you call it a winner or a loser.

Meta’s algorithm spends the first chunk of an ad’s budget testing into warm audiences it already trusts. You’ll notice that you always get new ads pushed to you. Starting there is great, it gives the algo data but it isn’t always indicative of true performance.

The real question isn’t “does this ad work on warm traffic?” (if it was then just run a promo) The real question is “does this ad work on cold audiences Meta hasn’t found yet?”

You don’t find that out at $200 in spend. You find it out at $2,000 or $200,000.

Strong ROAS for 2 days then it dropped off? Just ignore that ad.

Layer 2: Efficiency

Once an ad has cleared the scale threshold, then you look at efficiency.

CPA. ROAS. Contribution margin. Whatever the unit economics metric is for your business.

This is where the ROAS intuition is helpful.

But be careful about being overly reliant on click based tracking.

A problem solution ad may drive better overall performance (tracked by incrementality) than a promotion ad but not show it on your MTA. This is not an excuse to ignore those metrics but rather to measure more carefully.

Layer 3: Diagnostics

Only after an ad has scaled and proven its efficiency do you dig into the supporting metrics.

Hook rate. Hold rate. CTR. Average watch time. Add-to-cart rate. Landing page conversion.

These metrics don’t tell you whether an ad worked. They tell you why it worked and if it can be improved, so you know what to iterate on.

A 60% hook rate on an ad that didn’t get enough spend or perform means nothing. Same goes for hold rate. Same goes for CTR.

But once an ad has scaled and proven it works, the diagnostic metrics let you understand the formula. Then you can iterate intelligently.

This is the order: scale, efficiency, diagnostics. Skip any layer and you’ll waste time.

What Makes an Ad Actually Compelling

Alright so what makes a compelling ad that can scale at high performance?

Here is what I’ve learned:

A compelling ad shares something genuinely unique about your brand, in an engaging way, and communicates the benefit the customer gets from it that is unique to you.

That’s it. That’s the pattern.

Everything else (Format, length, USPs, etc.) shifts in my experience.

Your job as a creative strategist is to figure out how to engage someone’s attention while telling that story. The best ads do it in three beats:

1. Engage and qualify in the first few seconds

2. Explain the differentiation and the benefit

3. Make it compelling to buy

Notice what’s not on that list. CTAs. Problem-solution structure. Specific formats. Those are all optional. They are helpful for specific ads, but they’re not required in my opinion.

Engage AND Qualify

This is the single biggest framing shift I push on clients.

It’s tempting to chase hook rate as if it’s the goal. You are paying for impressions, so higher hook rate equals better ad, right?

No. A high hook rate from the wrong audience is worse than a lower hook rate from the right one.

I’d much rather have an ad with a 30% hook rate that qualifies the right person than an ad with a 70% hook rate that engages everyone but qualifies no one.

A 70% hook rate that pulls in college students for a $200 supplement is a waste of spend. A 30% hook rate that pulls in 45-year-old men with joint pain for the same product is a winning ad.

The job of the first three seconds isn’t to maximize attention. It’s to maximize qualified attention. The right person, not the most people.

You can engage and qualify in lots of ways:

• Problem-solution engages with a pain point only your customer feels.

• Founder story engages with a personal narrative that screens for the right audience.

• Testimonial engages with the kind of person your customer wants to be.

All three work. The format can shift. The point is whether the first few seconds pull in the right person AND give them a reason to keep watching.

Once you figure that out, you can go back and optimize for engagement. But it’s secondary to qualification, not primary.

Differentiate, Benefit, Then Make It Compelling

Once you’ve engaged and qualified the right person, you have a few seconds to make them care.

Differentiation: what’s actually different about your product.

The test: swap your brand name for a competitor’s. Does the ad still work? If yes, you’re describing the category, not your product.

“Clean ingredients” is describing the category. “Only 4 ingredients, no seed oils, made in a facility we own” is differentiation. Specific. Verifiable. Hard to copy.

Benefit: what the customer actually gets.

Features are what the product does. Benefits are what the customer’s life looks like once they have it.

“High in protein” is a feature. “You’ll stop feeling hungry at 3pm” is a benefit. Founders love features because they built around them. Customers don’t translate features into benefits. They just scroll.

If you can’t draw a clear line from a feature to something concrete in the customer’s day, cut the feature.

Compelling to buy: why now.

I try to avoid discounts and fake urgency to handle. It works short term and trains your customer to never pay full price.

The durable version is stakes. What does it cost the customer to keep doing what they’re doing? If the answer is “nothing really,” you have a positioning problem, not a creative problem.

The strongest versions are usually:

• Cost of inaction (every month on the wrong solution is another month wasted)

• Risk reversal (try it for 60 days, full refund)

• Specific moment of relevance (a deadline the customer already has in their life)

Discounts close the deal. They don’t make the case.

When you put all three together cleanly, the ad starts to feel inevitable to the right customer. Thats why this is so powerful and why most of the best ads follow this pattern.

Building Specific Learnings: Evaluating Across Ads

Individual winners = signal. An ad that cleared the scale threshold and posted strong efficiency is real. You can study it. You have stat sig.

Individual losers = noise. A single ad can fail for ten different reasons. The hook was wrong. The hold was wrong. The CTA was bad. The landing page was off. The targeting got weird. You can’t tell which one from a sample of one.

Aggregate patterns across multiple ads = real learning. If five UGC ads with similar hooks all underperformed and five with different hooks all worked, that’s signal you can act on.

The practical rule: don’t take a definitive learning from a single losing ad. Build off what’s winning. Use losers as starting points for new tests, not as diagnostic puzzles to solve.

Variations vs. Concepts

When you find a winner, you have two ways to test off it.

Variations: Take the winner and make three similar versions. Same idea to a new creator, change the first 5 seconds (not just 1), or extend the length heavily. Keep the underlying concept the same.

This is your safest, highest-hit-rate testing. Most variations work because the concept is already proven.

Just don’t make variations like it’s 2024. Small hook changes or CTA changes don’t count anymore.

New concepts off the same insight: Take the core insight from a winner and build three completely different ads around it. Different visual style, different format, different angle, but the same underlying story about why the product matters.

Often these don’t even look like the original ad. But they’re built on what you learned from it.

We find a ton of winners through both paths. The first compounds your existing concept. The second extends what you can do with the insight underneath it.

If you’re only running variations, your account will eventually hit a ceiling. If you’re only running new concepts, you’re leaving easy wins on the table.

A Few More Common Mistakes

Over-indexing on warm audience performance. Your retargeting CPA is not your creative quality signal. It’s your customer base. New customer CPA on prospecting is the real test.

Defaulting to statics because they’re cheap. Statics work. They have a place. But if 80% of your output is static because video is hard, you’re capping your account’s ceiling.

Discount-led creative as the primary lever. It works in the short term and degrades your business in the long term. The brands that win are the ones that figured out how to make creative compelling without a price-led hook.

The “feel cool” trap. Sarcastic, hyper-stylized, snazzy copy that makes the founder feel like they have a cool brand. It rarely converts. The ads that win usually look more boring than the founder wants them to look.

If you’re falling into any of these, the fix isn’t more ads. It’s better evaluation of the ones you have, and a clearer view of what compelling actually looks like.

Putting It Together

Evaluating creative well is mostly about discipline.

Scale before efficiency. Efficiency before diagnostics. Engage AND qualify, not just engage. Differentiate, benefit, make it compelling: in that order. Build off winners with both variations and new concepts. Don’t try to diagnose losers.

And remember the thing this whole framework is solving for:

You’re not trying to find an ad that works for three days on warm audiences. You’re trying to find ads that consistently resonate with cold audiences at scale.

Share

It’s also not revenue. Or scale. Or profit. Those are the scoreboard. The goal (I would argue) is to make something that maximizes the value to people for less than it costs to produce. Everything else is a proxy so that you can fit it on a dashboard.

Understanding this is key to properly managing your ecom marketing team.

A few classic examples: Private equity over-optimizes for profit. Venture capital over-optimizes for revenue. Media buyers over-optimize for ROAS.

Three critiques I got on LinkedIn recently landed on exactly this point.

(to be fair it was arbitrary)

Each of these has its own separate specific critique I could debate, but there’s a more important pattern underneath them that’s I want to focus on, which I will wholeheartedly agree with.

The moment you promote a sub-KPI into a goal, it can become harmful.

That’s Goodhart’s Law. When a measure becomes a target, it ceases to be a good measure.

Nick is critiquing change-log analysis. Taylor is critiquing quality-vs-quantity targets. Barry is critiquing ad-level ROAS optimization.

All of these are powerful tools that are incredibly useful in my experience but if over-simplified and over-optimized, can be dangerous and unhelpful.

Each of the posts I share on LinkedIn is based on scaling companies, $150M+ in total ad spend, and careful attention to accounts. It’s not made up. But be careful over-optimizing into any single metric I share.

This is the trap most operators fall into. Look at my LinkedIn for a perfect example. Simple posts with a single idea engage far more than complex nuanced ones. I always try to post nuanced ideas but its the simple ones that get the bulk of the views.

A sub-KPI as a golden star/silver bullet is so much easier. It’s also a trap.

Goodhart’s Law, translated into ad accounts

Charles Goodhart wrote his paper in 1975 on UK monetary policy and produced the cleanest mental model in operations. When a measure becomes a target, it ceases to be a good measure.

The classic example is the Soviet nail factory. Told to produce more nails, the factory made tiny useless nails. Told to produce more weight of nails, it made oversized useless nails. The measure was fine until it became the goal. Then it collapsed.

Your ad account is full of these.

“Produce more ads.” The target becomes count, not impact. Briefs get padded, variations get shallower, winners get drowned in noise.

“Hit a hook rate benchmark.” You brief creators to optimize the first three seconds for thumbstop. They do. Your CVR drops because you hooked the wrong audience.

“Keep average creative age low.” You start pausing ads that are still profitable because they are old. You sacrifice efficient spend to hit the age number.

“Kill anything below X ROAS at the ad level.” You pause the ads that were enabling scale. The losing ads were part of how the ASC learned. Revenue flattens.

Every one of these is a sub-KPI doing its job as a sensor, then getting promoted to the steering wheel, and quietly destroying the thing it was supposed to build.

Why this hits winning accounts hardest

Plenty of sub-KPI in a top accounts looks “worse” than some theoretical best. That’s the nature of portfolio optimization. You cannot have every component be best in class and also have the whole system be best in class. Real portfolios have weak corners.

This is why Barry’s critique lands well. Ad-level last click CPA in a consolidated ASC setup will look ugly at the ad level for plenty of winners, because the algorithm is optimizing for more than last click (or any single attribution model). Pause them to “fix” ad-level CPA and you risk killing true performance. The headline performance flattens.

Nick’s critique is the same move at a different level. Daily tinkering against sub-KPI drift is how good operators accidentally break good accounts. The goal is to make the right decisions when they are needed, NOT to make decisions as a default.

Both of them are saying the same thing I’m saying here. The sub-KPI is not the lever.

It was never the lever.

The operator rule: sensors vs. steering wheels

My rule of thumb across accounts: sub-KPIs are sensors, not steering wheels.

They tell you what is happening. They are not what you optimize for. The headline metric (usually blended ROAS, efficient new customer acquisition, or contribution margin depending on the business) is what you optimize for.

ROAS is a sensor for Revenue which is a sensor for driving value. You need to know what problem you are solving for in order to understand which one it is.

Another crucial test: if this number got better through gaming, would the headline metric actually improve?

Produce more ads? Usually no. Count can be gamed with low-effort repeats.

Produce more tested angles? Usually yes. Gaming it forces real creative exploration.

Hit a hook rate target? Usually no. Easily gamed with clickbait.

Cut production cost per proven concept? Usually yes. Gaming it forces better briefs upstream.

Be extra careful when using game-able metrics as targets.

The takeaway

A dashboard is only useful to the extent it makes you smarter about the real job.

And the real job hasn’t changed. Make something valuable. Get it to the people who want it and offer it for less than they’ll pay for it. Everything inside Ads Manager is a tool in service of that, not the end goal.

So each time I send a KPI be careful with the nuance. Sub-KPIs sense. You steer.

If this was useful, send it to your team or share on LinkedIn with your favorite graphic and tag me! Best compliment you can give!

Share

If you run paid media for a DTC brand, you’ve had this fight.

Your creative strategist builds something they believe in. They’ve studied the hooks, the angles, the audience. They’re proud of it.

Three days later, your media buyer pauses it. “Didn’t hit CPA.”

Your creative strategist is frustrated. “You didn’t give it enough time.”

Your media buyer is annoyed. “The data was clear.”

Both of them are usually half right.

I’ve managed over $100M in Meta ad spend across DTC brands, and this tension shows up in almost every account I’ve ever touched. It’s not a personality conflict. It’s a structural one. And if you don’t diagnose it correctly as a brand, you’ll either burn through creative talent or waste budget on ads that were never going to work.

Here’s what I see across accounts.

First, if you are readying this thinking “I wish I had enough creative to wonder if I cut too early” then you should really read this… this was brought to you by Billo,

I’ve been using Billo for 5 years across almost every client I manage creative for. One 9-figure brand, Billo’s content is around 50% of total ad spend, besting four other agencies producing creative. We put $3M behind a single Billo creator in 6 months for them (quite profitably). The process is simple: order wide, test a lot, cut the bottom, and scale the winners hard. Then go back to those creators for new angles and concepts. Take those winning ads and test them with new creators. Let the creators speak authentically and test at least 10. If you’re like me, you will find some real gems. If you’re running paid media and not using Billo as your UGC pipeline, you’re overpaying for creative. Check them out here.

I’ve had a dozen companies reach out about sponsoring this newsletter, but I chose Billo as a partner. After 5 years of using their tool, and seeing all the recent improvements (analytics, AI mashes, auto scripting) I feel very comfortable recommending them as a high-value partner that you can test out today.

Part 1: Is Your Media Buyer Actually Cutting Too Fast?

Before you take the creative team’s side, you need to test the claim. “You’re killing our ads” is a feeling. You need data.

There are two things to look at.

1. Are you spending too aggressively in the first 48 hours?

A new ad launches, gets $500 in spend on day one, and the CPA is $140 against a $90 target. It gets paused. The creative team says it never had a chance.

And sometimes they’re right.

If you’re front-loading too much spend before the algorithm has enough signal, you’re making decisions on noise, not data. Meta’s delivery system needs conversion events to optimize. If you’re spending $300-500 before you’ve gotten 5-10 conversions, you’re reading tea leaves.

The fix is simple. Look at your recent launches and answer two questions: How much did we spend before pausing? And how many conversions did we get before making the call?

If the answer is “we spent $200 and got zero conversions so we killed it,” that might be premature. If the answer is “we spent $800 and got 2 conversions at 3x our target CPA,” that’s a different story.

This is our internal tool (demo data of a poorly managed account) where we plot this out. At each spend level the cut threshold gets closer and closer to the actual CPA as you have more data.

Dont let ads get into the red like this:

On the flip side, don’t leave too many in the green (over performing for their spend level). Try to balance it out.

4 scenarios to think through:

2. Are you giving ads enough calendar time to develop?

This is different from spend.

In my experience ads need 3-7 days to find their audience, especially if you’re testing a new angle or a new customer segment. An ad that looks mediocre on day 2 can look great on day 5 once Meta figures out who to show it to.

Pull your last 20-30 ad launches. For each one, chart the CPA trajectory from day 1 through day 7. You’re looking for two patterns:

Pattern A: Ads that started bad and stayed bad. These are clear losers. Cutting them on day 3 was the right call.

Pattern B: Ads that started rough but improved significantly by day 5-7. If you’re seeing a meaningful percentage of these, then give ads more time before you cut them.

You need to know in YOUR account how long ads take to develop but 4 days is a good place to start. Knowing this + your spend thresholds should help you set your testing budgets daily.

3. The fastest diagnostic I know: the low-spend retest

This is the test that settles the argument.

Go pull every ad in your account that spent less than $1,000 but had strong top-of-funnel metrics across two groups. Low CPA, low cost per add-to-cart, low CPC. These are ads that showed early promise but never got enough budget to prove themselves.

Build 3 ad sets, top 8 ads each:

• Group 1: Low CPA ads that didn’t get scaled

• Group 2: Low cost per add-to-cart (excluding Group 1)

• Group 3: Low cost per click (excluding Groups 1 and 2)

Run it for a week.

If you find winners, your testing structure has gaps. You had good ads sitting in the account that never got a real shot. That’s a media buying process problem.

If nothing shows potential, your current testing is working fine and the creative team needs to hear that.

This is the fastest way to turn “you’re killing our ads” from a feeling into a fact. Either the data proves it or it doesn’t.

4. Stagger high-confidence with low-confidence launches

One more testing principle that most brands get wrong.

Let’s say you have a new skincare product and you drop 100 new ads in a week for it. All new products, all new creative. The result: ~10% win rate, and a ton of wasted test spend, and a week of terrible account performance while the algorithm sorts through the wreckage.

The fix is simple. Instead of launching 100 new ads in one shot, alternate between high-confidence and low-confidence batches. Launch an ad set of new product creative, then an ad set of proven winners (your core product, your hero SKU, whatever is already working). New batch, proven batch, new batch.

Why this works: you’re giving the algorithm ads that are likely to convert between the experimental ones. It stabilizes account performance while you test, and it means you’re not stacking 7 consecutive ad sets of unproven creative on top of each other.

Quick rule of thumb: Established products with proven creative angles hit at about a 20% win rate. Net new products with untested concepts hit closer to 10%. (Adjust if you measure hit rate differently) That difference is massive when you are trying to keep the account rolling and manage test spend.

If you’re launching 100 ads of established product variations, you can expect ~20 winners. If you’re launching 100 ads of brand new products, you’re looking at ~10. Plan your test budget and your expectations accordingly.

5. Use organic performance as a pre-filter (when you can)

This won’t work for every brand, but if you have an active organic presence, it’s a cheat code.

If a product or creative concept is performing well on organic social, prioritize it for paid testing first. If you have waitlist data, pre-order data, or any other signal of demand, use that to sequence what you test.

The goal is reducing wasted test spend by front-loading the creative that already has some signal behind it. It doesn’t guarantee a paid winner, but it tilts the odds.

Was this forwarded to you? I break down the systems behind scaling DTC creative and media buying every week.

Subscribe now

Part 2: The Bar Keeps Moving (and That’s Not Anyone’s Fault)

Here’s where I need to level with the creative side.

Even if the data shows your testing process is fair, giving ads enough time, making good decisions... your hit rate is still going to feel terrible. And it’s going to feel worse over time.

Most brands don’t talk about this.

The bar is always going up.

I wrote about this in my post on The Power Law of Ad Creative, but let me put it in the context of how it feels when you’re the one making the ads.

Six months ago, your team made an ad that crushed. $80 CPA, scaled to $50K in spend, everyone was happy. They’re proud of it. They should be.

Today, that same ad concept might not pass your testing threshold.

Not because it got worse. Because everything around it got better.

Your account’s top performers set a new ceiling. The algorithm found more efficient delivery paths. Your ROAS targets went up. Maybe your spend scaled and you need ads that work at higher volumes now.

Your competitors also got better, and now you are competing against bigger players.

This is how power laws work. You’re always raising the floor. An ad that was “great” at $100K/month in total account spend might be “average” at $300K/month. The winners keep winning harder, and every new ad is competing against your best, not your average.

What this means for your creative team: their “hit rate” will naturally decline as your account matures. If they keep improving the hit rate might stay the same.

My solution is to measure “big hits” $25k ads / $100k ads or whatever makes sense for your team.

Anytime your creative team creates an ad that hits $100k study it, learn from it, and celebrate it.

As you scale this keeps you focused on those big wins (which drive the account) and you can still celebrate wins.

Part 3: The Power Law Hit Rate Problem

Studies show that people are happiest when they succeed 80% of the time. That’s the magic point where they feel stretched but accomplished. But in power laws most things fail which stings.

Meta advertising doesn’t follow a normal distribution. It follows a power law.

In a power law, a small number of ads do the vast majority of the work. That normally means: 1-2% of your ads end up doing ~50% of your total spend. The rest are either modest contributors or outright failures (the majority).

That means the realistic “hit rate” for a well-run creative operation is not 80%.

It’s closer to 15%. Maybe 20% on a good month.

At best, you’re looking at 20% successes and 80% failures.

This is not how most professional feedback loops work.

In most jobs, if 80% of your output fails, you get fired. In Meta advertising, if 80% of your ads fail and the other 20% scale efficiently, you’re running an elite operation.

The disconnect between “how professional success normally feels” and “how power law creative production actually works” is the root cause of the tension between creative and media buying inside brands.

Your creative team feels like they’re failing because their mental model says 80% should work. The math says the opposite. And nobody noticed because both sides are using different scorecards.

The bias problem

There’s one more layer here that’s uncomfortable to talk about.

Every creative team loves their own work. That’s not a flaw. That’s what makes them good. You want people who believe in what they’re making.

But it creates a blind spot. I was talking to a team spending $1m+/mo recently and they felt confident there were more winners in there than they were finding. They thought they were too good for 15% hit rates.

And they are good. Their overall ROAS is well above average. Their content quality is elite. But even elite creative teams can’t beat power law math. Having great creative means your winners are bigger winners, and that the bar is now increased. You spend more and your hit rate stays the same. The difference between a great creative team and an average one isn’t 40% hit rate vs. 20%. It’s that the 20% that hit, hit harder and spend overall goes up.

When your team says “we should retest the losers because our content is too good to fail,” the honest answer is usually: it’s not worth the test spend. If an ad was going to be a big winner, it would have shown at least some signal in the first pass. Retesting to find a few more mediocre performers in the middle doesn’t change the math.

Part 4: The Similarity Tax

One more thing that kills creative performance and gets misattributed.

An ad that’s too similar to what’s already in the account won’t find its own audience.

Meta’s system is trying to find unique audiences for each ad. If two ads are nearly identical in concept, format, hook, and visual style, they’re competing for the same eyeballs. The system will pick the one with better historical data (your existing winner) and suppress the new one.

So your creative team makes a variation of something that worked. It launches. It dies. They blame the testing process.

But the real problem is the ad wasn’t different enough to find its own audience.

This is where it gets tricky. The best creative operations push hard on net new concepts, not just variations of winners. Variations have their place (they replace concepts that are fatiguing and help you improve), but the concepts themselves need to be different.

The practical rule: if you can describe two ads with the same sentence, they’re probably too similar.

Don’t take this too far though.

1. Take a concept and apply it to another product

2. Take an ad and apply to another format

3. Take a USP and speak to it in a new way

Just dont take the same static, same headline, and lightly tweak the colors or sub copy and expect it to win again.

Putting It All Together

Here’s what to do next time your creative team says, “You’re cutting our best ads too soon.”

Step 1: Check the data. Are you spending too aggressively before the algorithm has signal? Are you giving ads at least 5-7 days before calling it? If not, fix the testing process first.

Step 2: Set realistic expectations. A 15-20% hit rate is excellent in a power law system. Frame it that way for your team. Celebrate the winners loudly instead of mourning the losses.

Step 3: Variation vs. concept. Variations of existing winners will underperform because of audience overlap. Push for net new concepts and accept that most of them will fail. That’s the game.

Step 4: Acknowledge the moving bar. Show your creative team how the account’s performance ceiling has risen over time. Their work from 6 months ago built that ceiling. The fact that it’s harder now is evidence of success, not failure.

The best brands I work with share one trait: the people making the ads and the people buying the media have agreed on what “winning” looks like in a power law system. They stopped keeping score the wrong way.

If this changed how you think about creative performance, send it to someone on your team who needs to see it.

Subscribe now

The #1 response I got was some version of: “Great framework. But how do I actually set this up?”

Fair. That post gave you the what and the why. This one gives you the how.

If you haven’t read the original, start there. It explains why the hierarchy matters. This post assumes you get the concept and you’re ready to build it.

Brought to you by Billo

I’ve been using Billo for 5 years across almost every client I manage creative for. One 9-figure brand, Billo's content is around 50% of total ad spend, besting four other agencies producing creative. We put $3M behind a single Billo creator in 6 months for them (quite profitably). The process is simple: order wide, test a lot, cut the bottom, and scale the winners hard. Then go back to those creators for new angles and concepts. Take those winning ads and test them with new creators. Let the creators speak authentically and test at least 10. If you’re like me, you will find some real gems. If you’re running paid media and not using Billo as your UGC pipeline, you’re overpaying for creative. Check them out here.

I’ve had a dozen companies reach out about sponsoring this newsletter, but I chose Billo as an exclusive partner. After 5 years of using their tool, and seeing all the recent improvements (analytics, AI mashes, auto scripting) I feel very comfortable recommending them as a high-value partner that you can test out today.

Back to attribution, here’s the quick version: think of attribution like a Christmas tree. The star at the top is your blended MER/nCAC. It tells you if the business is working. The upper branches are your channel allocation tools (post-purchase surveys, MMM). They tell you which channels deserve more budget. The lower branches are your ad-level tools (MTA, platform data). They tell you which ads to scale or cut. And the trunk (server-side tracking) holds all of it up.

Most brands try to build this from the bottom up. They install an MTA tool and try to track every click before they even know their blended numbers. That’s backwards. You build top-down.

Its also important to note that we are aiming for accuracy AND precision, in order to do so you need to get the steps down in the right order. Skipping to MTA gives your precision (tempting) but not accuracy (dangerous)

Here’s how. Step by step.

Step 1: Build Your MER/nCAC Tracker (15 Minutes in Google Sheets)

This is the star on the tree. It takes 15 minutes to build and it’s the most important thing in your entire attribution stack.

Open a new Google Sheet. Here are your columns:

Column A: Date (Daily or weekly rows)

Column B: Total Revenue

Column C: New Customer Revenue

Column D: Total Purchases

Column E: New Customer Purchases

Column F: Total Marketing Spend

Column G: Total Acquisition Spend (paid media only)

Then your calculated columns:

Column H: MER (Marketing Efficiency Ratio)

=B2/F2

Total Revenue divided by Total Marketing Spend. This is your top-line efficiency number. Channel-agnostic. If MER is healthy, the business is profitable (or burning appropriately). If it’s not, nothing else matters.

Column I: Total CPO (Cost Per Order)

=F2/D2

Total Marketing Spend divided by Total Orders. This is your cost per order across all customers, new and returning. Because repeat buyers are in the denominator, this number should always be lower than your nCPA. Returning customers should cost you (almost) nothing to re-acquire, so they bring the average down.

This is useful as a broad health check, but it can hide problems. If your Total CPA looks great but your nCPA is ugly, your repeat customers are masking the fact that new customer acquisition is too expensive. You’re riding your existing base, not growing it.

Column J: AMER (Acquisition MER)

=B2/G2

Total Revenue divided by Acquisition Spend only. This isolates your paid media efficiency from retention spend.

Column K: nCPA (New Customer CPA)

=G2/E2

Acquisition Spend divided by New Customer Purchases. This is the number I watch most closely. (nROAS if AOV is changing a lot) It tells you what you’re actually paying per new customer on your paid channels, without retention costs or repeat buyers muddying the picture.

The gap between Total CPA and nCPA is one of the most revealing numbers in DTC. A small gap means most of your orders are new customers (either early-stage brand or your retention is weak). A large gap means repeat buyers are carrying the business, which is great for margins but means your nCPA target needs to be realistic about what acquisition actually costs.

Where the numbers come from:

Total Revenue, New Customer Revenue, Total Orders, and New Customer Purchases all come from Shopify (or whatever your ecommerce platform is). Filter by first-time vs. returning to split orders and purchases. Marketing Spend is your total across all channels, including email/SMS tools, influencer payments, everything. Acquisition Spend is just your paid media aimed at new customers (Meta, Google, TikTok, potentially influencers, etc.). It depends company to company but if it heavily impacts new customer growth and is not employee cost, include it.

How often to update:

Weekly minimum. Daily if you’re actively scaling or testing new channels. We track daily and occasionally intra-day pacing on large spends even at these high level numbers.

The two numbers that set your guardrails:

Your maximum marketing spend = Gross Margin % minus OpEx %. Cross this line and you’re losing money at the company level. This is the ceiling.

Your target CPA = projected 3-month customer profitability. This is your efficient growth zone. It’s the number where you’re acquiring customers who will be profitable within a reasonable timeframe.

Everything else in this post is about figuring out how to spend between those two numbers as effectively as possible.

Quick example of why this matters first: A client comes to me with a “4.2x ROAS” on Meta. Looks great. But when I pull their MER tracker, their nCPA is $120 on a product with $85 in 3-month projected profit. They’re underwater on every new customer.

The Meta dashboard was telling a story that the business math didn’t support. Once we identified it the solution was clear, fix the broken exclusions, change the creative strategy, and install a system to optimize for new customers on their higher AOV product.

That’s why you start here. Every time.

Step 2: Set Up Your Post-Purchase Survey (30 Minutes with Fairing)

This is the most underrated tool in DTC attribution. (Yes a majority use it, still underrated) It lives on the upper branches of the tree. It tells you where to allocate budget at the channel level.

The tool: I use Fairing. It exports to Google Sheets automatically with their built in tool, the setup is simple, and the data is reliable. KnoCommerce also works well. I’ve seen people custom build them too. I’m sure there are other good ones but I always recommend Fairing to start out since it’s never failed us and is inexpensive. Really though, any tool that lets you ask a post-purchase question and export the data works.

The exact question:

“Where did you FIRST hear about us?”

FIRST in all caps. This matters. You’re not asking where they last clicked. You’re asking what created the awareness. That distinction is the whole point.

The answer options (randomize the order every time):

• Facebook

• Instagram

• TikTok

• Google Search

• YouTube

• Podcast

• Friend / Referral

• TV / Streaming

• Other (with text field)

A few things to note here. Split Facebook and Instagram into separate options. Yes, they’re both Meta. You’ll combine them later in your analysis. But splitting them in the survey catches signal you’d miss. A lot of people know the difference between where they saw your ad, and that data is useful for creative strategy even if you combine it for budget decisions.

Keep total options under 10. Randomize the display order so you don’t bias toward whatever’s listed first. And always include “Other” with a text field. You’ll find stuff in there you didn’t expect. If you have any primary channels not listed here, swap them in.

Target response rate: 35%+. If you’re below that, make the survey more prominent in your checkout flow or simplify the question. Some brands get 50%+ with good placement.

Note, there are lots of good secondary questions you can ask. Follow ups to the first (more detail or specificity). Timing (how long since you first heard about us). Ideation (What is your favorite selling point). Etc. Normally about 50% of people answer each successive question (40%, 20%, 10%, 5%) so at scale you still get a lot of data 4 or 5 questions deep.

The output that actually drives decisions:

Export your survey data daily/weekly. The math here is simple but the extrapolation is what makes it powerful.

First, calculate your response rate. Take total new customer survey responses and divide by total new customers for the period.

=Total New Customer Responses / Total New Customers

If you got 133 survey responses out of 380 new customers, your response rate is 35%. That’s your multiplier for everything below.

Second, sum responses by channel. In your export, count how many new customers selected each channel (Facebook + Instagram, TikTok, Google, etc.) for the period.

Third, extrapolate to all new customers. This is the key step most people skip. Not everyone answers the survey, but the ones who do are a representative sample at 35%. Divide each channel’s response count by your response rate to get the implied total new customers from that channel.

=Channel Responses / Response Rate

If 47 people said “Facebook” and your response rate is 35%, that implies ~134 total new customers actually came from Facebook that week. Not 47. The survey only captures a slice, and you need to account for the full picture.

Fourth, calculate cost per implied new customer by channel.

=Channel Spend / Implied New Customers

Now you have an estimated true CPA by channel based on where customers say they actually discovered you, extrapolated to your full customer base. That’s a fundamentally different (and often more useful) number than what the ad platforms report.

If Meta is showing you a $40 CPA but your survey-implied CPA for Facebook + Instagram combined is $90, there’s a gap. That gap is usually view-through attribution inflation. The survey is catching the signal that click-based tools miss.

The key here is that you are evaluating based on impact (what people remember) and first impact (where they first heard about you) on an attribution model that is MECE (mutually exclusive and completely exhaustive) instead of potentially overlapping and overcounting or undercounting customers (which then means you can trust it for financial analysis and target setting.

Use this number to set your channel level budgets, how much you spend on Meta vs Google vs Tiktok.

ONLY then move onto allocating across campaigns/ads and using an MTA for intra channel analysis.

Limitation to remember: People don’t always remember correctly. Long purchase cycles make this harder. That’s fine. You’re using this for directional channel allocation, not surgical precision. The directional signal is extremely reliable still.

Step 3: Configure Your MTA Tool (Triple Whale)

Now we’re on the lower branches. This is where you make ad-level decisions: which ads to scale, which to cut, which to retest.

The tool: I use Triple Whale as my primary MTA. Northbeam is also one we use internally. The main reason I prefer TW: it gives live data that updates regularly and you don’t have to pay extra for the refresh rate. Paying extra for up to data data makes me upset but otherwise North beam is great too.

The setup that matters:

Start with Total Attribution as your default view (non MECE but we already solved for that in the first step). This gives you the full picture across all touchpoints. Then toggle between attribution models (first-click, last-click, linear) to pressure-test what you’re seeing.

Why don’t we just use an MTA for channel level allocation and skip the post purchase survey?

Facebook is much better at pushing people to click the link than TikTok, Google grabs a ton of last clicks. TV? No clicks at all! (Google will grab them though I promise).

A 3x Google ROAS =/= a 3x TikTok ROAS in an MTA.

But once we are within a channel it gets a bit cleaner (still exceptions though).

When you see a new ad performing well, don’t just look at the ROAS number. Click into the actual conversions. Look at the customer paths.

If you had 5 customers who all clicked the ad directly as their first interaction, that’s a strong signal. The ad is doing real work finding new people.

If you had 5 customers who all saw 3-4 other ads before eventually clicking this one, that’s a different story. The ad might be getting credit for conversions that were already in motion. It’s a closer, not a prospector. Still valuable but know the difference.

That distinction changes your scaling decision entirely. A prospecting ad that’s working deserves more budget. A closing ad that’s riding other ads’ coattails might require warm audiences to scale which means you need to be a bit slower in the scaling.

The Account Control Chart:

This is the visual I use to manage every ad account. Pull your MTA data and plot CPA (y-axis) vs. Total Spend (x-axis) for every active ad on a scatter plot.

You get four quadrants:

• Low CPA, High Spend (Bottom right): Your winners. These are doing exactly what you want. Protect them.

• Low CPA, Low Spend (bottom left): Efficient but under-scaled. Push more budget here. This is where the opportunity lives.

• High CPA, High Spend (top right): Burning cash. Cut or restructure immediately.

• High CPA, Low Spend (top left): Not working but also not hurting you much. Kill them and reallocate.

If your best ads aren’t in the low CPA / high spend zone, you have a media buying problem that no amount of new creative will fix.

Step 4: Wire It Together

You’ve got your three tools built. Here’s how they work together in practice.

Every day, in this order:

First: Open the MER tracker. Is the business healthy? Are we between our max ceiling and our target CPA? If nCPA is trending up, I need to understand why before I do anything else. If it’s stable or improving, I move on.

Second: Pull the survey data. Any shifts in where new customers say they’re discovering us? If Instagram’s share of responses is climbing while TikTok’s is dropping, that’s a signal about where demand is actually being created, regardless of what the ad platforms claim.

Third: Open Triple Whale. Which ads are winning? Do the winners align with what the surveys are telling me about channels? Scale what’s working. Cut what’s not.

When the tools disagree (this is where it gets interesting):

Real example, anonymized. A client had an ad on Meta showing a 5x ROAS in Facebook’s dashboard. Looked like a home run. But when we checked Triple Whale, the same ad was showing a 1.5x.

We dug in. The ad was promoting a specific product that simply didn’t have enough sales volume to justify what Facebook was reporting. When we looked at the attribution breakdown, the vast majority of Facebook’s claimed conversions were view-through credit. Meaning someone was “served” the ad (possibly for one second while scrolling), didn’t click, and then bought something later. Facebook took credit.

This is something Meta’s system does constantly. It’s not a bug, it’s how their attribution works. But it means their dashboard will consistently overstate performance on certain ads, especially broad-targeted ones with high impression volume.

We trusted Triple Whale, pulled budget from that ad, and reallocated to ads that were showing strong performance in both systems. Overall account efficiency improved.

The principle: You’re not looking for three tools to agree perfectly. They won’t. You’re looking for the disagreements, because that’s where the insight lives. Thats where you get to learn interesting things about your customers.

Step 5: The Upgrade Path (Build for Your Spend Level)

Not every brand needs every tool on day one. Here’s what to build first based on where you are.

$10K-$50K/month:

Build the MER tracker and set up Fairing and use interface data for optimization. That’s it. These two tools will give you 80% of the insight you need at this spend level. Skip MTA for now, the cost isn’t justified and your volume might not be high enough for the data to be meaningful.

Setup time: 1 hour.

$50K-$250K/month:

Add Triple Whale or Northbeam. Make sure server-side tracking (CAPI or Elevar) is set up and firing correctly on Shopify. Without server-side tracking, every other tool in your stack is working with incomplete data.

Setup time: About 1 week to get clean data flowing.

$250K+/month:

Layer in cohort LTV by acquisition channel. Start adjusting your CPA targets by channel based on customer quality, not just acquisition cost. Consider MMM (Meta’s Robyn if you have data science resources, or a SaaS platform like Measured or Sellforte if you don’t). Run your first geo-lift test (Haus makes this accessible, northbeam has this now too) on your biggest spend channel.

Setup time: 1-2 months for meaningful data.

The point is: start at the top of the tree. Get your blended numbers right. Layer in survey data. Then add precision tools as your spend justifies them. Most brands try to build this in reverse and end up with precise data pointing in the wrong direction.

The Takeaway

Attribution isn’t a dashboard you install. It’s a system you build, one layer at a time, starting from the top.

Your MER tracker takes 15 minutes and it’s the most important thing in the stack. Your post-purchase survey takes 30 minutes and it’ll change how you think about channel allocation. Your MTA tool takes a week to configure and it’ll change how you manage ads.

Wire them together. Check them weekly. Dig into the disagreements more than the agreements.

And never let a platform grade its own homework.

If you want the full framework behind all of this (the 8 models, how they fit together, and why the hierarchy matters), read the original post here.

If this was useful, send it to your team!

Share

Everyone is doing different things.

Everyone’s success looks very different.

Across millions in spend and billions in revenue here is what im seeing working as I talk to different brands.

What’s Working Right Now

Problem/solution ads as a full-funnel play.

The brands scaling hardest on Meta right now are not running promo-first creative.

They’re running problem/solution video ads. A real pain point. A credible voice (founder, doctor, real customer). A complete story.

These ads pull new people into the funnel AND drive direct conversions. One brand I work with saw their Black Friday performance jump 100% year over year, not because of better promos. Because we spent Q1 through Q3 building the top of funnel with problem-first creative. By November, the audience was pre-educated. The promos just had to close. Better performance through the whole year but very clear when BF came around.

Here’s what I see across accounts: When you scale ads to high budgets at strong performance AND those ads speak to a problem, the long term performance and sustainability of that growth is better.

When we come into an account and it’s heavy on promo language (percent off, free gift, limited time), the first move is almost always pushing toward problem/solution, video format, high-density information ads. That’s what expands the audience. That’s what builds long-term scale.

Offers bundles and promos have their place, but their place isn’t everywhere.

Long-form ads are having a moment.

This might be the most counterintuitive thing we’re seeing right now.

We’re testing 2-minute, 3-minute, 5-minute, and even 14-minute ads across multiple accounts. And they’re working.

One account has a 14-minute ad that’s one of their top spenders at strong efficiency. It’s basically a mashup. One strong 90-second ad stitched together with several other creator clips. All over the place from a production standpoint. Crushing it from a performance standpoint.

A separate brand has a 4-minute video walking through the full story and usage of the product. No script. It does have a fun eye catching before/after hook (thats still important). But they are authentically walking through their story. Average watch time was nearly 40 seconds. The comment section was people sharing their own story and commenting back about it.

A few friends have mentioned the same thing in their ad accounts. Long form ads hitting well (all kinds of formats)

It makes sense from one level: if someone watches 2+ minutes of your content, Meta has an incredibly strong signal about who that person is and what they want. You’re pre-qualifying buyers before they ever hit your landing page.

The completion rate doesn’t need to be high. Even 1-2% watching to the end at scale means you’re building a deep pool of highly educated prospects.

The 30% hook rate threshold still applies. You need qualified attention at the top. But once you have it, keeping people engaged for minutes (not seconds) is a massive signal advantage.

Whitelisted pages are still outperforming.

For brands with AOVs above $80-100, running ads from whitelisted pages instead of your brand page is one of the biggest performance levers I’m seeing right now.

The simplest version: find a credible expert (doctor, practitioner, creator with real domain authority) and run your ads from their page as a partnership ad.

The credibility transfer is real. People trust a doctor talking about a health product more than a brand page saying the same thing. And Meta’s algorithm picks up on it too. The engagement signals are stronger because the content feels more native coming from a person than from a brand.

If you’re in health, wellness, beauty, or any category where trust matters, whitelisting should be a core part of your creative mix. Not a nice-to-have. A core part.

The $3K/creative production benchmark.

Across our accounts, the math keeps coming back to one number: you need roughly 1 new ad for every $3,000 in monthly Meta spend.

At $100K/month, that’s ~33 new ads. At $300K, it’s ~100.

This accounts for creative fatigue (most accounts churn 25-35% of their spend monthly), hit rates (10-15% of ads become real winners), and the need to keep testing.

But here’s the thing most brands don’t talk about.

The volume trap is real.

I had three separate conversations this month with brands producing 100+ ads per month who couldn’t scale.

The problem wasn’t volume. The problem was that all 100 ads looked the same.

Different copy. Different creators. Different hooks. But the same core angle, the same format, the same visual feel. Meta is reaching the same pocket of audience over and over because the creative isn’t actually diverse.

One founder put it perfectly: “Even when the messaging is entirely different, when you look at the ad account, it all looks exactly the same.”

The fix is not more ads. It’s more different ads. Map your creative on a grid (selling point x format x creator type) and look for the empty squares. That’s where your next winner is hiding, not in another variation of your current best angle.

I’ve seen a single ad spend $800K+ at strong efficiency. It was unique and new. That one ad is worth more than 200 variations of the same concept.

What We’re Testing

Novel format “big swing” creative.

We’re allocating roughly 10% of creative production to “big swings.” Engaging, native-feeling formats that still incorporate real selling points.

Speed challenges. Street interviews. Game-show style comparisons. Science experiments. Founder content.

One brand did a game show style version of their product, made it into a challenge to apply the skin care product quickly. Casual phone shoot. Became one of their best-performing concepts. The format was engaging enough to hook, but the USP (ease of installation) was baked into the concept.

Other big swings that have crushed it: a founder talking to himself in two chairs about a product problem. A song about a product’s core benefit. A skincare vs child race filmed on a phone.

All engaging. All selling.

The hit rate is lower than workhorse creative. Some of them flop HARD. But the ceiling is much higher. When one hits, it can change the trajectory of an entire account.

The important thing: don’t go high production. The best big swings are phone-shot. Authentic. People associate polished video with ads and scroll past. High production works in specific contexts (podcast interviews, product demos in a warehouse, events where cameras make sense). Everywhere else, phone wins.

AI animated statics.

This one is early but worth watching.

We’re taking top-performing static images and running them through AI video tools (Veo, Gemini) to animate them. The AI adds subtle movement, background elements, and atmosphere.

For some accounts, this has become a significant chunk of account spend in just three months. Every week we launch new ones by animating the best performers.

Download the static, convert it, send to the AI tool with specific direction prompts, output a video. Not 100% perfect, and you need to filter out a lot but you get some really interesting content.

Twitter is full of the AI-generated “clay” style ads and seeing them scale to $50K/day in spend on launch. This space is moving fast.

Subscription-only landing pages.

Most brands default to showing both one-time and subscription options. If your subscription take rate is already above 50%, consider removing the one-time option entirely.

The math changes too. Your LTV goes up dramatically when everyone is on subscription. That means your allowable CAC goes up. That means you can spend more on Meta. That means you scale faster.

It’s not right for every brand. But if you’re already converting more than half your customers to subscription, the one-time option is probably just adding friction.

Retail support via Meta (three approaches).

For brands with retail distribution, we use three distinct strategies:

1. Conversion overflow. Keep everything conversion-optimized. When a top-of-funnel ad performs well, retail sees a pickup naturally. No special targeting needed.

2. Banner hijacking. Take your top DR ads and add “Now at [Retailer]” banners. Still conversion-optimized. But millions of impressions build retail awareness for free on top of ads already working.

3. Geo-targeted reach. Impression campaigns targeted to high-income households around specific retail locations. Best for major launches.

Most brands should start with #1. Layer in #2 when you have a clear retail story. Save #3 for big launch moments.

Top Tests You Should Be Running

1. Long-form video (2-5 minutes). Take your best short ad concept and expand it. Don’t pad. Add depth: more proof points, more story, more education. If you’re feeling bold, go longer. The 12-minute ceiling is real.

2. AI animated statics. Take your top 5 performing static images. Run them through Veo or Gemini. Launch them as video ads. Total time investment: an hour. Potential upside: a whole new format that scales.

3. Static ads with non-promo headlines. If your top statics are discount-driven, test the same visual with a selling-point headline instead. If it performs at parity, it’s better for the business because it doesn’t train the algorithm for deal-seekers.

4. Creative matrix gap analysis. Map your last 90 days on a grid: selling points on one axis, formats on the other. Find the empty squares. Fill one gap per week. This is how you escape the volume trap.

5. Post-purchase survey budget reallocation. Compare implied CAC by channel (spend / survey-attributed customers). If Meta’s implied CAC is 2-4x lower than Google non-brand, shift 20% of Google non-brand to Meta and watch overall CAC for 30 days.

6. One whitelisted expert ad. Find one credible voice (doctor, specialist, creator with domain authority) and run their content from their page. Even one strong whitelisted ad can shift your entire account’s efficiency.

7. One big swing. Block 2 hours this week for one creative concept that’s genuinely different from anything in your account. A challenge, an experiment, a format you’ve never tried. The worst case is you learn something. The best case is a million-dollar ad.

If this was useful, send it to your team or share on LinkedIn with your favorite graphic and tag me! Best compliment you can give!

Share

Most brands have 60-70% of the basics covered. This list is to fill in the gaps.

So I built this. One checklist. Everything an ecom brand needs to have in place before trying to scale, organized by category.

This isn’t a deep dive on any single topic. Just a place to start.

1. Unit Economics

If you don’t know your numbers at the individual customer level, nothing else on this list matters. You can’t scale what you can’t measure.

☐ You have a 1st Customer P&L. Not blended. Just first-time orders. Revenue minus COGS, shipping, payment processing, and acquisition cost. This is the number that tells you if growth is actually profitable.

(Read this substack to grab a template)

☐ You know your nCAC ceiling. The maximum you can pay to acquire a new customer and still hit your contribution margin target. If you don’t have this number, your media buyer is flying blind.

☐ You track LTV by acquisition channel. Meta customers behave differently than Google customers. TikTok Shop customers behave differently than both. If you’re using blended LTV, you’re probably overpaying on at least one channel.

☐ You know your contribution margin by customer cohort. Not just overall. By month of acquisition, by channel, by offer. The brands that scale fastest can tell you exactly which cohorts are profitable and which aren’t.

☐ You can forecast revenue from ad spend with 80%+ accuracy. If you can’t, you don’t have enough data or your attribution is broken. Either way, fix it before you scale.

☐ You know your payback period. How many days until a new customer becomes profitable after acquisition cost. If it’s 90+ days, you need to plan cash flow around it, not ignore it.

2. Creative Production

Creative is the single biggest variable in paid media performance. Make it a system that focuses on quality & quantity at the right levels.

☐ You’re launching ~1 new concept per $10k in monthly ad spend. At $500k/mo, that’s roughly 50 new concepts. (3 ads per concept, not too similar!) Scaling hard? Launch pre-emtively (project budgets and product for that). Ads fatigue quickly? Produce even more. (Don’t go above 1 ad per $1k (1 concept per $3k) in spend, though, unless you have a tiny AOV like Temu).

☐ You track creative hit rate. What percentage of new ads meet your CPA or ROAS target? Aim for 10-15%. Below 5% means your concepts are off. Above 25% means you’re probably not testing enough variety or scaling the right ones.

☐ You have a scatter plot of every ad. CPA on Y axis, spend on X axis (log scale). Look for a pattern of ads being scaled within confidence levels.

☐ You know your creative decays. How much does your ad account decay every month if you don’t add new ads? This tells you how fast you need to produce beyond general benchmarks.

☐ You track spend-weighted creative age. Weight each ad’s age by its share of total spend. If that number is climbing, your portfolio is aging and performance will follow.

☐ You’re not trying to over learn from losing ads. I’ve launched 20,000+ ads. Analyzing losers to learn what not to do can lead you in the wrong direction. If you want to learn from ads, focus on huge hits and aggregate learnings (you can aggregate losses to get statistical significance). If you want to improve ads, then take a winner from 2x ROAS to 3x instead of trying to save a 0.5x.

☐ You have a creative brief process. Not a Slack message that says “we need more ads.” A documented brief with the concept, angle, hook, target persona, and reference examples.

3. Media Buying

Good media buying is invisible. Bad media buying looks like a creative problem. Here’s how to tell the difference.

☐ Your top 1-2% of ads take ~40-60% of total spend. This is what a healthy power law looks like. If spend is spread evenly across ads, the algorithm isn’t finding winners (or your buyer isn’t letting it). Please note, the top creatives should not look identical and should not be promotions. Diverse problem aware ads at the top please!

☐ You have a kill rule. Example: at $250 spend, cut if CPA is 2.4x target. At $1,000, cut at 1.4x. At $5,000, cut at 1.15x. Use confidence intervals, not gut feel.

☐ You’re not testing faster than Meta can learn. Count new ads launched in the last 30 days. Check spend per ad. If you’re spreading $500k across 1000 new ads, none of them have enough data to read. Cut so many variations, focus on quality, and balance testing and scaling.

☐ You use both cost cap and highest volume. Cost cap for scaling volatile ads that have winning potential. Highest volume for testing new concepts that need room to find their audience.

Some accounts do best with all cost cap, some with all ABO. Most can benefit from both. Test everything Meta has for you and evaluate.

☐ Your account structure matches your spend level. Under $100k/mo, keep it simple (1-2 campaigns). Over $500k/mo, you probably need segmentation by LTV / Product / Margin etc. Whatever Meta can’t differentiate well or the business needs to dictate more than absolute performance.

☐ You’re day-trading budgets on winners. Green day (CPA in target)? Increase spend. Red day? Pull back. Your best ads should be getting more gas every day they perform, not waiting for a weekly review. Don’t make the mistake of the occasional huge swing. Small gentle nudges gives the algo good feedback.

Bonus: Use intra-day pacing, not just yesterdays data.

☐ You check the change log. Pull the last 90 days of changes from Ads Manager. You should know who is making changes in your account, is it 1 person or 10? Do you know who they are? Is it all automated? Common questions you can answer quickly by checking.

4. Attribution

If your attribution is wrong, every decision downstream is wrong. This is the part most brands skip because it feels complicated.

Channel level allocation is so much more important than campaign allocation.

You might be optimizing $’s in the completely wrong ecosystem.

☐ You use multi-touch attribution (MTA). Platform-reported numbers are biased. An MTA tool (Triple Whale, Northbeam, Rockerbox, etc.) gives you a second opinion on which ads actually drove conversions.

☐ You run incrementality tests. Holdout tests, geo-lifts, or conversion lift studies. At least quarterly. This is the only way to know if your spend is actually causing revenue or just taking credit for it.

☐ You have a post-purchase survey. “How did you hear about us?” is the simplest attribution model and it catches things pixel-based models miss entirely (podcasts, word of mouth, organic social).

☐ Your three models roughly agree. MTA, incrementality, and survey. If all three say Meta is working, it’s working. If they disagree, dig in before you scale. Trust non click based attribution first. Don’t over optimize for last click.

☐ You’re not relying on last-click for Meta. Last-click undervalues Meta by 30-50% in most accounts. If you’re optimizing to last-click, you’re cutting winners.

☐ You track view-through conversions separately. Platforms love to claim view-through credit. Break it out and compare to click-through. If 50%+ of your attributed conversions are view-through, be skeptical.

Go deeper into this topic here:

5. Landing Pages / CRO

You can have the best ads in the world. If your landing page doesn’t convert, you’re just paying to send people to a bad experience.

☐ You test dedicated landing pages for top ads. Not just your homepage. Your best-performing ad concepts should test pages that continue the same story, angle, and offer.

☐ Your page loads in under 3 seconds on mobile. Check it. Most brands think their site is fast. It’s not. Slow pages kill conversion rates and inflate CPAs.

Stop, test your top pages:

https://pagespeed.web.dev/

☐ You test landing pages, not just ads. A 20% lift in landing page conversion rate is worth the same as a 20% drop in CPA. Stacking 30 AB tests that improve CVR 1% means 35% improvement in ROAS (which could mean 2x the spend at your target ROAS).

☐ Your above-the-fold matches your ad. If the ad promises “50% off your first order” and the landing page headline says “Premium skincare for the modern woman,” you have a problem.

☐ You have social proof visible without scrolling. Reviews, star ratings, press logos, customer count. Something that says “other people already bought this and liked it.” Take the risk away for customers.

☐ You test your offer on the landing page, not just in ads. The offer is usually the highest-leverage variable. Free shipping vs. discount vs. bundle vs. gift with purchase. Test the page, not just the ad copy.

6. Channel Diversification

Meta is probably your primary channel. But try not to make it your only channel.

☐ You have a plan for at least one channel beyond Meta. Google, TikTok, AppLovin, YouTube, Pinterest. You don’t need to be on all of them. But you need at least one backup generating revenue is worth a bit of dilution. Don’t go too far and test all of them at once though. Get good at each channel.

☐ You understand the platform differences before copying ads over. Meta ads interrupt the scroll (3s hook is everything). AppLovin interstitial ads can’t be skipped for 15-30 seconds (tell a story instead). YouTube needs $2,500-5,000 for a reliable signal vs. $500 on Meta.

☐ You’re spending enough on each channel to learn. YouTube needs $15-25k/mo minimum to test properly. TikTok Shop needs volume. All channels need dedication. Don’t spread $20k and minimal focus across 4 channels and conclude “none of them work.”

☐ You’ve evaluated TikTok Shop as a creative subsidy. Even if it’s not profitable directly, high affiliate commissions can generate thousands of creator videos you can repurpose across Meta and YouTube.

☐ You’re not just reposting Meta ads on other platforms. Learn from all channels and test across all channels but the winners may not be the same.

☐ You have a Google brand search strategy. If you’re spending on Meta, you’re generating search demand. Make sure you’re capturing it, not losing it to competitors bidding on your brand name. But also PLEASE don’t just accept the CPC’s Google requests from you. Those customers are looking for you and Google knows its best to send them to you, bid less and they will accept it.

7. Offers & Pricing

Your offer is usually the single highest-leverage variable in your funnel. A mediocre ad with a great offer beats a great ad with no offer almost every time.

☐ You’ve tested at least 3 different offer structures. Percentage off, dollar off, free gift, bundle, free shipping, subscribe-and-save discount. Find the one that makes sense for you and ideally tie to a subscription.

☐ You know which offer converts best for new customers vs. returning. The offer that gets a first purchase is maybe not the same one that drives repeat.

☐ Your offer math works at your nCAC ceiling. A 40% discount that drives a ton of conversions but puts you underwater on first purchase is only viable if your LTV math supports it.

☐ You rotate offers seasonally. The same “20% off” gets stale. Most brands need rotation.

☐ You’ve tested a strong offer against no offer. Some brands (especially premium) convert better with no discount and strong value messaging. You won’t know until you test.

8. Creator & UGC Pipeline

Creator content is the fuel for paid media. But most brands treat it as a one-off transaction instead of a system.

☐ You have a repeatable creator sourcing process. Not “we post on a UGC platform when we need ads.” A system that consistently brings in new creators monthly.

☐ You track creator performance at the individual level. Some creators produce 3x ROAS content consistently. Others produce duds. If you’re not tracking this, you’re treating a 10x creator the same as a 0.5x creator.

Pull the best ones into a group and treat them like pseudo-employees.

☐ You have a mix of UGC styles. Talking head, unboxing, tutorial, lifestyle, street interview, testimonial. Different formats resonate with different audiences and fatigue at different rates. Diversity reduces fatigue across the whole account.

☐ You brief creators with winning angles, not scripts. Give them the hook, the angle, and the key proof points. Let them deliver it naturally. Over-scripted UGC performs like a bad infomercial.

☐ You’re repurposing top UGC across channels. Your best Meta UGC should be tested on TikTok, YouTube Shorts, and Pinterest. Most brands leave performance on the table by siloing content to one platform.

☐ You’ve considered a creator community model. Brands like Comfrt scaled to $1b with 1,000,000 TikTok affiliates. Even at a smaller scale, an affiliate/community model compounds in a way that one-off UGC doesn’t.

9. Retention / Email / SMS

Acquisition gets all the attention. Retention is where the margin lives.

☐ You have a post-purchase email flow. Not just a confirmation email. A sequence that educates, builds loyalty, and drives the second purchase.

☐ You know your repeat purchase rate by cohort. What percentage of January customers buy again within 60 days? 90 days? If you don’t know this by cohort, you don’t really know your LTV.

☐ You have a win-back flow for lapsed customers. Customers who bought 90-180 days ago and haven’t returned. A targeted offer here is almost always cheaper than acquiring a new customer.

☐ Your email/SMS list is segmented beyond “everyone.” At minimum: new subscribers, first-time buyers, repeat buyers, VIPs, lapsed. Each segment should get different messaging.

☐ You’re not over-discounting to your existing list. If every email is a sale, you’re training customers to wait for discounts. Mix value content, new arrivals, and social proof into the cadence. Don’t Bed Bath and send your company to the Beyond. (Sorry I can’t help myself)

☐ You track revenue from email/SMS as a percentage of total. Healthy benchmark is 25-35% of total revenue from owned channels. Below 15% means your retention engine needs work.

10. Reporting & Dashboards

If your team can’t see the data, they can’t act on it. Reporting isn’t a nice-to-have. It’s the operating system.

☐ You have a daily dashboard your buyer checks every morning. Spend, CPA, ROAS, and creative performance at a glance. If your buyer waits until Friday to check performance, you’re losing money Monday through Thursday.

☐ You track creative performance at the individual ad level. Not campaign level. Not ad set level. Individual ad. The scatter plot from Section 2 should be something your team builds weekly.

☐ You have a weekly reporting cadence with your team. 30 minutes max. What worked, what didn’t, what’s launching next week. No 45-slide decks. Just the numbers and the plan.

☐ Your reporting separates prospecting from retargeting. Blended ROAS hides the truth. A 4x blended ROAS could be a 2x on prospecting and 10x on retargeting. You need to see both.

☐ You track month-over-month trends, not just snapshots. A 3x ROAS this month means nothing without context. Is it up or down? Is CPA trending higher? Is creative fatigue accelerating? Trends tell the story.

☐ You share results with stakeholders who aren’t in the weeds. The founder, the CFO, the board. They need a one-page summary, not an Ads Manager export. Build it once and update it weekly.

How to use this

Count your checks. Figure out your biggest opportunities.

Don’t do everything at once, pick one high leverage change and keep improving your business!

Happy scaling!

Curtis

“You can’t turn off that ad. Facebook is sequencing the creative. What if that ad is a critical first touch?”

This has become the new meta-argument (pun intended) in performance marketing. It shows up in Slack groups, on LinkedIn, in pitch decks.

And I get why. Meta has rebuilt how ads get delivered from the ground up, and sequencing is a core part of it.

But the conclusion people draw, that you should just upload creative and let the machine handle everything, is getting stretched past what the data supports.

It permits lazy media buying. Turn “What if” into “Here is what the data shows” and keep improving.

The answer isn’t the opposite extreme either, day-trading your ad account with wild swings and killing anything that doesn’t hit ROAS targets in a 48 hour period. Or managing every ad at exactly the same CPA with no nuance.

I’ve reviewed roughly half a billion dollars in Meta spend over my career, most of it post-Andromeda. The answer lives in the middle. But where in the middle depends on your business, your spend level, and even what phase your campaign is in.

One thing I want to be upfront about: I don’t claim to know exactly how Meta’s algorithm works internally. Nobody outside of Meta does (Not sure if they do internally either).

What I can tell you is where the big ideas break down logically when you compare them against what I see in real accounts every day.

What Meta Actually Built

Three systems are working together now. The engineering behind them is worth understanding because it’s the foundation of the whole debate.

Andromeda is the retrieval engine. When an impression opportunity comes up, Andromeda narrows tens of millions of ads down to a few thousand candidates. It uses “Entity IDs,” which are clusters of semantically similar ads. If you upload 50 variations that are all minor tweaks of the same concept, Andromeda might treat them as a single entity.

GEM (Generative Ads Recommendation Model) launched in mid-2025. GEM is trained at a scale comparable to large language models. It predicts which ad, and which sequence of ads, will drive the best result for each user. It works across Facebook, Instagram, and Messenger at the same time. Previously, each placement had its own optimization model. GEM bridges those silos.

Meta reported that GEM drove a 5% increase in conversions on Instagram and 3% on Facebook Feed. For context, in standard creative testing, individual ads routinely outperform each other by 100-200%. So the sequencing system is a real improvement, but it’s not the magnitude of difference that some people imply.

Sequence Learning is the underlying paradigm shift. Meta’s engineering team published a detailed breakdown of this in late 2024. The old system used aggregated features: “ads a user clicked in the last N days.” The new system uses event-based features that preserve the order and timing of every interaction. It doesn’t just know what a user engaged with. It knows when, in what order, and what happened next.

Meta’s system now understands conversion paths as sequences, not just individual touchpoints.

So yes. Sequencing is real. The algorithm is deciding what order to show your ads in. Some ads are first-touch awareness drivers. Some convert on the fifth impression.

That’s the part everyone agrees on. And logically it makes sense from an awareness to conversion perspective:

The Case for Letting the Machine Work

The “trust the algorithm” camp has stronger arguments than most media buyers want to admit.

Start with the human bias problem. Most media buyers introduce more noise than signal. They kill ads based on 3-day windows. They anchor on whatever metric they checked that morning. They make gut calls that feel decisive but are statistically meaningless at the spend levels they’re working with. The algorithm is processing millions of signals per impression.

The argument isn’t really “do nothing.” It’s “most people’s version of active management is destructive, so the bar for intervention should be very high.” And that’s a fair point.

I’ve audited accounts where the media buyer was resetting learning phases weekly during their “Friday media buying spring”, never giving any ad enough data to prove itself.

Or worse, three people taking turns making changes at different levels of the account week after week with no shared framework. So much disruption.

Then there’s the adaptation speed. GEM saturates optimal audiences faster than the old system, so individual ads have shorter lifespans. But the system is already rotating spend away from fatigued creative on its own. If the algorithm is already cycling through your ads, your manual pruning might just be adding turbulence.

And the Entity ID clustering is real. Andromeda groups similar ads together. If your 50 hook variations all land in a single Entity ID, the media buyer spending an hour deciding which hook to kill is doing work the algorithm already handles.

Here’s the thing I’ll say directly: as of April 2026, Meta is a solid B+ media buyer for a clear product. If you sell a single SKU supplement with an obvious audience, Meta’s system will do a respectable job on its own with zero intervention on a bunch of ads in an ASC. If you’re not willing to be disciplined and data-driven about your account management, you’re probably better off letting Meta run it than doing it badly yourself.

Know your limits. For simple accounts (similar products by margin profile, clear ICP), a bad media buyer is worse than no media buyer in 2026. If the difference between a B+ and an A is millions in profit because of LTV gaps, margin complexity, or channel mix, that gap is worth paying for. But you have to earn that distinction with real work, not just button-pushing.

When I say “media buyer” I’m including financial strategy, creative insights, inventory awareness, and cross-channel thinking. If your definition of media buying is changing budgets based on interface data, that stopped working in 2024.

Where the Logic Breaks Down

So if sequencing is real, why am I not fully in Camp 1?

Because when I push on the specific claims people make, the logic falls apart.

The argument goes like this: “This ad had a terrible ROAS, but when we turned it off the entire account fell apart.”

I hear this same claim on Twitter, Slack groups, calls. But when I push on it, the answer is almost always “not my account, someone told me they saw it happen.”

If you’ve seen this firsthand with data to back it up, I’d love to talk. Seriously. DM me.

Think about what that claim is actually saying.

This ad is so important to the conversion path that the entire account depends on it. It’s the key first impression. It sets up every downstream conversion.

And it can’t generate a trackable click. On any attribution window. Not 1-day. Not 7-day. Not 28-day. Not first-click, not last-click, not multi-touch. Its all view through impact.

Important enough to affect the whole account. Not enough to tap “shop now” to learn more.

We aren’t arguing about impression campaigns here. This ad has a “Shop Now” CTA. It’s designed to drive action. And nobody who sees it takes any measurable action.

But it’s secretly the most important ad in the account.

So influential the account depends on it. So uninfluential nobody clicks on it.

That’s where the logic breaks for me.

And we’re not talking about a soft performer here. The ROAS is bad enough that you wouldn’t just pull back spend. You’d pause it entirely according to the quote I hear.

Could an ad contribute to awareness in a way that’s hard to measure directly?

Absolutely.

That’s real and I’ve seen it. But the version of this argument that gets used in practice, the version where a high-spend, high-CPA ad with declining engagement and zero click signal across every attribution window is actually a secret hero, that doesn’t hold up.

Now, a high-scaling ad that leads with a problem, speaks to non-aware customers, and has a slightly softer ROAS than your bottom-funnel converters? But most conversions are first click at a higher ratio than normal? That’s a different story. Keep it running even with that lower performance, we know there is some cookie loss. That ad is doing top-of-funnel work inside a conversion campaign, and it’s probably driving new customer acquisition that your last-click model undervalues. (I wrote a full breakdown of how to think about this with creative here.)

I also think most of the people repeating this narrative haven’t observed it in their own accounts. They heard it on a Reel or in a Slack group, and now it’s conventional wisdom.

It also happens to be a very convenient argument if you’re a media buyer who doesn’t want to be evaluated. “Just let Meta do its thing” is a nice way to avoid accountability.

Here’s what I tell my team instead. If you want to argue an ad is doing sequencing work, back it up with three things:

1. The creative logically speaks to awareness. It leads with a problem, not a product. It’s designed to reach people who don’t know your brand yet. If it looks like every other conversion ad in the account, it’s not a first-touch driver.

2. The first-click attribution signal is there. It should show a high ratio of first-click conversions relative to last-click. That’s the signal that people are discovering you through this ad, even if they convert later through something else.

3. The relative performance is reasonable. A softer ROAS than your bottom-funnel ads? Fine. A 0.3x vs 2.0x with declining engagement after 30 days and $3k in spend? That’s not a sequencing play. That’s a bad ad. Cut it.

The Algorithm’s Real Blind Spots

The real case for active management doesn’t rest on sequencing being fake. It rests on three gaps the algorithm can’t close on its own.

Meta’s goals are not your goals. Meta optimizes for high on-platform revenue per minute. They know giving you customers profitably is a piece of that but its not a 1:1 tie. Meta doesn’t care about your margin or whether one product creates a better customer experience than another except if it makes you spend more. It doesn’t know if one ad drives heavier retail or Amazon lift than another. If a discount-heavy ad converts at high volume but attracts customers who churn after one order, Meta’s system sees that as a win. If you aren’t tracking it and see that as a reason to spend more on Meta they will accept it. You shouldn’t. That gap is where human judgment matters most. The algorithm is working with incredible data, but it doesn’t have your margin model, your LTV curves, or your retail sell-through numbers. You do.

The algorithm can’t fix that your creative library is dying. It can only sequence what you give it. It can’t produce new ads. It can’t flag that the same three ads have been carrying the account for 90 days. The brands I see fail with the “let the algorithm work” approach aren’t failing because the system is bad. They’re failing because nobody is feeding it fresh creative, nobody is watching spend concentration, and nobody noticed the library went stale.

Meta is trying to fix this with their “AI ad tax” (pushing AI-generated ads without your permission, or with it if you turned it on I guess). If those AI ads are outperforming your creative, that’s a clear sign your library needs more diversity. Have you seen them? You can do better…

LTV segmentation requires human intervention. If different products or customer personas have meaningfully different contribution margins and lifetime values, and you’re optimizing for conversions, the algorithm will over-index on the easiest-to-convert customers. A subscription brand where one product line retains at 2x the rate of another has a massive LTV gap that Meta doesn’t account for. Active management, combined with MTA and predicted LTV data, is the only way to steer toward your best customers.

Meta is working on this (LTV optimization betas are rolling out, results are mixed so far) but for most accounts in 2026, this gap is still yours to manage.

The Creative Graveyard

I want to name the real danger that the sequencing argument correctly identifies, because it’s not wrong about everything.

I was on a call recently with a brand making 1000+ creatives per month. Their CMO said something that stuck with me. They have a “graveyard of creative that was probably working extremely hard for us, but was turned off because of reactivity to what we were defining as success.”

She was defining success as efficient direct-response conversion. But some of those killed ads had really high click-through rates, strong engagement, broad reach. They were doing awareness work. They were the first touch in a sequence. And they got killed because their direct ROAS didn’t clear the bar.

This is real and I’ve seen it at multiple brands. When you evaluate every ad by the same last-click metric, you systematically kill your top-of-funnel creative. And by top-of-funnel creative, I don’t mean awareness campaigns or impression-optimized fluff. I mean conversion-optimized ads that lead with a problem instead of your brand. Ads that can convert a customer who’s never heard of you. Those are the ones that get killed first, because they look weak on last-click ROAS even though they’re doing the hardest work in the account.

The inverse is also true: optimize only on first-click and you kill your bottom-funnel closers.

No single attribution model is perfect. Looking at multiple windows and understanding trends within individual ads over time is what separates a good media buyer from a dashboard watcher.

Over time, if you over-optimize to a single model, your ad library gets narrower. With last click, all your surviving ads are bottom-funnel converters. You lose the ability to reach new people. Growth stalls.

The fix isn’t “never turn anything off.” The fix is better evaluation criteria. And if you’re only looking at one attribution window (last click or 7-day click) before you decide to turn something off, you’re not giving yourself enough data points to make the call.

The Complexity Spectrum: Where Should You Sit?

I thought about cutting this section since its a side note but its really important…

Not every brand needs the same level of active management. Where you should sit on the spectrum depends on four variables.

Variable 1: How obvious is your audience to Meta?

If you sell dog products, Meta knows who has a dog. The signal is clean. The algorithm finds your people with minimal guidance.

If you sell something more nuanced, like specialty food for different age ranges with different medical needs, Meta’s signal is muddier.

Meta knows a lot about each person, but depth of knowledge doesn’t equal relevance of knowledge. It might know someone’s browsing patterns down to the minute, but it doesn’t know they just started a specific diet or that their kid aged out of your product line.

The clearer your audience signal, the more you can lean into the algorithm.

Variable 2: How complex is your product?

Single SKU supplement with one clear benefit? Pretty simple. The algorithm can figure out who responds to what.

Multiple product lines, different selling points per product, different customer personas, bundles versus subscriptions? Now you need creative that maps to each segment, and you need to watch whether the algorithm is matching the right message to the right person.

Simplest test: if a single ASC campaign does 90% of what your structured account does, your product is probably simple enough to let Meta do all the work. If you see meaningful performance differences by structuring different ad sets for different product lines, that complexity is real and worth managing carefully.

To be clear: managing your account does not mean turning off Advantage+ and going back to manual audience targeting. That’s not 2026. It means using the algorithm’s strengths (broad delivery, creative matching) while layering in structure at the ad set level so you can control budget allocation, test new concepts cleanly, and scale winners intentionally.

Variable 3: How different is your MTA from in-platform reporting?

If your multi-touch attribution (Northbeam, Triple Whale, whatever you use) tells roughly the same story as Meta’s in-platform numbers, you can trust the algorithm’s optimization decisions more.

But if MTA shows dramatically different performance from what Meta reports, the algorithm is optimizing for something that doesn’t align with your actual business outcomes. That gap is where human judgment adds the most value.

(Side note: If you’re spending $200k+/mo and don’t have MTA yet, it’s probably the highest-ROI investment you can make right now. We use Triple Whale across most accounts. Northbeam is solid too.)

Variable 4: How much are you spending?

At $10-20k per month, the cost of a bad decision (killing an ad too early, restarting learning phases) is high relative to your total budget. A lighter touch makes sense.

At $200k+ per month, you have enough data to make confident decisions quickly. And the cost of not managing is huge. Every day a bad ad runs at that level, it’s burning real money. Higher spend = more reason for active management.

The punchline: A single supplement brand spending $30k per month should probably run ASC and put its energy into creative production. Meta is a B+ buyer for that kind of account, and a B+ might be 90% of performance. A complex subscription brand spending $500k per month with 100s of creatives across different personas, different LTVs, big gaps between MTA and in-platform, and nuanced customer segments needs real hands on the wheel.

Most DTC brands fall somewhere in between.

The Practical Framework

Regardless of where you fall on the spectrum, these basics apply.

The three checks.

I don’t care what account structure you use. ABO, CBO, ASC, a hybrid, whatever. As long as three things are happening:

1. Your ads get tested effectively. New creative gets enough spend to prove itself. If you’re regularly finding that ads you paused early turn out to be winners when retested, you have a testing structure problem.

2. Your ads get cut effectively. Underperformers with enough data behind them get turned off. Budget stops being wasted.

3. Your top performers get scaled effectively. The top 1% of your ads drives something like half your revenue. Are those ads getting maximized? Are you building iterations around them?

If all three are working, the account is healthy. If any one is broken, performance suffers regardless of how sophisticated your sequencing theory is.

Think about what you’re removing, not just that you’re removing.

The risk isn’t removing an ad. It’s removing an entire concept or an entire stage of the funnel.

Turning off the worst-performing hook variation of a UGC concept? Totally fine. The algorithm has other variations in that entity cluster.

Turning off your only problem-first creative (the kind that reaches non-aware customers inside a conversion campaign) because its last-click ROAS is low? That might actually hurt the account. You created a gap in the ecosystem.

Feed the algorithm variety.

Sequencing means Meta is trying to show users the right ad at the right moment. If all your ads say the same thing in the same way, the algorithm can’t sequence anything meaningful.

Build for conceptual diversity: different formats, different messages, different angles, different funnel stages. Not 50 hook swaps on the same video.

One concept per $10k in monthly spend is my baseline for production volume, with 3 variations per concept. If you’re scaling quickly or burning through creative fast, produce more.

The Bottom Line

The sequencing infrastructure is real. Meta built a system that orchestrates multi-ad conversion paths.

But the debate has collapsed into two extremes that are both wrong.

“Never touch anything” ignores incentive misalignment, creative production gaps, and LTV segmentation. “Optimize everything by last-click ROAS” fills the graveyard with problem-first creative that was doing the hardest work in the account: reaching people who’d never heard of you and converting them inside a conversion campaign. Kill enough of those and your audience contracts, your new customer rate drops, and growth stalls.

And the most popular version of the sequencing argument, that a high-CPA ad with zero click signal is secretly the most important ad in your account, just doesn’t hold up when you think about it.

The right answer depends on your complexity, your spend level, and your campaign phase.

The algorithm decides the order. You decide the options.

And the quality of those options still matters more than anything else.

Share