Where 30 to 40 percent of your Amazon ad spend actually goes.

The number that gets quoted at every Amazon PPC conference is from sellerqi's 2026 benchmark report: the average Amazon seller wastes between 30 and 40 percent of their ad spend on poorly optimised campaigns. At an average CPC of €1.21 and rising, on a €20,000 monthly budget, that is between €6,000 and €8,000 a month flowing into impressions that will not convert, keywords that should have been negated weeks ago, and bids that are still climbing for a product whose inventory is about to run out.

The number is widely cited but the reasons behind it are not. This post is an honest map of where the waste actually goes, why standard dashboards do not surface it, and what a decision trace makes visible that a CSV export cannot.

The four buckets that swallow most of the waste

Across the accounts we have read in shadow, four patterns explain roughly 80 percent of the wasted spend. They are in order of how often they show up, not how big each one is.

1. Bids that did not adjust to thin inventory

The most expensive single mistake we see is bidding to first page on an ASIN whose days-of-cover has fallen below the reorder window. The campaign keeps converting at the same rate, the dashboard shows a healthy ACoS, and then the inventory runs out. The keyword ranking decays during the stockout. When inventory returns, the seller has to re-bid into a worse position, often at a higher CPC, to recover the rank they had three weeks earlier. The waste is not the spend that produced the stockout — that spend converted. The waste is the recovery spend that follows.

Trellis surveyed 240 sellers in 2025 and put the average stockout cost — in lost revenue, PPC recovery, and ranking damage — at roughly €18,000. Inventory-aware bidding, what we call Sentinel, throttles bids proportionally as DOC thins rather than hard-pausing at a threshold. The trace shows the supply multiplier on every bid, so the seller knows why the bid was reduced.

2. Search terms that should have been negated but were not

In a typical mid-size account, between 8 and 15 percent of search-term spend goes to terms that have never converted and never will. Sellers do harvest negatives, but they harvest them on a weekly cadence — and on a weekly cadence, a term that fires twenty impressions a day burns budget for a week before anyone notices. A semantic engine reading search terms in real time catches these in hours, not days.

The harder version of this problem is the term that converts occasionally — say, 1.2 percent — but at an ACoS that destroys the unit economics. A standard rule ("negate if zero conversions in N clicks") misses it entirely. A Bayesian conversion model with a confidence band notices that the term is statistically below the break-even pCVR, even when it has a few conversions on its ledger.

3. Bids that climbed because the click rate looked good

Auto-bid systems that optimise for ROAS without considering total net profit — ROAS over TACoS — keep pushing bids higher on keywords that have decent click-throughs but poor end-state contribution. The seller sees a healthy ROAS line on the dashboard, and the agency is paid against ROAS, so nobody is looking at TACoS. Months later the brand looks at the P&L and the contribution margin has fallen two points. Where did it go.

This is the vanity-metric trap. The fix is not a different metric. It is a system that prices bids against TNP (total net profit) rather than the proxy that is easiest to optimise.

4. Cross-marketplace copy without localisation

We documented this one in detail in a separate post — why German Amazon campaigns underperform UK ones — but the short version: copying a UK campaign to Germany without per-marketplace bid math costs +28 percent ACoS and −42 percent CTR, on average, in Ad Badger's 2026 international report. If you run more than one marketplace and your bidding tool is the same one in both, you are almost certainly paying this tax.

Why dashboards miss this

Standard PPC dashboards are organised around aggregates: campaigns, ad groups, ASINs, time ranges. The waste lives in the joints. The inventory bucket lives in the join between SP-API and Ads API. The negative-keyword bucket lives in search-term semantics. The TACoS bucket lives in the join between advertising spend and order economics. The localisation bucket lives in the join between marketplace metadata and bid math.

A dashboard can show the symptoms — ACoS up here, contribution down there — but it cannot tell you which join is leaking. That is what a decision trace is for. Every bid carries the reason: the supply multiplier (was inventory part of it?), the relevance score (was the keyword off-thesis?), the Bayesian estimate (was the conversion prior weak?), the alternatives ruled out (what bid did the agent almost place?).

How to estimate your number before you change anything

Three steps that cost nothing and take an evening.

1. Take last 90 days of search-term reports. Filter to terms with zero conversions and more than 20 clicks. Sum the spend. That is your bottom-floor waste estimate from bucket two alone.
2. Pull your stockout history for the same 90 days. For each stockout, estimate the recovery PPC spend in the four weeks following return-to-stock. Add it to the floor.
3. Compare TACoS across your marketplaces, normalised by margin. If one marketplace is 3+ points higher than the others, the localisation bucket is real on your account.

A reasonable floor is the number you get from the first two steps alone. The full 30–40 percent landing rate comes in once you add the joints that the dashboard cannot see — and a decision trace can. Read how the sixteen agents surface this in real time, or see what a single trace contains.

The reason we lead with Shadow Mode is that this is not an argument we want to win on theory. It is one we want to win on your data, on your real account, before any money changes hands.