Amazon Search Query Performance Report: How to Read Your Share of Demand

The cost side of the account moved again this year. Amazon added roughly $0.08 per unit on average to FBA fees in January 2026, then applied a 3.5% fuel and inflation surcharge on top of those fees in April, worth another $0.15 to $0.35 per unit depending on the item. On the auction side, Ad Badger's 2026 benchmark data puts average CPC at $1.18, peaking at $1.21 in February, with average ACoS at 32.5%. More than 70% of Amazon sellers now run ads, up from roughly 40% five years ago. An efficiency target set two years ago is being asked to clear a noticeably higher bar than it was built for.

That makes the question of where growth is still available a much harder one, and most of the reporting on your desk answers a narrower version of it. Your advertising reports tell you how the queries you bid on performed: what your campaigns served against, what those served impressions cost, and what came back. They are silent on the query where you never showed up at all, and on the query where you did show up and somebody else took the purchase.

The Amazon search query performance report is the only Amazon-native place where you see the whole query, your slice of it, and where in the funnel your slice narrows. For a given search term it reports the total marketplace count of impressions, clicks, cart adds, and purchases, and beside each of those totals it reports your brand's count and your brand's share. That structure is what makes it a share-of-demand report rather than a performance report, and it changes what you can ask of it.

Quick answer: what the Amazon search query performance report tells you

The Amazon search query performance report is a Brand Analytics report, available to brand-registered sellers inside Seller Central, that lists shopper search queries alongside four funnel stages: impressions, clicks, cart adds, and purchases. For each stage it gives both the total marketplace count for that query and your brand's (or a single ASIN's) count and share of it. That is the structural difference from ad reporting: it covers organic and paid demand together, and it shows you the demand you did not capture rather than only the impressions you paid for. It is available at brand level and ASIN level, in weekly, monthly, and quarterly views, with comparison against prior periods. Read as a share-of-demand report, it answers two questions worth answering: which queries are you under-indexed on, and at which stage does your share narrow.

What the report contains, and why the second number is the useful one

Most write-ups of the SQPR list the four funnel stages and stop. The stages on their own are the least interesting part, because you can get impressions, clicks and conversions from half a dozen other places. The part you cannot get anywhere else is the pairing.

Two numbers at every stage

For each query and each stage, the report carries a total marketplace count and your count, plus your share expressed as a percentage. A query with 40,000 total impressions where your brand holds 6% impression share is a very different object from a query with 4,000 total impressions where you hold 60%, even though both might return a similar impression count in an ad report. The first is a query you are barely present in. The second is one you own.

Alongside the counts, each stage carries a rate for the marketplace as a whole: click rate against impressions, cart add rate against clicks, purchase rate against cart adds. The query set is also ranked by search query volume, with a search query score giving each query's rank position by volume in that marketplace for the period, so you can tell a genuinely large query from a long-tail one without eyeballing raw counts.

The most overlooked columns are the median price and the shipping speed columns at the click, cart add, and purchase stages. They describe the products shoppers engaged with on that query, not yours. If the median purchased price sits well below your listed price, you have a checkable explanation for a weak purchase share rather than a guess about creative.

Where to find it, and at what grain

Access runs through Seller Central under Brands, then Brand Analytics, then the search query performance dashboard. Brand Registry is the gate: without it the report is not there. Inside the dashboard you can toggle between a brand view, which aggregates across your catalogue, and an ASIN view, which attributes the same funnel to individual ASINs. Both export to CSV, and the export is where any real analysis happens.

Date ranges come as weekly, monthly and quarterly buckets rather than custom ranges, and the data lands after the period closes, so treat it as a review-cadence input rather than a monitoring feed. Amazon documents the wider Brand Analytics suite on its Brand Analytics page, which is worth rechecking periodically, since views get added.

SQPR, the search-term report, and the search query performance dashboard: which one answers what

Two of these three are the same thing. The search query performance dashboard is the in-console view inside Brand Analytics, and the report is what you export out of it. The search-term report comes from a different system entirely, and if you have not worked with it recently, our guide to the Amazon search terms report covers it in its own right.

The distinction that matters: the SQPR is Brand Analytics shopper-search data covering demand across the funnel including organic, while the search-term report is advertising data covering what your campaigns served against. One tells you the size of the room. The other tells you what happened at your table.

Reading the drop-off: where your share narrows, and what that narrows the cause to

Work the four stages as a chain rather than four separate numbers. Impression share tells you whether Amazon puts you in front of the query at all. Click share against impression share tells you whether the listing earns attention once it is there. Cart add share against click share is the detail page. Purchase share against cart add share is checkout, where price, availability and shipping speed do most of the work.

The caveat governs everything below it: a low share at one stage has several possible causes, and the report narrows the list rather than naming the cause. Anyone who tells you a click-to-cart gap means your images are bad is guessing. What you get is a much shorter list of things worth checking, ordered by likelihood, which beats checking everything.

Two notes. Share comparisons across stages only mean something where volume makes the ratios stable; on a query with 300 impressions in a week, a two-point move is noise. And when impression-to-click is where you lose, the cheapest check takes ten minutes: pull up the results page and look at your main image beside the neighbours. Our notes on Amazon product images cover what tends to fail in that grid, and the wider listing optimization piece covers the detail-page side that shows up at the click-to-cart stage.

Branded versus non-branded demand: the split that shows whether you are growing or defending

This is the highest-value thing you can do with the report, and most teams never build it, because the report does not do it for them. Every query in the export is either someone looking for you or someone looking for the thing you sell, and those populations behave nothing alike. Blended, they produce a share number that moves for reasons you cannot interpret.

Building the split

Tag every query in the export into three buckets rather than two:

• Branded. Contains your brand name, a sub-brand, a product-line name, or a common misspelling of any of those. Build the misspelling list once and keep it; it is longer than you expect.
• Competitor. Contains a rival brand name. These behave differently enough from generic terms that averaging them in hides both.
• Non-branded. Everything else. Generic category terms, use-case terms, spec terms, occasion terms.

Then compute share per bucket the weighted way: sum your brand's purchase count across the bucket, sum the total marketplace purchase count across the same bucket, and divide. Do not average the per-query share percentages. Averaging gives a 20-impression query the same weight as a 200,000-impression query, and it is the single most common way this analysis gets quietly broken. The same weighting applies at every stage, so you can plot four bucket-level shares per week and watch them move.

What the two lines mean when you read them together

Branded share should be high and fairly stable. It is your own name, so losing share on it means something specific went wrong (a competitor bidding your term successfully, a listing suppressed, a stockout on the hero ASIN). The reading comes from putting the two lines side by side.

• Branded share healthy, non-branded share flat. You are defending, not growing. The demand you convert is coming from shoppers who already knew the brand before they opened Amazon. New-buyer acquisition is not happening on the marketplace, whatever the topline revenue line is doing.
• Branded share healthy, non-branded share rising. Growth is real and it is compounding, because non-branded wins today become branded searches later.
• Branded share slipping, non-branded share flat. Look at defence before growth. Someone is intercepting your own demand, and that is usually cheaper to fix than winning new category share.
• Non-branded share rising while total non-branded volume falls. You are taking a larger slice of a shrinking category. Worth knowing before you fund an expansion plan on the share trend alone.

One caveat: branded query volume responds to activity that has nothing to do with your marketplace work. A creator burst, a retail media flight off Amazon, a PR hit, all push branded search up. That is demand creation landing, worth counting as such, but it is not a closed attribution loop and should not be read as an on-Amazon win.

The split also gives you a defensible way to talk about competitive position, where the usual share of voice versus share of shelf argument tends to go in circles. Non-branded purchase share, weighted by volume, is a harder number than either.

Turning the report into a paid and organic decision

Diagnosis is only worth the time if it changes what the account does next. Two decisions come out of the SQPR more reliably than the rest.

Harvesting the non-branded queries where you convert but under-index

Look for non-branded queries where your purchase share sits materially below your impression share and the marketplace purchase rate on the query is at or above your catalogue norm. That combination says the demand converts and you are getting a smaller piece of it than your visibility should earn. Those are the harvest candidates: promote them into exact-match targeting with their own bids rather than leaving them to broad and automatic campaigns to pick up incidentally. Our keyword harvesting guide covers the mechanics of moving a term across campaign types without cannibalising the source.

Rank the candidate list by revenue at stake, not by the size of the share gap. Total purchases on the query, multiplied by your average selling price, multiplied by the share points you think are winnable, gives you a rough ceiling per query. A six-point gap on a query worth $40,000 a month beats a thirty-point gap on one worth $900.

Where organic share is strong enough to reduce paid support

The reverse move is more tempting and more dangerous. When organic position on a query is strong, paid support on the same term looks like paying for a click you were going to get anyway, and the campaign ACoS looks bad enough to justify pulling it.

The complication is real: clicks and conversions on a query feed the ranking system Amazon uses to order organic results (the system operators still generally call A9, or A10). Cutting the paid contribution reduces signal volume on that query, and organic rank sometimes follows it down. You save the ad spend and lose more revenue than you saved, and by the time it shows in the data the cause is three weeks back.

So test it, do not decide it. Pick a subset of queries where organic share is strong, hold a matched set as control, run the test for two to three full weeks, and watch total purchase count and organic share on the test queries, not the ACoS of the campaign you switched off. Read the outcome against total advertising cost of sale rather than campaign ACoS, because the effect shows up outside the campaign you switched off.

Common mistakes when reading the SQPR

• Reading share without volume. An 80% share of a query nobody searches is a rounding error. Always carry the total count beside the percentage.
• Comparing weeks with different query-set composition. The query set is not fixed. Seasonal terms enter and leave, so a bucket-level share that moves three points can be composition rather than performance. Reconcile the query list before you interpret the delta.
• Treating it as an ads report. There is no spend column, no CPC, no campaign attribution, and purchases on a query include organic purchases. Diagnosing paid efficiency from it will mislead you; that is the search-term report's job.
• Chasing volume without relevance. A query with enormous volume and no genuine fit to the catalogue will absorb budget and return nothing. Volume is a ceiling, not an opportunity.
• Reading one week as a trend. Weekly query-level data is noisy. Three consecutive weeks moving the same direction is a signal. One week is a week.

Where the weekly pass runs out

The analysis above is not difficult. It is repetitive. Split branded from non-branded, compute the weighted share change stage by stage, cross-reference the queries that broke against the listing, and rank the result by revenue at stake. Same work every week, per brand, and again per marketplace.

A careful manual pass gets through the top queries for one brand, and then the week is gone. So the pass gets shortened to a sort by volume and a look at the top twenty, which is exactly the subset you already watch. Or it slips a week, then two, and the comparison you needed (this week against last week, same logic both times) is gone, because the method drifted in between.

Handing the export to a general-purpose AI tool helps with the arithmetic and does not solve the drift. Ask the same question in slightly different words two weeks apart and the answers cannot be lined up, which defeats the point of a weekly read. The step that resists automation hardest is deciding whether a click-to-cart drop on a given query is a price problem, an image problem, or a relevance problem. That is a judgment about meaning, and a numeric threshold cannot make it.

Where Qore fits: the same analysis, run every week, the same way

Qore is the layer where a review you do by hand becomes a workflow that runs itself: you describe the analysis once, it gets assembled into logic you can read and edit, and then it is locked. Internally we call it the codified-workflow layer. Q, the assistant inside Qore, is what you talk to while drawing the workflow out, and it earns its keep on the part where you know what you want and have not written it down in a form a system can run.

For an SQPR routine, the locked logic is what makes week-over-week comparison meaningful. Same inputs, same steps, same output shape, so a change in the output is a change in the account rather than a change in how you asked.

A worked example

Input. The weekly brand-view export for one brand in one marketplace, the ASIN-view export for the top twenty ASINs, your branded and competitor token lists, and the current campaign structure.

The locked logic.

1. Tag every query branded, competitor, or non-branded against the token lists, including the misspelling variants.
2. Compute weighted share at all four stages per bucket, plus the week-over-week change, using summed counts rather than averaged percentages.
3. For each non-branded query in the top N by total purchases, compute impression, click, cart add and purchase share, and flag the stage where share falls by more than your chosen threshold relative to the stage before it.
4. For every click-to-cart flag, pull the ASIN's main image, current price, and the median clicked and purchased price from the same report row, then judge whether the gap reads as price, creative, or relevance. This is the semantic step, and it is why the workflow holds a standard rather than a formula.
5. Rank the flagged queries by total purchases multiplied by your average selling price, so the output is ordered by revenue at stake.

Output. A ranked list of roughly a dozen queries, each with the stage that broke, the candidate cause, the evidence behind the call, and a recommended action. Plus the four bucket-level share lines with their week-over-week movement.

The business decision. Say three of the twelve come back as price gaps at the cart stage, one as a main-image problem on a hero ASIN, and eight as relevance mismatches. That routes cleanly: one item to whoever owns price, one to creative, eight into the campaign structure as harvest candidates with revenue ceilings attached. Next week the same list arrives with the same method behind it, so the deltas are readable.

Run on a schedule, or across a roster of brands and marketplaces at once, which is where the time comes back for anyone managing more than one account.

The honest limits. Qore codifies your standard; it does not invent one. If the team has not agreed what counts as branded or what threshold matters, that argument still has to be had by people. It reads and analyses any account regardless of what runs your bids, so the diagnosis works whatever your stack is; for Qore to carry a bid or campaign change out itself, Trellis has to be the tool managing those bids. Actions default to manual approval, and autonomy is available on a given skill once you have watched it run and decided you trust it. Qore moved to a public waitlist in July 2026, out of private beta.

Start with one brand and one week

The Amazon search query performance report is worth the effort because it is the only Amazon-native view of demand you did not capture, and that is where the growth question gets answered. If you are starting from nothing, do this: export one week at brand view, tag the query set into branded, competitor and non-branded, compute weighted purchase share for each bucket, and pick the ten non-branded queries with the largest gap between impression share and purchase share by revenue at stake. That one pass tells you more about where you stand than a month of campaign-level reporting.

Then decide whether it becomes a habit. A diagnosis that runs once is interesting; one that runs every week with the same logic is how you build a record of methodical impact you can show someone. To see what that looks like as a codified workflow rather than a recurring calendar entry, book a walkthrough and bring a real export.