Three patterns I find in almost every Amazon audit
Over the last several months I’ve done dozens of Amazon clarity audits. The brands span apparel, skincare, pet supplements, baby goods, premium home textiles, supplements, energy drinks, deodorant, and food. Revenue scales range from accounts doing under $100K a month to one doing over $3.4M a month. Some are five-year-old established sellers. Some launched in 2024.
I expected each account to have its own version of the same problem. It didn’t go that way. Nearly every account has the same three structural patterns, in similar proportions. The pattern recognition is consistent enough at this point that I now check for these three things before I look at anything else.
Here they are, in the order they cost money.
1. The conversion rate gap inside the same catalog
The brand’s top-converting ASIN almost always converts at 3 to 8 times the account average.
One apparel brand I audited had a store-level conversion rate of 6.0% across 216,000 sessions. Their best ASIN converted at 27.1%. Lifting the underperformers even halfway toward the best one represented over $129,000 in recoverable revenue over 90 days. Same traffic. No additional ad spend.
Another, a running and fitness brand, was at 1.6% store-level CVR with 310,000 sessions. Best ASIN at 5.8%, worst under 0.5%. The recoverable opportunity from closing the gap was over $170,000 in 90 days.
This isn’t a “creative needs work” problem. It’s a measurement problem. Most agencies look at account-level ACOS, account-level conversion, account-level ROAS. They don’t look at the spread inside the catalog. So they never see the gap.
The fix is unglamorous. Figure out why the best ASIN converts so well. It’s almost always a combination of pricing, image hierarchy, A+ content, and review concentration. Then port what works to the underperformers one at a time. Most accounts have a 30-to-90-day project here worth more revenue than any ad optimization.
2. Branded keyword neglect
This one is consistent across every category I’ve worked in.
A skincare brand I audited had spent over $113,000 in ads at a 125% ACOS over ten months. They were burning $1.25 for every dollar earned. When I broke down the spend, their three branded terms had spent a combined $614 and generated $5,146 in sales. Branded ROAS over 8x. Non-branded ROAS under 0.8x. They had effectively underfunded the only thing that was working.
An energy drink brand showed the same pattern: branded ACOS 19% vs non-branded ACOS 125%. They were spending 70% of their budget chasing a 0.8x ROAS while their own brand defense was the cheapest, highest-converting traffic in the entire account.
I see this in every audit. Agencies obsess over non-brand acquisition keywords because that’s where the “growth” story is. They starve the defensive layer. Then a competitor starts bidding on the brand’s own name and the brand owner discovers their own customers are being intercepted on the way to checkout.
If you put $5,000 a month behind structured branded campaigns at sub-20% ACOS, that’s a $40,000-a-month revenue stream in most accounts. Most accounts get a fraction of that because nobody set it up properly.
3. The zero-order spend pool
This one is the most technical, and the most consistently missed.
Every account I’ve audited has a pool of ad spend accumulating clicks without producing orders, at click volumes high enough that it’s statistically clear those targets aren’t going to convert at any cost.
Most agencies handle this with a static rule. Something like “if a target has 20 clicks and no orders, pause it.” The static rule is wrong. The right threshold varies dramatically by account, depending on the account’s observed conversion rate and the variability around that conversion rate.
In one pet supplement account, the right dynamic threshold computed from the account’s actual conversion behavior was 7 clicks. In a baby monitor account at scale, it was 25 clicks. In a premium body pillow brand, it was 45.
The accounts using a generic 20-click rule were wasting money in two directions. Cutting some targets too early (the high-AOV ones that needed more clicks to validate). Letting others bleed too long (the low-priced impulse buys where 7 clicks already told you the answer).
In one premium body pillow account I audited, nearly 50% of total ad spend (about $14,700 in a 60-day window) was sitting in this pool. In a baby monitor brand running at scale, the equivalent pool was over $81,000 in 64 days, 38% of all spend. In a home textiles brand, the no-order pool was projected at over $300,000 over 90 days at current run-rate.
Most agencies don’t compute the dynamic threshold. Static rules scale across clients without thought. Static rules cost real money.
The pattern recognition is the point
Three patterns. Across every account I’ve audited. None of them exotic. None requiring advanced tooling. All three visible inside data the brand already has access to.
The reason most accounts have them isn’t that the agencies running them are bad. It’s that the model rewards bandwidth, not depth. One account manager runs fifteen brands. The job becomes risk management, not diagnostics. The three patterns above each require sitting with a single account for two or three hours and asking specific questions. The model doesn’t pay for that. So nobody does it.
If you run an Amazon account and you’ve never had someone look for these three specifically, your account probably has them. The fix in each case is a few weeks of structured work, not a complete rebuild.
Want to ask your agency these questions?
I put together a one-page diagnostic with five sharp questions you can send to your current agency. If they can’t answer in 24 hours with real numbers, you have a diagnostic gap.