I was on a call last week and a client mentioned, kind of proudly, that he had tested his main image against 500 shoppers. Maybe 900, he wasn’t sure. A big number either way.

Then he asked how many we use.

My response: Thirty.

There is always a pause when I share that number. I get this question quite a bit, and the pause is always the same length. Thirty sounds too small to be serious.

Here’s why I think people buy the big audience of respondents: they believe 500+ responses gets them statistical significance, and that statistical significance is what validates the result.

It is a completely reasonable thing to believe. It is also the wrong target for this kind of testing.

You are not looking for statistical significance in a consumer poll during the design process. You are looking for a number of validated reasons why people are voting the way they’re voting.

You read the language for patterns. The same objection surfacing over and over. The same detail getting noticed. The same confusion in five different people’s words.

Those patterns hand you the top five things to change in your design, and that iteration is what makes the next version more attractive to shoppers.

I won’t gatekeep, here is the exact sequence:

Four polls is the core sequence we run on every client project. When a round doesn't clear, you iterate and re-run before moving on, which is why some of our sequences are six or seven polls deep.

Every one of those polls runs at 30 shoppers. Four polls, 120 shoppers, less than one 900-person poll costs on most panels. Those remaining 750 shopper votes would have been better spent across another 25 polls for your main image, A+ content and product image stack.

The ultimate goal should be to improve your vote share from the baseline poll 1 to the final poll 4 by a considerable leap, set a goal of 10 points.

(If you still don’t believe me on the 30 respondents don’t worry, I’m about to break it all down for you.)

What a dollar a vote actually buys you

Let’s do some girl math together to help me prove my point.

The biggest polls we’ve run had 100 shoppers in them. So I went back, pulled the 100 written responses, and counted how many distinct reasons shoppers gave for their choice, one respondent at a time.

Thirteen of the fourteen reasons in that poll were already collected by respondent 30.

Respondents 31 through 100 gave me exactly one reason I did not already have.

For me the 13 reasons are everything. 100 votes means nothing without the reasons. The reasons collected from shoppers are the gold behind the design process.

Now put a dollar on every vote, for funsies:

Mic dropped. The first 30 shoppers hand you 93% of the reasons at about two dollars each.

Everything after that gets progressively more expensive for progressively less actionable. In the end, you are paying seventy dollars for a single insight you could have likely lived without. Make it make sense.

70 extra shopper votes can buy you the ability to trust a closer result but that misses the entire point of this type of testing. We don’t care about the winner, we care about the why behind the voter responses.

On this poll that “extra trust factor” made no difference at all.

One poll can be a fluke, so I ran it again on five more. All at 50 shoppers, five different clients, four main image tests and one A+ content test. I read all 250 written responses and grouped them one respondent at a time, before splitting anything.

67 of the 71 reasons were already there by shopper 30. Two of the five polls got nothing at all out of their second half.

The four late arrivals were one comment that an option looked AI generated, brand recognition twice, and one mention of a clinical badge. None of them would have changed a design.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for, broken down so you can use it yourself.

• The full breakdown: what each of the 4 polls is really for.

• 5 real testing examples with the actual before and after

• The 2 where our own iteration made things worse

• BONUS: Claude prompt that audits your own poll.

Read more

Last week a brand owner emailed me back with a candid question, one that I actually hear quite a bit.

I had sent him a breakdown of the results some of our clients are seeing from their A+ content. He wrote back:

Fair question. And he’s not wrong, exactly. He’s just measuring one conversion lever and applying it to all three.

Here’s the TLDR and then I’ll break down each one:

1. Getting found. Your SEO does the heavy lifting here.

2. Getting the click into your listing instead of a competitor’s. That’s your main image.

3. Getting the add to cart. That’s your product image stack AND your A+ content.

Three stages. Three different levers. The main image is genuinely excellent at exactly one of them.

(Full breakdown of which lever to fix first, the data behind each one, and a Claude prompt to diagnose your own listing below.)

Let’s add some clarity, becuaes people often use different terminology when it comes to PDP creatives. Here’s the terminology we use:

• Your main image is the product-on-white that shows up in the search results. It’s also the first image in your carousel of product images.

• Your product images (aka image stack) are everything sitting behind it in that main image in the carousel, and these are all above-the-fold on your listing.

Sometimes I forget that unless you are in the depths of building Amazon listings day in and day out, none of this is obvious.

Not everybody knows the process we go through to build or refresh a listing. Not everybody has a reason to know that a click and a sale are two completely different jobs. So I thought I’d break the whole thing down today.

1. Getting found: this is where the SEO does the work

Amazon is a search engine. That’s the part people skip.

The only way Amazon knows where to put your listing, and when to serve it to a shopper, is if you’ve put the proper keywords into your title and bullets, AND if people searching those keywords end up buying your product. That second half is what signals to Amazon that it’s showing you in the right places.

So SEO is the first thing to fix. Not because it’s the most exciting, but because nothing downstream matters if nobody sees you. You can have the best main image in your category and it will do nothing at all from page four.

The takeaway: SEO is the cheapest lever you own. It takes no designer, it’s a copy change, and you can be running it while your designer is still building images.

Test to run: Title and Bullets are two separate tests, and both go straight to Amazon’s Manage Your Experiments. Change one, hold your images and your price constant, and let sessions and impressions tell you.

Protip: if you don’t have Manage Your Experiments access, you can still do this manually. Pull your before numbers from Seller Central, make the change, give it a real window, pull the after.

2. Getting the click: this is your main image’s entire job

Here’s where the brand owner who emailed me is right.

Your main image is the lever that steals the click from your competitors in the search results. That’s the whole job. And it’s a big one, because in a search grid you’re fighting 20+ competitors for the same thumbnail-sized moment.

Our data on this is not subtle. Everything below is a sample of our database, main image tests only:

• A bare product shot with nothing on it wins 13% of the time (vs 28% baseline). That’s 417 designs across 246 tests. Translation: it loses less than half as often as luck would predict.

• An image with no copy on it at all wins 11% (vs 30% baseline), across 253 designs and 172 tests.

• An image showing the product actually being used wins 43% (vs 28% baseline), across 286 designs and 178 tests.

What we found: showing your product being used, with words on the image, beats a clean product-on-white almost every time.

The Learning: a good main image gets you the click. It does not get you the sale. Yes, they might find your product. Yes, they might click into your listing. Whether they add to cart is decided by something else entirely.

Protip: showing the product is the bare minimum imo. One step better is showing it in the moment somebody actually uses it, with the thing that gives it scale in frame.

3. Getting the add to cart: the one everybody skips

Now we’re inside the listing. Your product image gallery and your A+ content.

This is where the buy now happens, and the job here is completely different. It isn’t to be noticed anymore. They already noticed you, that’s why they clicked. The job now is to answer the questions in their head before they bounce to the next product page or advertisement that catches their attention more than your content.

Your carousel and your A+ content are doing the same kind of work. Both have to grab attention, both have to answer questions, and you have no guarantee anybody sees either one. Some shoppers swipe the carousel and never scroll. Plenty scroll straight past it.

The way I think about A+ content is that it’s your last chance to get one, two or three things about your brand or product into somebody’s head and win the add to cart, before they hit the reviews.

Why I think of each lever as a different job

Take a typical image, like a “what’s included” design. You know, the one that has the shot that lays out everything in the box, all the pieces, the full kit.

• On the main image it wins 37% of the time (vs 29% baseline). That’s 343 designs across 195 tests. Strong performer.

• In A+ content the exact same idea wins 23% (vs 30% baseline). That’s 26 designs across 17 tests. It loses.

Notice how the same design idea can win and lose. Nothing changed about the design. What changed was WHERE it sat, and therefore which question it was answering.

On the search page, “here’s everything you get” is a click worthy visual. It’s value and fast, even at thumbnail size.

Inside the listing, the shopper already knows roughly what they’re getting. They didn’t click to be told again. They clicked because they had a specific worry, and that is what you need to be answering next.

Here’s why I think this happens: the further down the page a shopper goes, the more specific their remaining question gets. So repeating your opening visual deeper into the listing becomes the most common wasted opportunity.

To clarify, I’m not saying to “never repeat yourself,” because I actually tell clients the opposite.

You have no guarantee anyone will see every image in your carousel. You also have no guarantee anyone will see your A+ content. You have to build both as if it’s the only visual they will see, and as if they’ll give it a few seconds. So repeating a message across the two is fine. Repeating it in the same form is the wasted opportunity.

Say it and show it in a different way the second time.

That’s my recommendation from doing this for years, not something the database proved.

The takeaway: Work out the top three to five things somebody needs to know about your product, and put those front and center in both places.

(The A+ sample here is 17 tests, so treat this as a strong pattern worth testing on your own listing rather than a law. I’ll have more data on this soon, we run these constantly.)

The part most brands get backwards: less is more, but only if it’s the right less

I think one of the hardest things to do as a founder or brand operator is to remember that your shopper is not carrying 50 questions. They’re carrying somewhere between one and five.

That’s my read from sitting in on hundreds of these tests and reading the comments day after day. The pattern I see: shoppers stop where something answers the question in their head, and they leave when they can’t find it fast.

So the order of what you show and say in your creatives matters more than the volume.

Which creates the actual tricky part of this work. You need enough density to answer everybody, and you need enough hierarchy that the top three questions get answered first. Those two things fight each other, and the fight is won with legibility, hierarchy, and testing. Not by adding more items to the mix.

Two things from our database that back this up:

One lackluster image can cost you the test. In a product image stack, when every single image carries copy the option wins 40% of the time (vs 30% baseline).

The moment even one image in the stack is text-free, it drops to 17% (vs 31% baseline). That’s from a small sample of just 13 tests across 11 different brands, so it’s a pattern to test, not a rule. But the direction is clean and it matches what shoppers tell us: they praise “easy to read at a glance” and they punish “boring” and “too wordy” at the same time.

In A+ content, the bolder more legible direction wins almost every time. Across 24 A+ tests spanning 13 different brands, the design with the biggest, most legible headline won 22 times. The design with the smallest headline won once.

For transparency: shoppers almost never SAY headline size is why they picked. They talk about legibility in general and they’ll credit the winner and the loser about evenly for it. So this one is a pattern I’d judge on the vote split, not on the comments. People are not always able to tell you what drove their choice, and that’s normal.

My controversial take…

What most designers do even today is design the images they think should be designed, because everyone in the category designs them that way and it reads as best practice.

That makes complete sense. And while we know that before and afters are a MUST in the beauty category, where they live and how your show them impact performance tremendously.

Just having them is one piece of the puzzle, and it’s NOT proof it performs. The only way to be sure is to run a split test yourself.

The Shopify brands, and why their beautiful content goes quiet on Amazon

I’ve told you about Akasha Superfoods before, so I’ll skip the setup. Shopify brand, doing well on their own site, launched on Amazon, barely sold anything. We rebuilt it and sales went up 589% in the first 30 days.

What I didn’t tell you last time is what was actually wrong with the images, and it started somewhere nobody looks.

• The product images were just photos of the front, side and back of the bottle.

• Their label didn’t list the functional ingredients. Bladderwrack was in the product. The label never said so.

• There was no A+ at all.

Let’s dial in on the label issue. A shopper searching bladderwrack by name, somebody with high intent who wanted exactly this, had no way to land on it and Amazon had no reason to serve it. The ingredient was sitting in the jar but Amazon couldn’t read it.

We built it into the creative, so now it’s one of the first things anybody sees.

The rest of the gallery had the missing context. Not one of them answered a question. So we rebuilt the mix, lifestyle and product together, around the two things shoppers in this category actually ask: what is in this, and what will I feel if I take it. Lastly, we added a branded layer to this so the image felt custom to the brand.

When we first started building listings like this, I was going off of a hunch. My read was that people want to be brought into a brand’s world, that they want to feel an aesthetic and not just get cheap looking “fiverr or AI” images thrown at them.

I had no data. I had taste and a theory. It took hundreds of listings before we could see the pattern in our own craft and put numbers underneath it.

Everything I’ve shown you above is that pattern, finally tracked and counted.

And the thing about Akasha that still gets me is that nothing about the product changed. The demand was already there. They already knew it sold, because it was selling on their own website every day.

They just hadn’t set it up on Amazon in a way that let anyone find it, click it, or feel confident buying it. Three levers. That was the entire gap between a rounding error and a real channel.

They’ve since launched a whole catalog, and they’ve rebranded and elevated their content again since we worked on it. That’s what we hope for every brand we work with by the way.

Brands step ladder into their identity. You don’t need it all figured out at once. You elevate it every time you get the chance, as you learn more about your customer and as you get clearer about who you are.

The key thing to remember is how different the shopping experience is from Shopify to Amazon. We take the best practices from both.

Translation: the same images, in a completely different environment fall short. The photography that reads as confident and premium on your own site reads as boring on Amazon, because on Amazon every image has to show AND tell to keep attention.

The fourth thing, and it’s the one that de-risks all three

Beyond the three levers there’s one more piece, and skipping it is the most expensive habit I see.

Split test your content. Both during the design process and on Amazon.

One of the riskiest things you can do is go to a designer, or go to ChatGPT, get something made quickly, and throw it straight up on your live listing. That’s a real revenue swing you’re taking blind.

Here’s the sequence we run, and you can run it too:

1. Pre-validate in PickFu while you’re still designing. This is directional data from real shoppers, and it’s best for anything visual: main image, image stack, A+ visuals. It tells you whether the thing communicates what you needed it to communicate, and it lets you take the lessons from competitors already winning in your space.

2. Improve the design based on what the comments actually said. The vote tells you which one. The comments tell you what to build next.

3. THEN upload the winner to Amazon’s Manage Your Experiments to validate against live traffic.

That is the order. And that is how you turn something otherwise risky is what into a measured practice.

Most important, it’s not a one-time project.

Content should be tested and improved throughout the year, because a winning main image can still be beaten, and because as your reviews accumulate your customers hand you their objections and their love in their own words. That language is free and it’s the best material you’ll ever get.

Protip: the easiest testing win nobody takes is your own review section. Pull the objection that shows up most, and make an image that answers it.

PS - We track results across clients and publish the data, so you can see how each lever gets tested: mindfulgoods.co/mindful-metrics

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for, broken down so you can use it yourself.

Below the paywall:

• The one design format that wins in BOTH places, and why it’s the exception

• How to tell which of your three levers is actually broken, using numbers you already have in Seller Central

• The order we fix them in when a brand comes to us head to toe, and why we never start where the client thinks

• How I would rebuild an image stack that’s answering the wrong questions

• A Claude prompt that diagnoses your own listing lever by lever

• The Three Lever Decision Matrix: your symptom, your broken stage, your first test

Read more

This week Inaya and I recorded a video breaking down the 10 best storefronts in the pets category on Amazon that are inspiring our team at Mindful Goods right now.

I won’t spoil it for you. There are way too many gems in the video to cover in this newsletter but today I’m sharing the top data that we’ve been seeing behind the scenes for the Pet category.

Storefronts still remain the only uninterrupted space on Amazon. There are no competitor ads, no sponsored rows, no distractions. It’s a great place to focus on conversion and it’s also a great place to build out custom landing pages for driving paid traffic.

One thing that we’re known for at Mindful Goods is making what we coined “drool-worthy” content. For me, this term applies to every category but even more so when it comes to anything food-related.

We’ve worked with so many brands over the years and done so much split testing that I’ve realized how important it is to showcase your product in the best light possible regardless of the category.

The idea behind drool-worthy content is that if you can’t smell or taste the product, the next best thing is to make someone drool when they see it on the screen. Show the drizzle, show the shine, show the transformation, get the close-up shot, capture the feeling, the moment...you get the point.

When I say, make it drool-worthy I mean show, don’t tell.

When we think about applying this to the pet category, it’s not that we’re trying to make the customer hungry for pet food.

Yes, we want the product to look appealing but the focus becomes less about the food and more about the moment with your pet. And the facial expression of a dog or a cat is what draws that emotional connection from the customer.

A pet food customer is not thinking, “Oh that looks so yummy for my dog.”

They’re thinking of the joy that their dog is going to get when they get to eat this food and they’re thinking, “Is this the right food for my particular dog breed?”

And we have data to back this up.

When we run a PickFu split test, we’re not just looking at which design won. We’re looking at the qualitative feedback that the customer is giving us and then we’re tagging that feedback into various different themes so that we can ask super pointy questions to our database like, “What do pet shoppers care about that other shoppers don’t?”

And here’s the response to that question.

1. Appetite appeal is not a primary focus in the pet category. It only shows up 8.5% versus 49.5% in the general food category.

2. Emotional comments are a main factor. Shoppers saying things like, “that dog looks so happy,” shows up in our pet testing 4x the rate of anything else in our database. Emotional comments don’t tell us which design wins but it does tell us that customers are tapping into their emotions when it comes to their pets.

3. Self-identity is another big one, it shows up 2.3x more than anything else in our database. Shoppers saying things like, “that dog looks like my dog” means they can instantly see their dog using this product.

The content shift for the Pet Category:

When building creatives for a food product that a human is going to eat, you would want to make that food as appetizing as possible. You would want that to be the focus of your creatives.

When you are showcasing a pet product, you want the focus to be the actual pet themselves. More specifically you would want it to be a photo of the dog that conveys an emotional moment that the pet owner can imagine having with their pet.

So while yes we always want the food or the product or the creatives to look good, we more so want to see how the pet is reacting to the product.

In the examples of Native Pet they did such a great job of showing the dog licking the actual box or bag of the food product or, in the case of Wuffes, showing the dog’s nose touching the product as if they’re so curious and want to smell it and they’re very interested in it.

Those are very natural cues that a dog might express if they were near a food product. Capturing and showcasing closeups of those moments are the key.

In the 10 storefronts that we showcased in our video, very rarely was the product or the food ever shown alone without a pet. In fact, the only brand out of the 10 that really focused on food rather than pet was Maeve and that might be because they’re deliberately leaning into a clinical supplement feel with their branding.

Creative Rule #1: Pet Identity

I’m calling out pet identity as the number one rule in the pet space not just because it’s mentioned 2.3x more than anything else in our database. It’s because of the language that was used by shoppers when they voted for a creative that had their dog’s breed or age or a cat or dog that looked like their dog. They typically stated it as the entire reason for choosing the design that they did.

This is why Woof’s design works so well because almost in every single section of their storefront they’re showing a range of breeds, sizes, colors. So the odds higher that any given shopper might find their dog in the mix.

(Though Wuffes, if you’re reading this, you still don’t have a Frenchie anywhere. My guy, Piggy Smalls, is available for hire and his rate is one treat. :-)

Creative Rule #2: Gender Divergence is real.

In the Pet category men and women vote differently on the same creative. This happens twice as often as it does in any other category in our database.

Depending on the product the votes might shift in one way or another based on gender. In one group of polls we noticed that women were leaning more towards packaging and brand recognition while men were leaning more towards lifestyle images of dog imagery. We also have polls that show the exact opposite, depending on the product.

The key takeaway: you should be pulling your brand analytics to see who your primary buyer is. And you should be building a mix of packaging shots and lifestyle dog imagery shots throughout your content to appeal to both audiences if you are targeting both audiences.

🔒Now let’s get to the good stuff.

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks per month and you get the strategy I charge clients real money for, broken down so you could use it yourself.

Below the paywall:

• Color palette for pet best practices.

• My six-section storefront build order for pet brands mapped to the exact lever each section is pulling.

• The tradeoff between a beautiful storefront and the converting one that nobody warns you about.

• The trust stacking rule that matters more in the pet category and why.

• A copyable Claude prompt that scores your existing storefront against all of this and hands you a ranked fixed list.

Read more

It’s done and I’m sharing it here with my substack community. Now, non-designers can easily add disclaimers in 1-click.

In this video I show you how to use the tool, walk you through the five templated disclaimers to choose from and share my thought process on how we recommend adding disclaimers to your images for Amazon.

I built this tool to solve these three simple questions:

1. What should the disclaimer actually say?

2. Where should it go on the image?

3. How can this take as little time as possible for a non designer?
   

Protip: We label the file name of our files so that the clients clearly know which images have AI Humans in them and which ones don’t.
Ex: brandname_pi1_aihuman.jpg

If you want to give this tool a try. This is the link to the tool directly.

TRY THE TOOL

(PS - I’m leaving it ungated and completely free for anyone to access and use.)

To recap:

New York State’s law has been live since June 9th. If your listing has an AI-generated human in it, you should be adding a disclaimer to avoid getting charged a $1,000 fee.

If you want to go deeper, here are the previous posts that I’ve written about this in case you want to reference them.

At this point in time Amazon is only requiring you to select a checkbox on the backend but it doesn’t add a disclaimer to the frontend, which is what New York State law requires if you have an AI human in your images.

Maybe it’s because everyone was heads down on Prime Day, maybe it’s because none of it came with a press release, but I wanted to take a moment to recap three big-ish things that changed on Amazon in about six weeks.

I found out about all 3 of these via LinkedIn. That’s probably why most brands I speak with have still only heard about one of them.

AND more importantly, one of them can cost you $1,000 per offense.

I won’t gate keep this part. Here’s the whole list, and then I’ll break each one down:

1. AI-generated people in your images now have to be disclosed. New York law, live since June 9. Amazon has its own separate tagging requirement on top of it.

2. Your product titles got capped at 75 characters on July 27, and Amazon’s AI is rewriting the ones that were too long.

3. Amazon quietly put a free benchmarking tool inside your Storefront builder that will grade your own pages against data they used to keep inside their certification program.

The first two you require action to stay compliant. The third is a little gem from the Amazon gods.

A little context on this newsletter: Earlier this year, I did something I’d been wanting to do for years. Using Claude, I analyzed 900+ PickFu split testing polls (now 1,000+) from 34K (now 38K+) respondents across the 850+ eCommerce brands we’ve worked with. The result was a database that lets me look up trends, spot patterns, and answer almost any question about a client’s creative before we even start designing. This has reframed how my team and I make every creative decision and I’m sharing all of it here in Design Proof. Twice a week: data-backed creative insights to help you convert better on Amazon and eCommerce.

1. AI humans in your images (Happening now, with a fine $1K fine)

We’ve been using AI in our creative work for over a year now, and that’s not changing. If anything, we’re leaning further into it. That said, we use it to augment design not create a design from scratch.

And with that comes a responsibility we take seriously, which is staying on top of how the laws around AI use are evolving. And honestly? I think that’s a good thing. The more guardrails that exist around AI, the better it is for everyone including brands, shoppers, and the industry.

What changed: New York passed a synthetic performer disclosure law, General Business Law 396-b. It took effect June 9, 2026. It requires advertisers who know their ad contains a synthetic performer to conspicuously disclose it.

A synthetic performer, in the law’s own definition, is a digitally created asset made or modified by computer using generative AI or a software algorithm, intended to create the impression of a performance by a human performer who isn’t recognizable as any real identifiable person.

Translation: the AI model holding your product, and now she needs a label. Whether it’s just the hands or a whole AI human, you should label your images with a disclaimer.

The part people are missing: there are actual penalties, and they’re issued by the state of New York. $1,000 for a first violation, $5,000 for each one after that.

Every Amazon product listing has the potential To be served nationwide and there’s no way to control which states are going to view your product listing. Therefore every listing is liable to be compliant.

And if New York is the first to pass this law, they’re likely not going to be the last state to pass this law or something similar to it.

The Amazon requirement

This is the piece almost nobody is talking about, and it’s the one that touches every file you upload. Every time you upload an image Amazon is asking you To check one of two checkboxes:

1. Option A: AI-generated image

2. Option B: AI-generated people

Sharing this because this part confused me: If you toggle the second checkbox, the first checkbox is automatically selected for you. Toggle only the first checkbox then Amazon leaves the second checkbox unchecked unless you check that box.

Amazon’s own words: “This requirement only applies if your image or video contains photorealistic AI-generated people.”

Amazon uses this entry to add metadata to your image and of today they have not disclosed how or if they will add disclaimers to images.

My guess is that they are already testing. My concern would be if Amazon decides to add front facing disclaimers that are disruptive to conversion.

BUT here’s the important part, the New York law wants a disclosure a shopper can see.

Amazon’s request only adds a keyword in the file that a shopper can’t see. That does not satisfy the NY law.

Something like this example:

Here’s what we’re recommending to clients

Initially we were adding disclaimers to images on behalf of clients but I’m not sure that is the right move. Some clients may not want us to add disclaimers to their images.

For the moment what we are doing as an agency is adding a clear note to the file name. This way, our clients know which images have AI humans in them or not. Using that name of the file they can decide if they want to add a disclaimer or if they want to tag it when they upload it to Amazon’s dashboard.

To make the visible disclosure straightforward, we recommend adding a small text disclaimer to all new creative that features AI-generated models.

Three examples, pick the one that fits your brand:

Option 1, human touch: “This design features AI humans, made by real humans.”

Option 2, brand-forward: “AI-generated models. Human-obsessed creative.”

Option 3, minimal: “Imagery includes AI-generated models.”

Protip: to do this yourself in about five minutes, open the file in Canva, drop the disclaimer in small type in a corner where it doesn’t fight your callouts, and re-upload to Seller Central. You do not need your designer for this.

Some top questions I’ve seen so far with my take:

AI-generated hands holding your product? I’d tag it. The law says: “the impression of a performance by a human who isn’t a real identifiable person.” AI hands are an impression of a human action, and they belong to a fake AI human. No face is a gray area but still risky.

AI-generated background behind a real model? Coverage says the background alone doesn’t trigger it, because the law is about people, not scenery or even pets. Which makes hybrid images, real model + AI environment, a genuinely good strategy. But any single AI human in a background can still trigger a fee.

The useful reframe on all of it: the rule isn’t written around what percentage of your image is AI. It’s written around whether there’s a person in the frame who is not a real human.

2. Your titles got cut to 75 characters (Already happened, July 27)

I wrote about this one before it landed. It’s arrived.

What changed: Amazon capped product titles at 75 characters including spaces, in every category except media. The old ceiling was 200. Any listing still over the limit gets rewritten by Amazon’s AI, gradually, without you doing anything.

The detail that decides how bad this is for you: if you’re Brand Registered, you get a 14 day window to review Amazon’s rewrites in Review Listings Changes. If you’re not Brand Registered, there’s no review window. Amazon just applies it.

Amazon also introduced Item Highlights, a 125 character field that shows up below your title in search results and on the detail page. It’s searchable. That’s where your secondary keywords, materials, and use cases go now.

My read: this isn’t really as big an issue as everyone cried about. Titles were already truncated on mobile, and over 70% of Amazon shoppers are on mobile. Amazon using the title you had, they’re just asking you to break it into two. The brands getting hurt are the ones who never looked at their own listing on a phone.

In case you missed it: We created a Title Playbook and Title Generator to help brands reformat titles for Amazon’s new two-field system. Built from 74 tested winning titles, it applies the eight conversion patterns we found across our client experiments to generate a 75-character title and matched Item Highlights field.

Full playbook + tool access on Stan Store → [Stan Store link]

GRAB THE TITLE PLAYBOK

3. Amazon put a free benchmarking tool in your Storefront builder (the new gem)

What changed: there’s a feature called Analyze Performance inside the Storefront AI Builder that hands you benchmark data Amazon used to only keep inside their Creative Certification program for select agencies.

Where to find it: Seller Central → Storefront → AI Builder → Analyze Performance (bottom right corner).

Run this exact prompt in your own Storefront right now. Takes two minutes and costs nothing:

“Let’s do a larger analysis of maybe the last six months of store traffic and get your recommendations.”

You’ll get a feature-by-feature breakdown of what’s working on your storefront and where to improve, with the data behind it.

The problem: generic recommendations. I ran this across client accounts and the tool has all the metrics and none of the eyes. It can tell you a page underperforms. It cannot tell you that your category tiles are in the wrong order, or that your product section is illegible on a phone. When I pushed it, the tool was playing dumb because it couldn’t actually see anything.

The solution
Pair it with Claude vision with these two steps:
1) Extract as much storefront data as you can out of Amazon.
2) Upload that data plus screenshots of your storefront design to Claude/Chatgpt and have it analyze both together.

Using this method, we discovered real problems like traffic routing and module type, and most of them were fixable without touching a design.

Full step by step breakdown of that one is in Monday’s post, including the Claude prompt.

So what to do this week

I’d tackle these 3 things, in this order:

1. Sort your lifestyle imagery into real model, AI model, no people. Add disclaimers and re-upload to avoid any surprise fees.

2. Check whether Amazon rewrote any of your titles. If you’re Brand Registered, your review window is 14 days from the change, so look at Review Listings Changes now rather than in September. Then move your dropped keywords into Item Highlights. (try our title tool here)

3. Run the Analyze Performance prompt on your storefront. Free, two minutes, and it hands you Amazon’s own benchmark data.

We can all agree that compliance work is nobody’s favorite when it comes to running an eCom business, but a disclaimer in a corner is a five minute Canva job, and a rewritten title you never reviewed is a reach problem you’ll be diagnosing for months.

Daniela Bolzmann is the founder of MindfulGoods.co, an Amazon creative services agency specializing in data-driven content for 850+ 7 and 8-figure brands.

Connect on Instagram, TikTok, and LinkedIn or book a strategy call.

I opened a client’s storefront last week with one question: do we need to increase dwell time or not?

Increasing dwell time on storefronts has been a focus and best practice for my team for years. The logic is simple: keep people on the page as long as you can, and a percentage of them will convert the longer you hold their attention.

Pair that with strong merchandising and streamlined navigation strategy we’ve usually been able to build a strong storefront. I’ve never really questioned it, and I went in expecting the numbers to tell me how much more of it we needed.

This practice has given us great results like 58% increase in sales from our work with Buffr.

BUT the deeper I dug into Amazon’s new AI storefront tool the more the data revealed and actually made me pump my brakes.

Spoiler alert, dwell time was the wrong focus on a homepage.

Don’t worry, because we’ve developed a new best practice for auditing storefronts.

The important thing to share is that by auditing a handful of client accounts I was able to pinpoint immediate conversion issues with a hit list of fixes we could make immediately, without touching their design at all.

And a conversion killer I almost missed entirely that I wouldn’t have been able to catch without this new process.

Here’s why I think most brands are falling into the design trap, and I’d put us in this bucket too: when a storefront underperforms, it’s easy to treat it as a design problem, because design is the thing you can see.

After this week, I’m certain the better first fix is checking for a routing problem and a module-type problem. Neither of those is visible until you put the store metrics next to your actual design like I did with Claude and Amazon’s AI tool.

I won’t gatekeep this part. To do this yourself there are two steps:
1) Extract as much storefront data as you can.
2) Upload your data and storefront design screenshots to Claude for analysis.

( full step by step breakdown and claude prompt below)

So first, how do we get these store metrics?

Right now, there are two Amazon tools that will surface your Amazon storefront data. One of them almost nobody has yet.

1. Sectional Performance (beta)

Amazon is testing a feature called Sectional Performance on a small number of accounts. We’ve only seen it on a handful of the ones we have access to.

→ Check your account to see if you have this available to you.

It overlays four numbers on every individual module section of your storefront: renders, views, clicks and click-through rate.

Then it lets you filter by page, by traffic source, by mobile versus desktop, and by store version.

Storefronts to date cannot be tested in Manage Your Experiments. My hope is that Amazon will add that soon.

For now, this tool can help us solve that problem by checking module by module performance on different versions or mobile vs desktop.

2. AI Store Builder (New)

A couple of weeks ago I wrote about the new AI chat tool sitting inside Amazon’s storefront builder. I shared my favorite prompts for extracting storefront data.

Find it in Seller Central → Storefront → AI Builder → bottom right corner → Analyze Performance. [Link to my previous post.]

My honest take, the tool could be better but it’s still early days so I’ll celebrate the progress by Amazon and save my heavy criticisms.

What I do love so far, is that it can easily extract data points for you, quickly.

Where it falls flat is when you ask it anything around strategy or anything with deeper context or specificity. You’ll get a dozen excuses, that it can only report on your metrics data and refer you to the Stores Help documentation, which we all know is pretty useless.

My takeaway, this tool is for pulling metrics, not strategy.

That wasn’t good enough for me. So I tapped my chief strategist Ella, aka Claude, and together we built an entirely new approach to auditing storefronts for our clients.

The rest of this post shares the play-by-play of pairing Amazon’s AI with Claude. And sometimes pitting them against each other for funsies.

Each and every learning was paired with a strategic lever and came from a gap that one of them couldn’t see on its own.

The questions to try:

The order you ask matters. I asked the questions in the wrong order the first time, and the tools didn’t give me the depth I needed.

Ask these first, in this order:

1. Give me a traffic source breakdown for the last two weeks (or 6 months), with bounce rate and dwell time. Start here because it tells you who is arriving and whether they behave like buyers. On the store I audited, this is where I found one social channel sending nearly half the traffic and almost none of the sales.

2. Break down traffic and conversions by source, including orders and sales per visit. Question one gives you engagement. This one gives you money. They rarely agree, and the gap between them is usually the whole story.

3. What’s my Store Quality Score, and what are the top recommendations by potential impact? Write these down. You’re going to check them against your own page design later, and some of them will not make the cut.

4. Which of my pages are driving the most engagement and orders? The most valuable question in the whole list. Ask for conversion rate and dwell time per page, not just visits. Every decision later in this post comes from this answer.
   
   Follow up with these, the goal is to extract as much as possible:

• Which storefront pages should I create next to improve discoverability and shopping flow?

• How well does my storefront align with my Sponsored Brands traffic, and where is the mismatch?

• What evidence suggests shoppers are interested but not progressing deeper into the store?

What we were able to extract

By going deep with the questions above, the Amazon AI tool identified insights like this across our clients:

• Brand-driven organic traffic is the strongest audience. Generating nearly all recorded sales.

• Social traffic produces substantial awareness but weak purchasing behavior.

• The main problem appears to be traffic routing, not a lack of shopper interest. Several audiences spend meaningful time browsing but cannot easily find the right products or next step.

• The homepage is the largest conversion bottleneck. Too many visitors are sent to a general page that does not immediately surface products, categories, or clear shopping paths.

• Specialized category pages substantially outperform the homepage. Shoppers who reach focused product category pages are much more likely to purchase.

• Paid advertising appears misaligned with its landing experience. Ads drive visits but low sales.

• The greatest opportunity is improving how shoppers move from the homepage to products.
  

The problem: generic recommendations

Amazon accurately shared plenty of great metrics but fell short on actionable recommendations. I saw things like:

• The recommended homepage redesign would prioritize:

  • A clear hero message with a strong shopping CTA

  • Prominent links to the strongest categories

  • Visible bestseller and new-product grids

  • Product-level calls to action

  • Solution-oriented shopping paths based on customer needs

  • Brand story and trust signals near the bottom

• New intent-based pages could improve discovery. Suggested concepts include shopping by problem or desired solution, curated bestsellers, bundles, educational guidance, and a stronger brand story.

• And called out basic signals worth testing:

  • A text tile linked to a product page at +34% sales

  • A text tile linked to a sub-page at +32%

  • A product grid on the homepage at +33%

  • A background video at +27%.

(Those are Amazon’s numbers for its peer set, not results we measured.)

Amazon says clearly, that these are a signal worth testing and not a guarantee.

The Solution: vision + data

As I looked through multiple storefronts’ data, something became obvious to me:

Amazon’s AI tool is not able to see our designs. The tool is only reading and reporting metrics, it’s not able to strategize against the data to give us a detailed module plan or layout because it’s running blind.

I had more unanswered questions like:

• Are the category tiles in the wrong order?

• Are the product sections legible on mobile?

• Should the brand video live above the fold?

Problem: every recommendation Amazon gave above was generic at best.

“Add a tile, add a grid, add a video.” Not one was about moving, reordering, removing, or redesigning something that already existed.

Solution: pairing with Claude’s vision gave me actionable guidance

When the AI report said add a video, in some cases we already had a video.

And the reorder turned out to be free and probably the biggest single lever across all client storefronts.

Again, the lightbulb moment was realizing, the AI tool was playing dumb because it couldn’t see anything. It has the numbers, not the eyes.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for, broken down so you can use it yourself.

Below the paywall:

• The exact way to run this audit the way we did

• Why the mobile view is the only one that matters, and what it exposed for us

• 5 things we keep finding on storefronts that no analytics tool will ever report

• 2 traps in Amazon’s own storefront reporting

• The one rule that decides every tile on your homepage

• The conversion killer I almost missed entirely

• The homepage layout map, top to bottom, with what belongs in each position

• The new beta report on select accounts that gives you click-through rate on every single module

• BONUS: A Claude prompt to run the whole audit on your own store

Read more

A couple months ago, I went full nerd and did something I’d been wanting to do for years. I used Claude to analyze 900+ PickFu split testing polls (now 1,038+) from 34K (now 38K+) respondents across the 850+ eCommerce brands we’ve worked with. The result was a database that lets me look up trends, spot patterns, and answer almost any question about a client’s creative before we even start designing. This has reframed how my team and I make every creative decision and I’m sharing all of it here in Design Proof. Twice a week: data-backed creative insights to help you convert better on Amazon and eCommerce.

Maybe it’s the cool kids, maybe it’s the smart kids but I’m starting to see this design trend hit every category. It likely started in the beauty space and now I see it spreading to various different categories like supplements and pet.

Call it clinical, or science-driven, or lab-coded. You know it the second you see it. It can be one or multiple of these elements in the design combined to create the effect of a data backed trustworthy product.

Elements like these:

• A big number sitting on its own in the hero.

• Precise decimals instead of round figures (45.68% vs 45%).

• A footnote citing the exact third party lab used for testing.

• Named actives with trademark symbols.

• Thin type fonts

• Restrained or minimal palette

• Lots of white space and breathing room in the design

It started in beauty, and that part makes sense. Beauty has been selling clinical credibility for decades, so they had the vocabulary first.

What I find genuinely interesting is that it isn’t staying there. It’s showing up in supplements. It’s showing up in pet.

And the brands adopting it are not copying beauty page-for-page, they’re adapting the aesthetic to their own category, sometimes in really smart ways.

Here are five product pages I pulled from Amazon, across three categories.

I also did the next obvious thing and ran the looks through our own testing data.

First, the data:

This post is about conversion, so everything below comes from product image stack tests and A+ content tests only. Nothing from main image tests.

That distinction matters for Amazon conversion :

• Your main image has one job, which is stealing the click out from your competitors in the search results. It’s a click-through lever.

• Your product image stack and your A+ content are what a shopper actually digests once they’re inside your listing. That’s where the “buy now” happens. That is your conversion lever.

An aesthetic decision is a conversion decision, because the look of the whole design is something a shopper only experiences after the click.

What we found: The clinical look is really two things bundled together.

The evidence works. The thin type font does not.

The clearest single example, and the one you should test tomorrow :

Things like a picture that shows what's inside the product. A bar sliced open so you can see the layers. A capsule opened up with the powder next to it. The supplement facts panel blown up big. In A+ content it won 50% (vs 30% baseline). In product image stacks, 38% (vs 27%).

Translation: This is a rare finding with enough confidence to advise that most clients just implement immediately. No pre-testing cautiously, just do it.

Every one of the five brands I analyzed has one. Enviromedica’s radial diagram naming four strains. Maev’s ingredient cards with a function line each. Depology’s four-quadrant peptide layout. They arrived at the same module independently, and our tests say they were right.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for, broken down so you can use it yourself.

Below the paywall:

• The signature clinical module that loses in four separate tests

• Why the big number works and the clinical badge next to it doesn’t

• What our own Zesty Paws tests show, across seven polls

• A prompt to run on your own page today

• The Science-Driven Design Scorecard

Read more

A couple months ago, I went full nerd and did something I’d been wanting to do for years. I used Claude to analyze 900+ PickFu split testing polls (now 1038+) from 34K (now 38K+) respondents across the 850+ eCommerce brands we’ve worked with. The result was a database that lets me look up trends, spot patterns, and answer almost any question about a client’s creative before we even start designing. This has reframed how my team and I make every creative decision and I’m sharing all of it here in Design Proof. Twice a week: data-backed creative insights to help you convert better on Amazon and eCommerce.

Last week, I wrote a guest piece for the Selling From The Beach newsletter about A+ content. The point I made there: your hero headline shouldn’t answer “what is this,” it should answer “why should I keep scrolling.”

That still applies. But that piece was just the teaser, today I’ll go deeper into the hero structure of successful A+ pages.

After reviewing a batch of our 88 verified A+ tests with more than 2,300 shopper responses here’s what the winning and losing hero section types taught us.

1. The information-first hero is a hard no. A+ pages that opened with specs, ingredients, comparison charts, or a feature rundown won just 3 times out of 29. Chance alone would have predicted a third, so this is the single worst-performing choice. It’s also the most common instinct, sharing the logic first feels like the right move. Tell customers what it is and immediately educate, makes complete sense. Here’s why it’s the wrong move. The data says you’re spending your best real estate on the thing they’ll read last.

2. Here’s the one that makes me cringe: the show stopping hero doesn’t win either. I assumed the lifestyle-led hero would crush the rest. It didn’t. Openings that were purely a lifestyle scene won about a third of the time, again this is exactly what chance predicts. But this part actually makes the most sense, a beautiful photo with nothing else doing any work is not a strategy. It’s just a beautiful photo and a huge missed opportunity to sell.

3. So what actually wins? Hybrid. A hero that combines a large lifestyle scene AND the product AND something that tells you what it does. This is the exact formula that won about half the time against the chance third rate. That combination is the best-performing opening in the entire sample set, and it’s the one I see least often, because most A+ pages commit to being either the pretty hero image or the informative hero.

4. Here’s the kicker: the scene has to be big. This one ties back to our signature design move that we’ve been doing for years. The double module hero. It’s basically two banners combined to make one extra large lifestyle hero. We scored how much this strategy actually won:

• A dominant, large scene: won about 47% of the time

• A small, supporting scene tucked in a corner: 28%

• No scene at all: 18%

Remember, a chance vote sits around 34% for all three. So a small lifestyle image performs much closer to having no scene at all. Half-committing to the double module hero scene gets you almost none of the benefit.

Reflecting back on the findings above, the structure makes a lot of sense. Think of it like a dinner party. You don’t open the door and start sharing the recipe and ingredients you are using. You invite them in, show them the dining room, then you tell them what’s cooking, and the details come up later once they actually want to go deeper.

Below: the biggest unlock for A+ content messaging with four real examples, and a prompt to run on your A+ content designs today.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for, broken down so you can use it yourself.

Below the paywall:

• The headline that won nearly two thirds of its tests, and why it’s the opposite of what wins on main images.

• Four real client pages, named, with what the winning opening actually did

• Why our single best-performing module is the worst thing to put first

• Exactly how your A+ should be structured top to bottom

• The Claude prompt to run on client A+ pages before you touch the layout

• The Above The Fold Scorecard: five things to score your own opening on

Read more

What we built, and why

For years our mini audit has been a manual thing. A strategist spends up to two days on one listing: research it, research the competitors, write up every section, record a 20 minute Loom video walking through it. It’s the single most effective thing we do to show a brand what’s actually wrong, and it doesn’t scale even slightly.

So we built the tool version of our mini audit. It scrapes a live listing, runs every image through Claude vision, tags what it sees in the exact same checklist we use in our buy now method, and then matches the listing against our library of more than 1,000 real shopper split tests from 36,000 respondents.

That last part is the whole point. Most listing graders are superficial at best, they check whether you have six images and a keyword in your title.

Ours asks a different question: does this listing look like the ones that actually won when we put them in front of real shoppers?

The buy now score it generates, come from code so the same listing scores the same way every time. The judgment on design quality comes from vision, held against the standard we hold our own client work to. And it’s built to be honest rather than flattering, which means it will tell you your listing is a 49.

I know that, because I ran it on my dog’s favorite brand, Roverlund. They have an impressive Shopify site but their Amazon listing falls short on almost every metric that matters.

The listing

I’ve been using the same dog carrier for about five years. Bought it when I moved to Peru and needed to fly my dog back to the States. Paid somewhere around $200 to $300, then paid again to ship it to Lima. My pup, Piggy Smalls, uses it regularly. It’s basically, his car seat now.

The brand’s own website is genuinely lovely, the photography hooked me instantly. Media mentions, reviews, UGC, real Instagram presence, award-winning. All the components we use to build strong Amazon listings. Their listing also has 4.5 stars and 768 reviews at $174.99, that’s a strong start.

It scored 49 out of 100. Translation: Working Against You.

And then I did the thing that actually stung. I put its closest lookalike on the shelf beside it, the cheaper near-identical dupe that shows up in the same search.

The lookalike also scored 49.

Same score. Roverlund is the original, award-winning, five years of loyal customers. The other is a copycat aka dupe. On the page where the buying decision happens, a shopper has no way to tell them apart, and our grader couldn’t either.

The single biggest reason: the copy has A+ content. Roverlund doesn’t.

The part I didn’t expect

Here’s what I assumed I’d find and what the tool actually found.

I assumed the copy would be the weak link, because that’s what usually happens when a DTC brand lands on Amazon. Great design instincts, no idea how Amazon search works.

Their words scored 84. That’s the best thing on the listing by a distance. The title leads with airline compliance, names the included leash, states the 20lb limit. The bullets are benefit-led with real specifics. Somebody who knew what they were doing wrote those.

Their images scored 19.

That’s the inverse of what founders expect, and it’s the most common pattern I see. The brand that obsesses over design on its own site ships the least designed thing on Amazon, because the two platforms reward opposite instincts and nobody tells you that.

Our goal for client work is 75 and up. That’s where the top content in a category lives, and we treat it as the starting line, not the finish.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for, broken down so you can use it yourself.

Below the paywall:

• The full rebuild in the order I’d do it, with what each fix is worth in points

• Where our own tool and our own shopper panel flatly disagreed about one image, and which one I think is right

• The review that shows a happy customer who almost didn’t buy

• A feature this listing has that a top review publicly says it doesn’t have

• The search real estate, two words missing from the title

• How to get us to run this on your listing

Read more

Last month, I went full nerd and did something I’d been wanting to do for years. I used Claude to analyze 900+ PickFu split testing polls (now 991) from 34,000 respondents across the 850+ eCommerce brands we’ve worked with. The result was a database that lets me look up trends, spot patterns, and answer almost any question about a client’s creative before we even start designing. This has reframed how my team and I make every creative decision and I’m sharing all of it here in Design Proof. Twice a week: data-backed creative insights to help you convert better on Amazon and eCommerce.

I posted this on LinkedIn yesterday and it’s already at almost 10,000 views in under 24 hours, so clearly I’m not the only one who thinks this is a big deal.

Here’s what happened. Bradley Sutton from Helium10 DM’d me to give me the heads up on a new storefront feature.

I immediately ran to Seller Central, opened one of my a client’s storefront and noticed a new button: “Personalized Experiences” Two options sat under it.

Personalize with AI is positioned as a builder, help you customize experiences for different audience segments. Love the concept, testing layouts for targeted segments is ideal. Unfortunately, I immediately hated the execution. When I ran my test to create two “experiences”, it didn’t actually design anything. It just dropped a bunch of empty modules into the page, which honestly created more work for me than it saved. Maybe it gets better with more use, maybe I’m missing something. For now, I’m skipping but I’ve bookmarked it for later in case it’s a gem in disguise.

Analyze Performance is the one that stopped me cold.

I one-shoted a prompt that turned out to be pure gold:

And BINGO, it came back with this:

A full breakdown, feature by feature, of what was already working on the store and what to improve with data to back it up.

Pro tip: Run that exact prompt in your own Storefront right now. Takes two minutes and costs nothing.

Seller Central → Storefront → AI Builder, bottom right corner → Analyze Performance.

Here’s why this is such a big deal.

I’ve been doing this 8+ years. The only other time I’ve seen Amazon publish numbers like this was inside their own Creative Certification program, shown to a small group of certified agencies, never public. Now it’s sitting inside a free builder tool any seller can open.

Amazon handing brands their own benchmark data, unprompted, inside their own tool, is a real shift. It means brands (and agencies) can finally see how their storefront stacks up against peers instead of guessing.

Also worth noting, Amazon is the first to tell you that their recs are not a guarantee but they are a signal worth testing.

My hope is that this is just the beginning of some new and improved tools for Storefront. Storefronts have been lagging on functionality and testing capabilities for some time and this is a step in the right direction.

How we intend to use this with clients is to roll out our designs as we normally to and then use our Amazon’s recs as a test in our phase two after collecting baseline data. Most of Amazon’s recs didn’t even require additional design but I’m sure they will perform better with custom design as we have seen with our client results.

🔒 Now Let’s Get To The Good Stuff

I ran the tool a lot further than that one screenshot. I asked it 20+ follow-up questions to find where it’s actually useful and where it’s just a wall of “refer to the Stores Help documentation.

Below the paywall:

• Three prompts that are actually worth stealing and testing today.

• What the tool is best used for and what it’s not.
  
  Subscribe to unlock.

Read more

Last month, I went full nerd and did something I’d been wanting to do for years. I used Claude to analyze 900+ PickFu split testing polls (now 991) from 34,000 respondents across the 850+ eCommerce brands we’ve worked with. The result was a database that lets me look up trends, spot patterns, and answer almost any question about a client’s creative before we even start designing. This has reframed how my team and I make every creative decision and I’m sharing all of it here in Design Proof. Twice a week: data-backed creative insights to help you convert better on Amazon and eCommerce.

I’m doing something a little different this week. Instead of me digging through our own data, I’m handing the mic to someone else first.

Max Sigurdson-Scott left a decade of teaching to sell on Amazon full time, then spent a full year posting a new growth hack on LinkedIn every single day. Now he’s living abroad on the beach, like me. And he appropriately named his newsletter “Selling From the Beach”, as he writes from Costa Rica helping Amazon sellers who want the tactical stuff, not the fluff.

A few weeks ago he posted something that stopped me mid-scroll, about a packaging mistake I see constantly and had never actually put words to.

Here’s Max:

Here’s why using ALL CAPS in your images & packaging sucks:

When we read, we don’t go letter by letter. Our brains recognize word shapes. These shapes are made by a mix of tall letters like b and h, short ones like a and e, and letters that drop below the line like g and y.

That’s how reading stays fast. We’re pattern matching.

But when you type in ALL CAPS, every letter becomes the same height. You lose the shape. You flatten the word into a block. Every word becomes a rectangle. No visual contrast. No cues for the brain to scan.

(As an aside, I had a science teacher in high school who wrote notes on an overhead projector ALL IN CAPS ALL THE TIME. I guess that’s why it was so hard for me to concentrate? 😅)

...Anyway... So what happens?

• Reading slows down

• Understanding drops

• Mental effort goes up

And your conversion rate takes a hit.

This isn’t just an aesthetic choice. It’s backed by research. Reading all-caps slows you down by up to 20 percent. It increases fatigue. It reduces comprehension. Especially on mobile, where shoppers are scrolling at speed.

If your key info is buried in a hard-to-read wall of text, no one’s reading it. They’re skipping you.

I just posted three examples of this. Great products. Great ideas. But the text design is killing their click-through rate. If you’re doing this on your own packaging or image overlays, fix it now.

Use sentence case. Use line breaks. Make it scannable.

Max’s point isn’t a style preference, it’s a readability mechanism, so it’s testable.

Have a design with all-caps callouts? Mock up a sentence-case version with the same words. If it’s a headline or callout on a main image or A+ module, that’s a clean PickFu pre-test. If it’s in your title or bullets, that’s an Amazon Manage Your Experiments test.

That got me curious enough to go check our own numbers on readability. Here’s what we actually found:

Max’s mechanism is real. Word shape is genuinely one of the ways your brain decides how fast something is to read. I should also add that I’m not against using ALL CAPS in design. In fact, we use caps quite a bit, mostly for headlines or where emphasis is needed. But it’s one input into something bigger, and we’ve tested main images, product images and A+ content enough times now to know readability isn’t one trick.

It’s actually five things stacking on top of each other, and most brands are only doing one of them…

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• The five things that actually drive packaging readability in our own testing (with the real effect sizes behind each)

• The one place bigger text alone won’t save you

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A founder I follow just posted his packaging before-and-after, and it’s one of the cleanest examples of “leveling up” I’ve seen in a while. The brand is Boostcous, a gluten-free, protein-packed couscous. (Great name. I found them on TikTok months ago through the founder’s storytelling series, where he shared the misses alongside the wins, and I’ve been rooting for them since.) He put the old box next to the new one and asked LinkedIn what they thought.

Here’s the detail that makes this perfect. The founder, Bar, said the old packaging sold over 150,000 boxes, so it clearly worked for their launch and validation period.

But he also mentioned that a couple of people thought his couscous boxes were an alternative milk - that’s a packaging fail. The box was cute, and it didn’t tell you what was actually inside.

Now look at what the refresh did:

• “Protein Couscous” went right up top, so you know what it is immediately.

• The protein number got made huge. “18g protein, 11g fiber” now reads instantly, where before it was tucked into small badges near the bottom.

• The gluten-free certification moved to the front of the pack.

• The word count came way down, and somehow you can read *more* of what matters, because the important things got bigger instead of competing with everything else.

• Biggest of all: they put a big spoonful of the actual cooked couscous right on the box.

That last one is the whole point of this post. Bar’s own words for it: “Actual product on the box (crazy concept, I know).”

On a physical store shelf, that spoon shot helps. On the digital shelf, it’s EVERYTHING. Its the literal difference between getting the click and losing it.

Here’s why: online, a shopper can’t pick up your box, can’t read the fine print, and definitely can’t taste your product. So the next best thing, the only thing that answers “what even is this?” is *seeing* it.

If your packaging doesn’t show the product, and the competitor next to you in search does, most shoppers are going to click the one they can actually see.

Protip: showing the product is the bare minimum imo, but one step better is making it drool-worthy. If customer can’t taste it or smell it, make them drool when they look at it.

We’ve tested this exactly, for hundreds of CPG food and beverage brands. One worth mentioning for a snack bar brand called Yes Bar, we ran the version showing the unwrapped bars against a version that only showed packaging. Showing the actual product won with 77% of the vote. Not a close race.

In that category, hiding the product is basically disqualifying. The bright color helps too. Boostcous kept a bold blue that’s going to pop off Amazon’s required white background instead of getting lost in the grid. But the color is the assist.

The product shot is the goal. So if you’re a founder staring at your own packaging right now, the first question isn’t “is it pretty.” It’s “can someone tell what this is, and see the actual product, from a thumbnail on their phone.” If the answer is no, that’s the first fix, before you spend a dollar on anything fancier.

Which brings me to who I send founders to when they’re earlier in this journey than we can help with…

If you make a packaged product (food, bev, pet, wellness, beauty, or home goods), it’s worth a look. Join by Tuesday, July 21

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There’s a bigger lesson underneath the Boostcous refresh, and it’s the thing I actually want you to take away.

Great packaging is almost never the first design a founder makes. It’s the third, or the fifth.

What you’re looking at in that before-and-after isn’t “bad vs good.” It’s “good enough” growing into “great.” And knowing the difference between those two stages, and which one you actually need right now, is one of the most useful things a founder can learn.

I’ll break that down below: the three moments brands come to us to level up (and how to tell which one you’re in), the 80/20 rule I use to decide when to fix packaging versus when to just get to market, and the exact short list I give a brand before they invest in creative.

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Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

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• The step-ladder framework for iterating your brand over time

• The three moments brands come to us to level up (ezmilk, Yes Bar, and Tosi

• The real tests and before-and-afters behind them, including the packaging that got rejected by 92% of shoppers)

• BONUS: a copy-paste Claude prompt that scores whether your packaging shows the product.
  
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It’s time for another Tuesday split testing insight, but before I get there, I want to tell you about something I built over the weekend.

I had a deep session with the AI I lovingly refer to as Claudette (if you’ve been reading a while, you’ve heard me talk about her before). And yes, she’s a her, because she can multi-task ;-)

I got access to a much more powerful version of Claude, free within my usage plan, and I burned through that allowance fast. By the time I was done, I’d spent over $1,000 building two different tools for the business. I truly believe it was money well spent, down to the last penny.

I’ll tell you about one of them today and save the other for another post, but this one ties directly into everything this newsletter is built on. It takes our whole testing library and turns it into a “God view” dashboard for my team. Anyone on the team can pull up a poll, a category, a pattern, at any time. We can filter it, slice it, question it, and run our own designs against it. We’re using it to predict what we should be testing next, put together client reports with real data behind them instead of piecing it together by hand, and dig into our own designs against everything we’ve ever tested.

It also just quietly surfaces trends to the team, the kind of pattern one of us might notice and never think to mention to anyone else. Now it’s sitting in one place everyone can see.

I was in that dashboard this weekend when something caught my eye: gender divergence.

Let’s start here: A kids’ shampoo brand about to launch, ran a main image poll and got a winner. On the surface, it’s exactly the kind of result you close out and ship without a second thought.

Then we split the vote by gender. The winning image pulled 50% of male respondents. Among women, it pulled 24%. Kids’ personal care is a category where the primary buyer is female by a wide margin, a category-level estimate we use as a starting point when we don’t have the brand’s actual Seller Central data, not a hard number for this specific brand. Even using that as a rough guide, the image that won the poll was the image the minority of the panel actually preferred.

Nobody sets out to build a shampoo strategy around what men think of it. It happens quietly, buried in a table most people scroll past on the way to the headline number. With this particular client, this mattered because the we realized her packaging wasn’t resonating with the audience she needed to attract. She was able to rebrand before launching because of this catch, saving her from lost serp clicks.

But here’s where this actually matters even more, especially once you’re an 8 or 9-figure brand. At that size, your creative isn’t a rough sketch anymore, it’s built to speak to a specific audience. If your buyer skews toward one gender and you’re not testing against that specific audience, any new designs your launch to Amazon out your conversion at risk.

Compare that to a snack bar brand that ran a similar test. 74 respondents, a clear winner, 69% overall. When I split that one by gender, the winning image pulled 89% of women and 48% of men, a 41-point gap. For a snack bar brand with a predominantly female buyer, the overall winner and the right answer for the primary buyer were the same image. Same size gap, completely different story. That one was fine to ship as-is.

Two polls, two gender splits of similar size. The difference was never the size of the gap. It was whether the group actually driving the overall win was the group that buys the product. That is what your should be testing for.

I went back through our full library and pulled every poll with a gender divergence above 20 points. There are 85 of them, roughly 1 in 11 of the 932 tests we’ve run. Most confirm the obvious and point the same direction as the overall winner. But a meaningful chunk show the thing that should stop you: the overall winner was set by the segment least likely to actually buy the product.

The divergence isn’t random, either. It clusters in categories where the buyer skews hard one way: Pet Supplies (13 polls), Grocery (10), Supplements (10), Kitchen & Dining (8), Tools (8). Those are exactly the shelves where “who showed up in the panel” and “who actually buys” can quietly come apart.

Below I walk through six of these polls in detail, including a notebook brand where the same pattern showed up in the opposite direction (male respondents controlling the win in a category that skews heavily female), and a full product-image-stack test where the split was sharp enough to flip the winner entirely.

Here’s the mechanism to watch. A PickFu poll aggregates preference across every respondent. The overall winner is what the majority of whoever showed up preferred. That’s only your answer if the people who showed up are a reasonable stand-in for the people who actually buy. When gender preference diverges significantly and your buyer skews heavily toward one segment, the headline result can tell you exactly the wrong thing.

One honest caveat before you take this as gospel: this is an audit habit, not a law. A 30-person panel split by gender leaves you with maybe 15 people per segment, so a jump from 24% to 31% is really just two or three people changing their vote. I haven’t isolated every detail here, and I’m not claiming the exact percentage is precise down to the point. What I am confident in is the pattern: when the gap is wide and the segments disagree on the winner, it is worth two minutes to check who was actually driving it.

Most brands never check this. When the winner is obvious, the gender breakdown is the data section most people scroll past.

Pull up your most recent PickFu poll right now. If there’s a gender breakdown visible in the results page, check whether the overall winner also won with the gender that makes up the larger share of your buyers. If yes, you’re done. That result is clean. If the winner was set by the minority segment, that’s worth understanding before you ship.

The paid section covers exactly how to read it.

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Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• A detailed breakdown of six of these high-divergence polls with what drove each split

• Whether the poll type (main image, A+ content, or full image stack) changes the odds of this happening

• How to find your buyer’s gender distribution in Seller Central - A two-step check that takes about two minutes and tells you whether your next poll result actually applies to your buyer.
  
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Certifications are the most talked-about thing in supplement creative. Across 140 supplement split tests we’ve run on Amazon (4,600+ real Prime shoppers, 58+ brands), shoppers brought up clinical proof, third-party testing, or certification badges in 36% of polls. That’s 2.2x the rate of every other category we test.

So you’d assume the badge is the thing that wins the sale. Put the certification front and center, win the click.

The data says the opposite.

When certification talk dominates a supplement test, the result is basically a coin flip. Only 44% of those polls produced a decisive winner (17 of 39). That’s the worst decisiveness rate of any category we track. For comparison, grocery polls are decisive 90% of the time. When everyone’s arguing about badges, nobody wins.

Here’s the part that flips it. When the winning image itself carries a certification badge, it wins decisively 79% of the time (27 of 34).

Read those two numbers together, because that’s the whole insight:

• Tests about certifications: 44% decisive (a wash).

• Winners that have a badge: 79% decisive.

The badge isn’t what wins. It’s the expectation. Miss it and you lose customers. Have it, and you’re exactly like everyone else who has it.

Real shoppers say it plainly:

“This ad layout looks the most professional with clinical information that would be ideal to talk to a doctor or pharmacist about efficacy.” (Female, 55-64)

“Option C, the USDA organic label can clearly be seen. Peace of mind knowing product does not contain harsh chemicals.” (Male, 35-44)

Notice what they’re describing: relief that the badge is present. Not excitement, not a reason to choose you over the other bottle that also has one.

In grocery, a certification differentiates you. In supplements, it’s the entry fee.
And if every pays the same entrance fee, it all becomes a wash.

Here’s where I think most brands leave money on the table. They earn the certification, they place the badge, and then they stop. As if the icon does the talking.

It doesn’t.

The average shopper does not know what most of these certifications actually mean. They don’t know the testing standards behind them, they don’t know which ones are hard to earn versus easy to slap on, and they definitely don’t know that your process might be more rigorous than the brand sitting right next to you on the shelf. They glance at the badge and feel a small, vague sense of “okay, this seems legit.” That’s it. That’s all a badge on its own buys you.

Think of it like a diploma hanging on a wall. It tells someone you’re qualified, but it doesn’t tell them what you actually learned or why you’re better at the job than the person next to you. The badge is the diploma. The story behind it is what actually earns trust.

So if you hold yourself to a higher standard, say it. If you test for things other brands skip, say that too. If your certification is one that very few brands in your category have managed to earn, that is not a detail to bury. This is exactly the stuff people care about when they’re putting something into their body. They want to feel safe, and safety is a feeling you create with specifics, not symbols.

In our experience, badge-versus-badge tests end in ties, and brands keep spending their most valuable creative real estate on the one element that no longer moves anyone.

Which raises the real question. If the badge is table stakes, what actually wins the supplement shopper?

We know, because we measured it. There are three levers that separate the decisive winners from the coin-flips, and none of them is the thing most brands are optimizing.

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Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• The three creative levers that actually decide supplement tests (with the exact sample sizes behind each)

• Why “clean and minimal” is backwards in this category

• More unblurred winning images

• The supplement main image decision matrix

• Bonus: Claude prompt to score your own main image against the pattern
  
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A few weeks ago we ran an experiment inside the agency. We wanted to know if we could use AI to generate the first concepts for a client’s A+ content (the detailed image sections further down an Amazon listing) the same way we already use it for main images. Main images have been a real win for us with AI. So we assumed A+ would follow.

It didn’t. And I want to walk through why, because our honest experience contradicts almost everything you’re seeing about AI design on LinkedIn and socials right now.

Here’s what we ran into. To get an A+ concept out of AI, a skilled human with design training still has to sit there and prompt it, round after round, guiding every version.

This means the entire time that person is prompting, they could just be designing the thing instead. The AI didn’t save the work, it just turned designing into prompting.

And the burn is that the versions that came out of AI in “1-click” weren’t even close to the quality of what our team ships. Interesting to look at, occasionally useful for sparking an idea, but never something I’d put in front of a client as a finished direction.

When you show a 7-figure brand an AI concept and they’re expecting the quality they hired you for, you’ve set the wrong first impression before the project even starts. We’d spend the call explaining that the AI versions were for concepting, which is just not the best use of anyone’s time.

This is where I want to talk about the thing everyone’s posting. You’ve seen it:

And sometimes the image genuinely looks great BUT here’s the part that they left out. That famous brand already spent tens of thousands, sometimes millions, building their creative over years, and all of that work is online. When AI makes something that looks on-brand for them, it’s remixing an asset library someone else already paid a fortune to build.

Now hand that same AI image to that brand’s actual design team. They’ll come back with twenty edits before it’s allowed anywhere near a live listing. And here’s what the demo videos never show you: you go to make those twenty edits by prompting, and every time you fix one thing, the AI changes something else you didn’t ask it to touch. You end up re-checking the entire image after every round to make sure it didn’t quietly break. It becomes a longer process than building the design properly in the first place.

Here’s a story that made this real for me.

I had two clients around the same time. One in the kids category. One in home goods. Completely different products, completely different customers, nothing in common except that they’re both Amazon sellers.

At some point in the process, both of them went off and made their own images in ChatGPT. Neither one knew the other existed. And when they came back to me with what they’d made, the images looked almost identical to each other. Same lighting. Same composition instincts. Same overall feel and colors, even though a five-year-old and a fifty-year-old were supposed to be looking at these two listings.

Think of it like this: if two strangers walked into two different stores, in two different cities, shopping for two different things, and came out wearing the same outfit, you’d assume they shopped at the same place. That’s what’s happening with AI-generated creative right now. Different brands, different categories, same store.

Since then I’ve started noticing it everywhere, I can’t unsee it. I’ll scroll LinkedIn and see creative from brands I’ve never worked with, in categories I don’t touch, and I can tell within a second that it came out of the same tool with the same defaults and even the same fake AI models. The aesthetic is becoming a tell.

My prediction: this gets worse before it gets better. More brands are going to lean on AI to move fast, and more of them are going to end up looking like slightly different flavors of the same brand. On Amazon, where you’ve got half a second to earn a click, that’s the opposite of what you want. You’re not trying to look like everyone else’s AI output. You’re trying to look like the one option a shopper actually stops for.

In our own testing, these one-click tools get it right maybe 70 to 80 percent of the time. That sounds high until you remember the last 20 percent is exactly where brand consistency lives, the part a shopper actually notices.

And it’s not just us seeing this. Adobe surveyed more than 16,000 creators in 2026: 57 percent said their AI outputs need moderate or extensive editing before they’re ready to share, and 81 percent said human judgment is still what decides whether something is good enough to publish.

But the cost I care about most isn’t the wasted time. It’s trust. And on Amazon, trust is the entire game. A shopper is deciding in about two seconds whether your product is real, whether it’ll show up looking like the picture, whether they believe you. And AI fatigue is real.

We tag every poll we run for the reasons shoppers give us, and one of those tags fires whenever someone, completely unprompted, says an image looks fake or AI-made.

This year alone, that happened in 13 separate tests, across beauty, supplements, pet, home, and baby. Nobody asked these shoppers about AI.

They brought it up on their own. It isn’t new either, though the vocabulary is. Here’s a sample of what real people have typed into these tests, in their own words:

“B doesn’t even look like a real product. It looks like someone said ‘AI generate me a sheet set with a box.’” (Female, 35-44, bed sheets)

“All the other ones look AI. I would want legitimate items.” (Female, baby product)

“B looks really fake. All the other candles are actual pictures of the candle, and I trust that a lot more.” (Female, 45-54, candles)

“Please no more of the fake hands. They just make products look cheap and poorly made.” (Female, 25-34, nail care)

“Looks fake, like an infomercial. It makes me feel weird to look at her photo as part of a product meant for my sensitive skin areas.” (Female, 35-44, skincare)

One of those lines, the baby-product one, came from an A+ content test. The same kind of content I just told you we couldn’t get AI to do well on our own.

Look at the candle shopper for a second. She spelled out exactly why she landed where she did: the real photos earned her trust, so that’s where she went. That’s the point to pay attention to.

When a shopper types this kind of thing into a test, they’re telling you something simple and expensive: they don’t quite believe the product is real. On Amazon, that doubt is the gap between a click and a scroll. It’s the same gap that many brands are competing for.

Here’s where this gets expensive in a way most brands never see coming. Plenty of full-service Amazon agencies charge a $5,000 to $10,000 monthly retainer and promise they handle all of it: your ads, your logistics, your account, and your creative.

In my experience, creative is usually the weakest thing most agencies do. For years it was one freelance designer taking odd jobs with no real strategy behind it.

Now I’m watching those same agencies quietly hand that freelancer a one-click AI tool and keep charging the same premium. So you’re paying top dollar and getting AI slop on your listings, and the trust cost lands on you without anyone flagging it.

Whether that actually hurts you depends on what kind of brand you are. If your creative was already weak, or you’re selling a pure commodity, the AI version might even test a little better.

But if you’re building a real brand, with a catalog people are starting to know and trust, this is the exact blind spot that quietly bleeds you. The shopper who calls an image fake in a test is the same shopper deciding whether to trust your whole brand.

To be fair, one-click AI genuinely makes sense at the very start or even for prototyping and testing. If you’re brand new with almost no budget, testing whether a product even has legs, AI images are probably fine, and you can likely make them yourself.

It’s once you’re an established brand with a design system and years of data behind your decisions that good enough starts costing real money. At that point your team needs to open a working file, make a precise change, and move on, not negotiate with a tool that reinvents the layout every time they nudge it.

Quick thing you can do right now: pull up your main image and your most recent A+ section and look at them the way a stranger would in two seconds. Does anything read as too perfect, too plasticky, too generated? If it does, that’s a trust leak, and the good news is it’s testable.

IMPORTANT NOTE:
One more thing that recently caught me off-gaurd and turned all of this from an opinion into a risk decision for our clients. As of June 9, a new New York law requires a visible disclosure on any ad that uses an AI-generated human built to look like a real person.

Your Amazon listing reaches shoppers everywhere, New York included, so “that’s just one state” doesn’t get you out of it. And Amazon now asks, on every image you upload, whether it contains AI or AI-generated people.

The Risk: Nobody knows yet what they’ll do with that answer: a disclaimer stamped on your creative, reduced visibility for the listing, or nothing at all. Human-led design with a few AI-augmented pieces, rather than fully AI-generated people, keeps you on the safe side of all of it while the rules settle.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• The four specific ways we do use AI successfully every week (including the one I think is genuinely the future for large catalogs)

• The exact workflow we use to turn a winning design into a fresh split test in 15 minutes instead of three hours

• Bonus: Claude prompt you can run on your own listing to catch the AI tells before your shoppers do
  
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Let’s show you how a little “Merica” can sway consumer opinion at point of purchase. Nineteen of thirty shoppers picked the same seat cushion image. That’s a 63% win in a five-way test, which almost never happens. Five options, and one pulled six times the votes of the runner-up.

The winning image had three things the others didn’t: color swatches, a weight-capacity badge, and a small “US OWNED” flag badge in the top corner. When we read the comments to find out why it won, roughly a third of the panel brought up that flag on their own. Nobody prompted them. They just kept mentioning it. (Honestly, I didn’t expect the flag to be the thing people circled back to.)

Most brands treat the American flag as a patriotism play, a little pride sticker for shoppers who “buy American.” But that’s not what shoppers are actually doing with it. Read the reasons they gave and the flag comes up constantly as a fast read on quality and safety, and almost never as a pledge of allegiance.

What was tested
This was a ranked main image test for a portable inflatable seat cushion, run to thirty US Amazon Prime shoppers in the Fitness & Outdoor category. Five image variations, all showing the same product, differing in angle, badges, and on-image information. Respondents ranked all five and explained their reasoning in their own words.

What was found
The winning image took 63% of first-choice votes (19 of 30). The origin badge came up again and again in the explanations, and always as a reason to buy, not just a nice-to-have:

“The image that shows that the seat cushion is US owned makes me want to make this purchase to support businesses in the US.” (Female, 25-34)

“I like seeing the made in USA so big and center. That is important to me.” (Male, 35-44)

“US owned... ensures me that the company is US owned.” (Male, 45-54)

Notice what’s missing: nobody mentions pride or patriotism. They talk about trust. One shopper put it plainly, the flag “signals quality and safety.”

Why it happened (the short version)

On Amazon, a shopper is making a snap judgment with almost no information.

They can’t touch the product, can’t ask a question, can’t tell a real brand from a drop-shipper. So they hunt for shortcuts. Anything that answers “can I trust this?” without making them read.

Think of the flag badge like a firm handshake. It tells you nothing factual about the person, but it registers in a half-second as “this one’s probably legit.”

That’s the job the flag is quietly doing here. It stands in for “a real company is behind this, and it wasn’t slapped together overseas and forwarded to me.” That is also the reason it can blow up in your face.

Before you keep reading, open your own main image right now and ask what a first-time shopper still can’t tell about you. If “is this even a real company” is anywhere on that list, this one’s for you.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• The unblurred test images

• The one comment that should make you nervous (a shopper ranked the flag option dead last and called it “try hard”

• The four categories where the origin badge does the most work

• The framing that turns it from a trust cue into an insult

• Bonus: step-by-step framework and the Claude prompt to score your own main image for trust signals
  
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Here’s the whole Native Pet main image playbook, with our own data behind it.

Let’s start from the beginning…Everyone fixated on the badge.

If you were anywhere near LinkedIn during Prime Day, you saw it. Native Pet ran a “Selling fast” badge on their main images that looked a little too close to an official Amazon label.

Amazon experts and sellers alike were triggered. The whole conversation went straight to ethics and compliance, and honestly, fair. That badge was risky, and Amazon probably should have caught it.

Would I recommend it to my clients, no. But that’s not the point.

While the LinkedIn Amazon gurus argued about the risky badge, my attention was on something else entirely.

Native Pet was quietly split testing five packaging edits in real time. For me, the packaging details and variants were the actual story.

Here’s what almost nobody mentioned. Every one of Native Pet’s main images had been redesigned. The packaging in those photos doesn’t match the bags they’re selling on their own website right now.

And they didn’t make just one change. They made FIVE. All at once, on the highest-traffic shopping day of the year.

Here’s what I counted:

1. The product name got bigger and the brand name got smaller. On the real bag, “native pet” leads. In the images, the product (”Clean Breath,” “Itchy Skin”) leads.

2. They added the words people actually search. “Itchy skin.” “Trim down.” “Clean breath.” Written big enough to read at thumbnail size. None of that copy is on the actual packaging.

3. A different beloved dog on every image. A golden retriever on one, a French bulldog on another. Different cherished breeds, sizes, and ages. That’s not decoration, that’s matching the image to whoever’s shopping.

4. A scoop of the actual food in frame. So you can see the kibble or powder before you click, instead of guessing what’s inside the bag.

5. The badge, worded and placed differently on each one. Even the risky badge everyone argued about was part of the test but again I was focused on the circular one in this case.

Think of it like a restaurant testing five versions of the same dish on five different tables on its busiest night. By the time the kitchen closes, they know which one earns a spot on the menu. Native Pet turned Prime Day into a test kitchen.

Now here’s the part I really want you to sit with, because it’s why most brands never try this. There are two fears in the way, and both of them are beatable.

The first fear: the image has to match exactly what’s in the box. So sellers shoot the bag as-is and move on. But think about where that image actually lives.

It’s a thumbnail the size of a postage stamp, sitting in a grid next to twenty competitors, and a shopper is giving it about half a second before deciding whether to click.

The bag on your shelf has all the time in the world to win someone over. The thumbnail has half a second. So when Native Pet blew up the product name, added “itchy skin” in big readable type, and dropped a scoop of food into frame, they were answering the three questions a shopper races through in that half-second:

1. What is this?

2. Will it fix my problem?

3. What do I actually get?

The second fear: the one that actually keeps people frozen is the belief that if your image doesn’t match Amazon’s design guidelines to the letter, you’ll get suspended or kicked off the platform.

I’ve watched that rumor stop good brands cold. So let me say this clearly, as someone whose team has done this across more than 850+ eCommerce brands and tens of thousands of SKUs: guidelines are guidelines. They are not suspension rules.

It is okay to color a little outside the lines on your main image to win click share from the competitor sitting right next to you. The brands winning the category are doing exactly that.

And this isn't some scrappy brand rolling the dice. Native Pet does over $45 million a year on Amazon (according to SmartScout data 6/30). This is how the most sophisticated operators behave: they treat the main image as a lever to keep pulling, not a box to check once.

One thing I want to be honest about, though, because it matters. Native Pet didn’t win on edits alone. Their packaging was already beautiful. Bright, modern, the kind of direct-to-consumer look that pops on a both digital and retail shelves. The edits amplified a strong foundation.

If your packaging is flat to begin with, no amount of image work saves it. So the real lesson is two parts: invest in packaging worth photographing, then get obsessive about how you photograph and apply post-edits.

This isn’t theory for us. We ran this exact playbook for a premium toothpaste brand recently. Tested a few main image directions, found a winner, ran it live on Amazon. Click-through rate went up 167.5%. Same product, same price, same traffic. The only thing that changed was the image.

Quick thing you can do right now: pull up your own main image and shrink it to thumbnail size on your phone. In half a second, can you tell what the product is, who it’s for, and what problem it solves? If you have to squint, that’s where your work is.

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Below the paywall:

• The full toothpaste teardown showing the variant that won and the 167.5% live CTR result

• 5 of our own split tests breaking down exactly which image elements move click-through (with the real vote splits and shopper quotes)

• The safe way to upload an edited image to Amazon without the suspension risk

• Our Six-Lever Main Image Framework

• Bonus: the Claude prompt to audit your own main image
  
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Today let’s talk about these 6 key shopping moments we’ve been obsessing over this week.

You’ve probably hear of buyer personas. Typically it sounds something like, “Value-conscious millennial, 32-38, household income $60-90k, cares about clean ingredients, shops Amazon at least once a week.” It’s basically a demographic and psychographic composite. Useful for targeting ads and sometimes useful for knowing what to put in a product image.

More actionable are actual archetypes. These map to behavior, not demographics: the Researcher who reads every ingredient, the Impulse Buyer who makes snap decisions on aesthetics. Archetypes describe how someone tends to shop. That helps us get closer with our creative choices but they still treat the shopper as a fixed type, a consistent pattern you can design for across every purchase they make.

Shopping moments are different discovery entirely. A shopping moment isn’t a trait. It’s a filter. The mental state and criteria someone brings to this specific purchase, right now, in this category, at this price point. And the thing that caught my attention: the same person is in a completely different shopping moment for every purchase they make.

Think of it like this: someone spending $12 on a candle is on autopilot. The same person spending $70 on a supplement for a real health concern is checking everything.

Same person. Same demographic profile. Completely different filter.

She evaluates vitamins like a researcher, dose by dose, and selects throw pillows based almost entirely on whether the color feels right. That’s the reality of how purchase decisions actually work.

What shifts is the filter: category familiarity, price point, personal stakes, buying context. Someone buying for themselves filters differently than someone buying for a child. The mode changes with the purchase, not the person.

This is why personas don’t predict what wins in creative testing. A 35-year-old mom isn’t one type of shopper. She’s operating in a different shopping moment for every listing she lands on. Your image has to meet her in the one she’s in when she gets there.

What specifically made me take this seriously: I kept seeing the same demographic profile vote in completely opposite directions by category. Dense, information-heavy images beating warm lifestyle images in supplements. Warm lifestyle images winning decisively in home goods. Archetypes couldn’t explain it. Shopping moments could. If the filter changes with the purchase, the image that earns the yes changes with it.

The images that won by the widest margins weren’t optimized for one shopping moment. They were decent for several.

And the images that lost, or barely won, were usually beautifully optimized for one shopping moment while quietly missing everyone else.

That’s the whole thing, honestly. But to actually use it, you need to know which six shopping moments show up most consistently, and what each one needs from an image.

Six patterns emerged from the data. Not seven. Not twelve. Six.

They’ve shown up across every category we’ve tested: supplements, beauty, grocery, pet care, home goods, tools, personal care.

One note on the percentage ranges: they’re frequency estimates from verbatim comment analysis, not a formal demographic survey. They reflect how often each decision pattern appeared in consumer language across the dataset, not a census of who is on Amazon.

What they tell you is which shopping moments dominate in a given category, and which ones your current creative is actually reaching.

Here are the six shopping moments, what activates them, and what the data behind each one shows.

The Analyst (roughly 20-25% of decision language in the dataset)

This shopper comes in with a checklist, and they’re working through it before they commit to anything.

Their job, as they see it, is to find the option that already answers their questions. For supplements: what’s the benefit, what’s the dose, what’s the form? For food: what flavor, what portion size, what signals clean ingredients? For tools: what does it do, what size, will it fit?

When those questions go unanswered, Analysts don’t click through to find out. They find the listing that already answered, and they keep moving.

Information completeness is the highest-confirmed signal across the entire dataset. 56 independent polls across 39 brands and 15 category types all point the same direction. One test showed 93% preference for a hair tool variant that simply displayed the barrel size on the box. No redesign, no new photography. Just displaying the one piece of information shoppers needed before they could decide.

Here’s what’s happening underneath that: Analysts aren’t being demanding. They’re managing decision risk. The question they can’t answer is the question that prevents them from committing. Answer it visually and you remove the obstacle entirely.

Right now: Look at your main image and list every specific question a first-time buyer might have about your product. Count how many your image answers before they have to click.

The Looker (roughly 20-25% of decision language in the dataset)

This shopper’s decision happens before they read a single word.

They feel the image first. Color, composition, visual appeal — all of it registers in the first fraction of a second. If the palette is wrong, the decision is made before they’ve consciously processed anything else.

Color drove preference before any other element registered across 52 independent polls and 39 brands, making it the second-highest confirmed signal in the dataset. Between 15% and 35% of respondents in any given poll cited color negatively, which means a palette problem is not a small drag. It puts a ceiling on how many votes the image can ever win, and no amount of callout optimization lifts that ceiling.

This is the shopping moment creative teams tend to know best, because creative teams are often already in Looker territory when they evaluate images. Which means Analysts, Trust Seekers, and everyone else on this list can get quietly ignored when the image is being designed, not out of negligence, but because nobody in the room feels their friction.

Here’s why this matters: when your VP of marketing looks at your product image and immediately has a reaction, they’re not applying a shopper filter. They’re applying a taste filter. Those two filters are not the same thing, and the people who confuse them are the ones who keep losing polls they expected to win.

The Trust Seeker (roughly 15-20% of decision language in the dataset)

This shopper wants proof that someone other than the seller has verified what the seller is claiming.

The mechanism underneath this is consistent across every form it takes: Amazon shoppers often don’t know the brand. In that context, borrowed credibility is the fastest path to a decision. It tells the shopper that someone they already trust has already vetted this, so they don’t have to start from zero.

That borrowed credibility shows up in two distinct forms, and both matter.

The first is institutional validation. “Made in the USA.” “Clinically tested.” “Organic certified.” “Dermatologist recommended.” In one poll (candles, 30 respondents), 40% of people cited the “US OWNED” badge as their primary reason for choosing. Not the design. Not the photography. The badge. That signal held across 15 independent polls spanning beauty, supplements, grocery, pet care, home goods, and personal care.

The second form is visual honesty. When a product photo looks AI-generated, heavily airbrushed, or staged beyond recognition, Trust Seekers say so out loud in the verbatim responses. One respondent described a collagen mask image as “stupid images of young airbrushed women who have no use for this type of product.” Another said, “Please no more of the fake hands. They just make products look cheap and poorly made.”

I did not expect the visual honesty finding to be that blunt. These respondents aren’t describing a mild preference. They’re describing a trust rejection. The product fails the credibility check before they even read the copy.

The Trust Seeker isn’t bothered by elevated aesthetics. They’re bothered by aesthetics that don’t feel earned.

The Brand Loyalist (roughly 10-15% of decision language in the dataset)

This shopping moment bypasses the visual evaluation almost entirely.

When a Brand Loyalist recognizes a brand name, they don’t assess the image. They confirm a prior decision. The creative work is essentially irrelevant to them. Across 26 independent polls and 18+ brands, established brand recognition drove 56-68% win rates in competitive SERP tests, with 10-40% of respondents per poll explicitly citing familiarity as their primary driver.

Real quotes from respondents:

“I ranked based on brand name recognition and how well I know the product works.”

“Bausch and Lomb has been around forever. I trust their eye products.”

“I would get the GNC brand before the others because it is a name brand.”

For established brands, the implication is clear: your brand name is your highest-value creative asset in competitive browse. Not your color palette, not your callout count, not your lifestyle hero. The name. Make it the largest, most legible element on the image at thumbnail scale (150x150px). Modern minimalism that pushes the brand name small is a strategic mistake on SERP.

For challenger brands, the implication is less obvious but more interesting. You can’t win by looking more premium in the same lane the established brand already occupies. The challengers who beat recognized brands in this dataset introduced a trust signal the established brand didn’t visually claim. They didn’t out-design the competition. They answered a different question.

Right now: Pull up your main image at 150x150px (thumbnail size). Can you read your brand name in under two seconds?

The Use-Context Shopper (roughly 10-15% of decision language in the dataset)

This shopping moment activates when a shopper needs to see the product in their specific situation before they can decide. Not in the abstract. Actually in their kitchen, on their kid’s feet, in their bathroom routine.

When a shopper can see the product in use, the cognitive work is done for them. The decision becomes confirmation rather than evaluation.

The most lopsided result in the entire dataset came from a shoelace test. Showing the laces on an actual shoe beat a beautifully arranged color display 29-to-1. Every respondent said some version of “seeing them on a shoe makes it click.” The shoelace image answered a question the color display never even acknowledged. This pattern confirmed across 32 independent polls and 20 distinct brands in 15 categories.

This shopping moment intensifies when the buying context involves someone other than the shopper themselves. A parent buying for a child runs two filters simultaneously: will my child actually want this, and is this actually good quality or am I just being sold fun? An image has to clear both filters to get the vote.

What works in testing for this buying context: happy children visibly using the product (not posing with it), gender-neutral designs that work for a multi-kid household, imagery that helps the parent visualize their specific child’s reaction. The target is fun but not childish. It’s a narrow window, and most brands miss it by landing hard on one side or the other.

Here’s why the context requirement exists at the psychology level: abstract product shots ask the shopper to do imaginative work. Some shoppers will do that work. Use-Context Shoppers won’t, not because they’re lazy, but because the answer to “how will this actually work for me” is the thing they’re buying. The product that visually answers that question wins.

The Premium Seeker (roughly 10-15% of decision language in the dataset)

This shopper is deciding what owning the product says about them.

Aspirational, spa-coded, elevated. Soft lighting. Natural textures. Minimal prop styling. Warm tonality. When this look is authentic to the product’s actual price point, it short-circuits rational price evaluation. The shopper shifts into an emotional state where price becomes secondary to identity.

This shopping moment confirmed across 11 independent polls in beauty, home, personal care, and food and beverage categories, which is fewer polls than the other shopping moments and more concentrated in a narrower category range. Worth naming: aspirational creative tends to cluster in categories where the aesthetic convention already runs premium. It’s harder to cleanly separate shopping behavior from category convention in these verticals, so treat Premium Seeker data as directional, not definitive.

The most important thing to understand about this shopping moment is the break condition: aspirational staging for a budget-positioned product doesn’t read as premium. It reads as misleading. The elevated look sets a price expectation in the shopper’s mind. When the actual price doesn’t match, the image actively hurts you. This creative approach is only available to brands whose pricing and positioning actually justify it. If yours don’t, a straightforward image will outperform the elevated one every time.

Why the winning images speak to more than one shopping moment

This is the part that changes how you build an image.

Across 900+ polls, the images that won by the largest margins were almost never optimized for a single shopping moment. Images that spoke to one shopping moment and ignored the rest won close polls. Images that spoke to two or three won decisively.

Here’s what this looks like in practice. Take a supplement image optimized purely for the Analyst: every callout answered, dose displayed, ingredients visible. Strong, functional, cold. Now add a warm color palette that clears the Looker’s first-pass filter. Add a certification badge in the corner that gives the Trust Seeker their borrowed credibility. You haven’t changed what the image communicates or buried any of the callouts. You’ve changed how many shopping moments it reaches on the first pass, and the vote split reflects it.

The question I’m always asking when I look at a new image: which two or three shopping moments can we satisfy without making each other worse?

This is also why polls where your team’s favorite image loses can feel so confusing. The image was probably optimized beautifully for one shopping moment, usually the Looker, because that’s the shopping moment creative teams feel most fluent in. The winning image was probably just decent for several. “Decent for several” beats “exceptional for one” almost every time.

The diagnostic

A PDP is three surfaces: your main image, your product image stack (images 2–7), and your A+ content. The shopping moment framework applies to all three, and a gap on any one of them is a gap.

Main image. Look at it and ask: which shopping moment was it actually built for?

If the image is gorgeous but tells you nothing: Looker-only.

If the image is packed with callouts but isn’t beautiful: Analyst-only.

If the image feels premium but has no certifications, proof points, or trust signals: Premium Seeker-only, and you’re ignoring roughly 80% of your potential buyers.

Product image stack. Each of your secondary images should have a job. The question isn’t whether each individual image is good — it’s whether the six images collectively cover the modes your main image couldn’t. If your main image is callout-heavy for Analysts, do you have a dedicated lifestyle image for Use-Context Shoppers? A certification image for Trust Seekers? A stack of six images that all read as variations of the same mood board is Looker-only at the stack level, and it leaves a lot of shoppers with unanswered questions that the A+ content will have to work twice as hard to close.

A+ content. Shoppers who scroll to A+ are self-selected — they were interested enough to keep reading, which means they cleared the Looker filter already. The modes that move the needle in A+ are Analyst (still working through questions, now with more space to answer them), Trust Seeker (brand story, clinical data, deeper proof points), and Use-Context (lifestyle imagery at a size the main image couldn’t accommodate). If your A+ is primarily atmospheric — full-width lifestyle images with minimal copy — you’re serving shoppers who were probably already going to buy. The ones you can actually move with good A+ are the Analysts still on the fence.

Then ask: which shopping moments would my actual buyer most likely be operating in when they land on my listing? And across all three surfaces, which ones have no coverage at all?

Most brands don’t do this, and it’s not because it’s hard. It’s because nobody on the team is in the shopping moment they’re missing. You can test for what you can’t feel.

The question I always get is whether better poll results actually move sales. 82% of our clients see a sales lift after launch. The range is 20% on the low end and 2500% at the top. We build every listing with multiple shopping moments in mind, and we’ve been doing it long enough to know that’s not a coincidence.

My agency, Mindful Goods runs image testing and creative strategy for brands selling on Amazon. If you want to know which shopping moments your current main image is built for, and which one is quietly driving your competitor’s wins, here’s what we actually do: we run your creative through this framework, map which shopping moments you’re hitting and which you’re leaving empty, and run a PickFu split test that shows you the gap.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• BONUS: Generate the shopping moments for your Amazon listing with this custom Claude prompt.

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If you’re watching your sales dashboard and feeling good about it, that’s understandable. Deal is performing, sessions are up, revenue is tracking. That’s the right thing to want on a Prime Day.

But there’s a different number you should pull right now.

Unit session percentage. That’s what Seller Central calls your conversion rate: the ratio of shoppers who visited each listing to shoppers who bought.

Here’s what makes Prime Day so useful beyond the revenue: the traffic is cold.

Shoppers on Prime Day are browsing broadly, comparing quickly, and less committed before they land on your listing. They found you through a deal, not through search intent. That’s a fundamentally different shopper than the one who finds you organically on a Tuesday in March.

And that cold traffic pattern? That’s what Q4 sends you at scale.

Black Friday, Cyber Monday, the full holiday window: that traffic runs colder than your typical organic search baseline. Deal-driven, comparison-shopping, brief attention. When your Prime Day conversion rate holds up against cold traffic, that’s signal. When it doesn’t, that’s also signal, and it’s the more useful one.

I have some version of the same conversation every July. A brand doing real volume on Amazon, feeling good about their Prime Day revenue, and then we pull up the conversion rate together and there’s a pause. A number like 6.8% on a brand doing around $4M when their Q4 rate last year was 11.3%.

Same products. Same listings. The deal drove traffic their listings couldn’t convert at the rate their warmer organic traffic does. That gap is where the gold is. And right now, with Q4 about 12 weeks out, there’s still time to do something about it.

By October, there won’t be.

Designing and split testing content on PickFu needs time for iteration. Making creative changes on Amazon takes time to settle. Amazon Experiments, the platform’s built-in testing tool, takes 4-8 weeks to reach meaningful data. New images and SEO copy need time for the algorithm to index them and for session data to accumulate.

If you’re swapping creative in November, you’re running untested content during the highest-stakes traffic window of the year, and you won’t know if it’s working until December.

The brands that enter Q4 with confidence used the summer to do the work. Prime Day either confirmed their listings were ready, or it showed them exactly what to fix while there was still time to fix it.

This is go-time. Let’s do this.

The full game plan is in the paid section below.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• What your Prime Dat data is actually telling you

• The Q4 content sprint game plan

• How to scale the execution

• BONUS: Post-Prime Day Optimization Playbook with our top recommended split tests to run before Q4 (free for paid members)

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We looked for patterns. Compared them to our PickFu signal database. Mapped the module structure.

And we found patterns. A lot of them. All 10 brands use some form of clinical or quantified proof. All 10 show the product in use on a real person. All 10 name specific ingredients.

The module sequence across the pages is almost identical: hero claim, what it is, how it works, proof, usage, brand story.

My first instinct was to write this up as a framework. Here’s the pattern. Apply it. Go.

Then I made myself slow down and do something I should probably do more often: I played devil’s advocate against my own analysis before publishing anything.

Here’s what I found when I did.

The honest problem with pattern analysis

The most obvious flaw is that every brand I analyzed was pre-selected by our team as better than “good.” We picked them for the video because the designs impressed us and that is hard to do.

All 10 use clinical proof, lifestyle imagery, and named ingredients and I genuinely can’t tell you whether those are the things contribute to them being better than “good” in our minds. It might just be what every decent beauty brand is doing with A+ Content in 2026. The low-performing ones might have them too.

The second problem goes in a direction I wasn’t comfortable admitting at first. Bubble has raised significant venture capital and was built explicitly for Gen Z with a full community and content infrastructure. Youth to the People was acquired by L’Oréal. Nood raised millions to fund its marketing. These brands might have excellent A+ content because they’re well-resourced organizations that invest in quality creative across everything. The A+ page quality is a symptom of that organizational health, not necessarily the cause of their Amazon conversion.

I’m not saying their A+ content doesn’t work. I’m saying I’m say it most certainly works because these brands are doing so many things right. Reviews, advertising spend, TikTok traffic, brand recognition built off-platform. All are working along side each other to improve conversion once traffic hits the pdp.

The PickFu cross-reference

I ran the visual patterns through our Signal Library to see which ones mapped to confirmed split test results.

• The Clinical/Trust signal is Replicated across 15+ independent polls.

• The Lifestyle/Use Context signal is Replicated across 32 polls.

Important note: most of that data is from Home & Kitchen, Supplements, Food/Grocery, and Personal Care. Beauty A+ content at scale is underrepresented in our library.

When I write “this pattern maps to a Replicated signal,” that means it’s been confirmed across Amazon broadly. Not specifically in beauty A+ content. The mechanism is plausible. The evidence is adjacent, not direct.

The honest framing is: the visual patterns and the split test data point in the same direction. They’re not the same data.

What holds up after the stress test

These 10 brands aren’t just “doing aspirational imagery.” They’ve matched their specific visual aesthetic precisely to who’s actually buying their product.

Nakey uses hot pink and body-positive irreverence because their buyer is a millennial woman who finds spa aesthetics alienating.

Ancient Minerals uses deep ocean blue and origin-of-earth language because their buyer is a wellness-committed adult who takes sourcing seriously.

Bubble uses Gen Z visual language because their buyers are teenagers. None of them are off-brand for their own audience.

You can add a clinical percentage to any listing. Getting the tone, style, and messaging right requires actually knowing your buyer. And the brands that get it wrong aren’t just bland. They’re actively losing purchase consideration because their creative sends the wrong signal to the right person. (I’ve watched this happen in split tests more times than I can count. The product is good. The creative is just talking to the wrong version of the customer.)

Worth noting the reverse too: for budget-positioned products, aspirational staging backfires. It sets a price expectation the listing can’t meet, and the shopper arrives expecting premium and leaves when they see the actual price.

This might seem basic or obvious, but it more than you think.

If all 10 of these high-performing brands have clinical proof, lifestyle imagery, and named ingredients, then those things are probably the baseline of what brands need to be successful with their creatives on Amazon.

The question for any beauty brand in 2026 isn’t whether to include a clinical percentage claim. It’s what you’re doing on top of it that creates any real separation from the category.

Design is a lever that separates you from the pack. The baseline is higher than most brands are hitting, even with AI. That alone is useful to know.

For a deeper dive into why we chose these brands, check out our YouTube video below.

The two undocumented patterns worth more than the six confirmed ones.

I flagged two patterns that weren’t in our Signal Library…yet.

1. Expectation Subversion: this is where the brand surprises you by contradicting what's expected in their headline claim ie “without the downside you expected,” appears in 6 of these 10 pages.

   Nakey uses hot pink and body-positive irreverence because their buyer is a millennial woman who finds spa aesthetics alienating.
   
   Ancient Minerals uses deep ocean blue and origin-of-earth language because their buyer is a wellness-committed adult who takes sourcing seriously.
   
   Bubble uses Gen Z visual language because their buyers are teenagers. None of them are off-brand for their own audience.

   That structure: [name the feared downside, then show the product overrides it] proactively resolves skepticism rather than just stacking positive claims.
   

2. Proprietary Technology Hook: this pattern appears in 5 of the 10. This can be a named compound or formulation that creates a memory hook for the shopper. “PrecisionPulse™ IPL.” “NAD+ Restore Complex.” “Barrier Lipids.” The name turns an ingredient specification into a differentiator. That’s harder for competitors to claim.

Neither of those is anything new, but both require genuine product differentiation to execute honestly. Those are the more interesting creative bets right now, and neither of them shows up in a “10 things great A+ pages have in common” list because they’re subtler and less universal.

Same question. Three very different right answers.

Three personal-care brands made wildly different design choices — color, language, energy. The trick isn’t that one is “better.” Each one is engineered for exactly who’s buying it.

What to actually do with this

Pull up your A+ content. Or your client’s.

Ask two questions.

First: what is the aesthetic of this page, and does it match who’s actually buying the product? Not “aspirational” in general. And not appealing to EVERYONE. Which flavor of aspirational and for who specifically. Spa? Clinical? Gen Z community? Ingredient-forward? Wellness ritual? Get specific, then check if the photography, color palette, and copy all point to the same answer. If they don’t, the misalignment is costing you before the shopper even hits your pdp.

Second: what expectation does your buyer arrive with that your product disproves? Name it precisely. Then look at your A+ content and check whether it says so directly, in the first two modules, in plain language.

If neither answer is obvious from looking at the page, that’s where the work is.

There’s one more observation I saved just for my paid subscribers.

🔒 Now Let’s Get To The Good Stuff

Upgrading to paid is like getting the version of my brain that isn’t filtered for a general audience. Less than one Starbucks order per month, and you get the strategy I charge clients real money for — broken down so you can use it yourself.

Below the paywall:

• The one finding I found the most interesting

• The most universally replicated signal in our split test database

• The most practical test for any A+ Content in any category

[Paid section starts here]

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