--- title: Amazon Search Query Performance: The Secret Data Most Sellers Aren't Using to Find High-Intent Keywords | AMZ Global Experts url: https://www.amzglobalexperts.com/blog/amazon-search-query-performance-high-intent-keywords-2026 description: Amazon ---Most Amazon sellers pick keywords by guessing, or by trusting a third-party tool's estimate of what customers might be searching. Amazon has already told you exactly what customers searched, exactly how many times your listings appeared, exactly how many of those people clicked, added to cart, and bought — and, critically, exactly how that compares to every other product that showed up for the same query. That data has a name: Search Query Performance (SQP), part of Amazon Brand Analytics. It's sitting inside Seller Central right now for every Brand Registry–enrolled seller, and the overwhelming majority of accounts have never pulled the report. Don't guess which keywords will make money. Use Amazon's own customer-search data, enhanced with AI, to find where demand, intent, conversion and competitive opportunity intersect. That's the entire premise of this article — and it's a structurally different starting point than another round of "10 Amazon PPC tips." 1st-party SQP is Amazon's own search & purchase log — not a modeled estimate 7 Data fields per query: impressions, clicks, cart adds, purchases & rates vs. category Every metric shown as your share against the entire query's total 0% Of this data available to sellers without Brand Registry enrollment ## What Search Query Performance Actually Is Search Query Performance is a report inside Amazon Brand Analytics that shows, for every real customer search query connected to your catalog, exactly what happened: how many customers saw your product for that query (impressions and impression share), how many clicked (clicks and click share), how many added to cart (cart adds and cart-add share), and how many purchased (purchases and purchase share) — each one reported both as your absolute number and as your percentage share against the total for that query across every seller in the category. It also reports median price paid, giving you a pricing benchmark specific to that exact search intent rather than a category-wide average. This is fundamentally different from every third-party keyword research tool on the market. Helium 10, Jungle Scout, Data Dive and similar platforms estimate search volume and ranking using sampled, reverse-engineered, or modeled data — genuinely useful for broad market discovery, but approximate by design. SQP is not an estimate. It's Amazon's own internal record of what customers typed and what happened next, filtered down to the queries that actually connected to your ASINs. When a third-party tool tells you a keyword "might" convert well, SQP tells you whether it already did — for your specific products, against your specific competitors, on that exact search term. ![Search Query Performance funnel diagram showing impressions narrowing to clicks, clicks narrowing to cart adds, and cart adds narrowing to purchases, with your brand share vs. category share reported at every stage.](/images/blog/figure-keyword-intent-tier-funnel-v1.svg) Figure 1: The SQP funnel. Every stage — impressions, clicks, cart adds, purchases — is reported both as your raw count and as your percentage share against the entire category for that exact query. The gap between your click share and your purchase share is where the real diagnostic signal lives. ## Why Most Sellers Never Use It **Reason 1: It requires Brand Registry.** SQP lives inside Brand Analytics, which is gated behind Brand Registry enrollment. Unregistered sellers and many newer brands simply don't have access, and some registered brands never realize the report exists because it isn't surfaced anywhere in the main advertising or listing workflows. **Reason 2: The interface buries the signal.** The native Seller Central table is dense, unsorted by actionable priority, and shows raw numbers without flagging which rows represent an opportunity versus which represent a problem. A seller who opens it once, sees a wall of queries and percentages, and closes the tab without a framework for interpreting it is the norm, not the exception. **Reason 3: Third-party tools became the default habit.** Most sellers learned keyword research from Helium 10 or Jungle Scout tutorials and never crossed back into Brand Analytics once that habit was set. Third-party tools are faster to browse and easier to filter — but they're guessing at data Amazon is handing you directly, for free, already scoped to your exact catalog. **The core idea:** third-party tools answer "what are people searching for in this category?" SQP answers "what are people searching for, and what happened when they found _my_ specific products?" The second question is the one that actually predicts PPC and listing ROI. ## The Three Diagnostic Patterns Hiding in Your SQP Data Once you pull the report (Brand Analytics > Search Query Performance, filterable by ASIN or brand, exportable as CSV), the entire analysis comes down to comparing four ratios against each other for every query: impression share, click share, cart-add share, and purchase share. Three patterns explain almost everything worth acting on. Pattern 1 Purchase Share > Click Share — Underexposed Winner You're converting customers who find you at a higher rate than the category average, but your click share (and often impression share) on the query is low. This is a proven, high-converting term you aren't buying enough visibility for. It's the single highest-confidence opportunity in the entire report — a query where the product-market fit is already proven, and the only variable missing is exposure. Action: Bid aggressively Pattern 2 Click Share > Purchase Share — Conversion Leak Customers are finding and clicking your listing for this query at a healthy rate, but far fewer of them buy compared to the category. Something specific to this search intent — price relative to expectation, missing feature signal in the title or images, or review count — is losing the sale after the click. More PPC spend on this query without a listing fix simply inflates ACoS on traffic that already isn't converting. Action: Fix listing first Pattern 3 High Category Volume, Zero Brand Purchases — Untapped Demand A query with substantial total category purchase volume where you have little to no impression share at all. You're structurally absent from a search customers are actively using to buy in your category. Worth testing with a new Sponsored Products campaign and, if relevant, a listing or backend keyword update to establish relevance before bidding aggressively. Action: Test new campaign ## Turning SQP Into a High-Intent Keyword System SQP data becomes a system, not a one-time report pull, when it's run on a repeatable cycle and cross-referenced against your PPC account and your listing content. The workflow below is the one we run for AMZ Global Experts clients every reporting period. 1 Export SQP filtered to your ASINs, trailing quarter Pull the full CSV rather than reading the on-screen table — you need to sort and calculate ratios that the native interface doesn't surface. 2 Calculate the share gap for every query Add a column: Purchase Share minus Click Share. Sort descending for Pattern 1 opportunities (underexposed winners), ascending for Pattern 2 problems (conversion leaks). 3 Cross-reference against your live PPC campaigns Check whether Pattern 1 queries are already in an Exact Match campaign at a competitive bid. Most aren't — this is the fastest, highest-confidence PPC restructuring move available, because the conversion data already exists. 4 Flag Pattern 2 queries against your listing content For every conversion-leak query, check whether the specific intent behind that search (a feature, a size, a use case) is addressed in your title, first three bullets, and main image. Fix the listing before increasing bids on that term. 5 Build a test list from Pattern 3 untapped-demand queries Add the highest-volume Pattern 3 terms to backend search terms if relevant, and launch a small-budget Sponsored Products test to establish baseline performance before committing meaningful spend. 6 Re-pull SQP every reporting period and track share movement The real value compounds when you track whether your share on Pattern 1 queries is increasing after bid changes, and whether Pattern 2 gaps close after listing fixes — turning SQP from a snapshot into a feedback loop. ## SQP vs. Third-Party Keyword Tools: Using Both Correctly Question Best data source Why What are all the ways customers describe products like mine? Third-party tools (Helium 10, Jungle Scout, Data Dive) Broad market discovery across the whole category, not limited to queries already connected to your listings Which of those keywords actually convert for my specific products? Search Query Performance First-party purchase data scoped to your exact ASINs, not modeled or estimated Am I losing sales after the click on a specific search term? Search Query Performance Only SQP reports cart-add and purchase share against the category for that exact query What's my overall estimated search volume and rank trend? Third-party tools SQP doesn't report absolute search volume or organic rank position — only your share of what happened Which underexposed query should I bid up first this week? Search Query Performance Purchase-share-minus-click-share is the single most actionable ranked list SQP produces ### Turn Your PPC Spend Into a Data-Driven System Once your Pattern 1 keywords are identified from SQP, the next step is restructuring bids and budget around them. Run your Amazon Advertising CSV through our free AI PPC Audit — it flags waste keywords, scaling opportunities, and a prioritized 7-day action plan. [Run Free PPC Audit →](https://www.amzglobalexperts.com/tools/amazon-ppc-audit) ## From SQP Signal to PPC Bid Logic SQP tells you _which_ keywords deserve aggressive investment. It doesn't execute the bid changes for you. That's where a structured campaign architecture and intent-tiered bid logic come in — the same three-tier framework we cover in [The 2026 Amazon PPC Playbook](/blog/amazon-ppc-playbook-intent-mapping-geo-signals-ai-bid-logic-2026.html). In practice, Pattern 1 queries from SQP should map directly into your Tier 1 (Transactional) exact-match campaign at aggressive bids, since you already have first-party proof of conversion. Pattern 3 untapped-demand queries belong in a Tier 2 (Evaluation) test campaign until they build their own performance history. If you want a live view of what a keyword's economics look like once you've identified it — break-even ACoS, target bid ceiling, projected profit per unit — run it through our free [Amazon PPC Calculator](/tools/amazon-ppc-calculator/). It turns the ad budget, CPC, and conversion rate you can pull from your campaign manager into a break-even CPC and bid recommendation table, so you're never bidding on a Pattern 1 keyword blind. ## What SQP Reveals About Your Competitors Because every SQP metric is reported as your share against the total for that query, the report is implicitly a competitive intelligence tool. A query where your impression share is high but your purchase share is a fraction of your click share tells you competitors are winning the purchase decision after the click — likely on price, reviews, or a feature your listing doesn't surface. To go a layer deeper on which ASINs are actually capturing that remaining share and what keywords are driving their visibility, our free [Reverse ASIN tool](/tools/amazon-reverse-asin/) lets you enter a competitor's ASIN and see the keyword set they're ranking for — a direct cross-check against the gaps SQP surfaces. This combination — SQP for your own conversion truth, reverse ASIN lookups for competitor keyword exposure — replaces guesswork with a closed loop: you know exactly which queries matter, exactly how you're performing on them relative to the category, and exactly who's capturing the share you're missing. ## Common SQP Mistakes to Avoid **Mistake 1: Reading absolute numbers instead of share.** A query with 40,000 impressions and only 200 clicks for your brand looks weak in isolation, but if your click share is 38% against a category where the next competitor holds 12%, you're actually dominating that query. Share against the category, not the raw count, is the number that matters. **Mistake 2: Acting on low-volume queries.** A query with 50 total category impressions in the reporting period doesn't have enough data to draw a reliable conclusion from a single-digit purchase count. Prioritize queries with meaningful category-wide volume first. **Mistake 3: Bidding up conversion-leak queries instead of fixing the listing.** Pattern 2 queries (high click share, low purchase share) are a listing problem, not a bidding problem. Increasing spend here without addressing the underlying gap just raises ACoS on traffic that was never going to convert at the current listing state. **Mistake 4: Treating SQP as a one-time report.** Search behavior, competitor listings, and your own conversion rate all shift quarter to quarter. Brands that re-pull and re-analyze SQP every reporting period catch new Pattern 1 opportunities before competitors do; brands that pulled it once in 2025 are working from a stale map. ## Frequently Asked Questions What is Amazon Search Query Performance (SQP)? Search Query Performance is an Amazon Brand Analytics report available to registered brand owners that shows, for every real customer search query, how your specific products performed relative to the entire category on that query — including impressions, impression share, clicks, click-through rate, cart adds, cart-add rate, purchases, purchase rate, and price. Unlike third-party keyword tools that estimate search volume from scraped data, SQP is Amazon's own first-party record of what customers actually typed and what they actually did next, broken down at the individual query level for your specific catalog. How is SQP different from Helium 10, Jungle Scout, or other keyword research tools? Third-party tools estimate keyword search volume and ranking using sampled data, reverse-engineered algorithms, and modeled conversion rates — useful for market-wide discovery but inherently approximate. SQP is ground-truth data pulled directly from Amazon's own search and purchase logs for the queries that actually led customers to your listings. The right approach uses both: third-party tools for broad market keyword discovery, SQP for validating which of those keywords convert for your specific brand and where you're structurally weak relative to competitors. How do I access Amazon Search Query Performance data? SQP is available inside Amazon Brand Analytics, which requires Brand Registry enrollment. Navigate to Brand Analytics > Search Query Performance in Seller Central or Vendor Central. The report can be filtered by ASIN or brand, by time period (weekly or quarterly), and exported as CSV for deeper analysis. What is a good cart-add rate or purchase rate to look for in SQP data? There is no universal benchmark because rates vary heavily by category and query type — that's exactly why SQP reports your rate alongside the category total for the same query. A query where your purchase share is meaningfully below your click share signals a listing-level conversion problem specific to that search intent. A query where your click share is low but purchase share is high signals a visibility and bidding gap. How do I turn SQP data into a PPC keyword strategy? Sort SQP by purchase count, then filter for queries where your purchase share meaningfully exceeds your click share — these are proven, underexposed terms worth aggressive Sponsored Products bidding. Flag high-category-volume queries with low or zero purchases attributed to you as demand you aren't capturing. Flag queries where click share is high but purchase share is low as listing problems that need fixing before more spend is justified. ## Related Reading - [Amazon Listing Optimization: How to Increase CTR, Conversion Rate & Organic Rankings in 2026](/blog/amazon-listing-optimization-high-intent-keywords-ctr-cvr-2026.html) - [Amazon Product Photography: How Your Main Image Impacts CTR](/blog/amazon-main-image-product-photography-ctr-2026.html) - [Amazon A+ Content: How to Increase Conversion Rate](/blog/amazon-a-plus-content-conversion-rate-2026.html) - [The 2026 Amazon PPC Playbook: Intent Mapping, GEO Signals & AI-Driven Bid Logic](/blog/amazon-ppc-playbook-intent-mapping-geo-signals-ai-bid-logic-2026.html) - [Keyword Intent for Amazon: How to Build a High-ROI Keyword Map](/blog/keyword-intent-amazon-roi-keyword-map.html) - [Building the Ultimate Amazon Keyword & PPC Stack](/blog/amazon-keyword-ppc-stack-2026.html) - [Data-Driven Amazon Growth: What CVR, TACOS, and PPC Intent Numbers Actually Tell You](/blog/data-driven-amazon-growth-cvr-tacos-ppc.html) ## Related Tools - [Amazon PPC Audit](/tools/amazon-ppc-audit/) - [Amazon PPC Calculator](/tools/amazon-ppc-calculator/) - [Amazon Reverse ASIN Keyword Tool](/tools/amazon-reverse-asin/) - [Amazon PPC Efficiency Score](/tools/amazon-ppc-efficiency-score/) ## FAQ ### What is Amazon Search Query Performance (SQP)? Search Query Performance is an Amazon Brand Analytics report available to registered brand owners that shows, for every real customer search query, how your specific products performed relative to the entire category on that query — including impressions, impression share, clicks, click-through rate, cart adds, cart-add rate, purchases, purchase rate, and price. Unlike third-party keyword tools that estimate search volume from scraped data, SQP is Amazon's own first-party record of what customers actually typed and what they actually did next, broken down at the individual query level for your specific catalog. ### How is SQP different from Helium 10, Jungle Scout, or other keyword research tools? Third-party tools estimate keyword search volume and ranking using sampled data, reverse-engineered algorithms, and modeled conversion rates — useful for market-wide discovery but inherently approximate. SQP is ground-truth data pulled directly from Amazon's own search and purchase logs for the queries that actually led customers to your listings. It shows your exact click share and purchase share against the category total for that specific query, which no third-party tool can replicate because they don't have access to Amazon's internal attribution data. The right approach uses both: third-party tools for broad market keyword discovery, SQP for validating which of those keywords convert for your specific brand and where you're structurally weak relative to competitors. ### How do I access Amazon Search Query Performance data? SQP is available inside Amazon Brand Analytics, which requires Brand Registry enrollment. Navigate to Brand Analytics > Search Query Performance in Seller Central or Vendor Central. The report can be filtered by ASIN or brand, by time period (weekly or quarterly), and exported as CSV for deeper analysis. Data typically has a reporting lag of several days and reflects the trailing period selected, not real-time search activity. ### What is a good cart-add rate or purchase rate to look for in SQP data? There is no universal benchmark because rates vary heavily by category and query type, which is exactly why SQP reports your rate alongside the category total for the same query — that comparison is the actionable number, not the absolute rate. A query where your purchase share is meaningfully below your click share (for example, you're capturing 22% of clicks on a query but only 9% of purchases) signals a listing-level conversion problem specific to that search intent — pricing, images, or reviews are losing the sale after the click. A query where your click share is low but purchase share is high signals a visibility and bidding gap: customers who do click convert well, but too few customers are seeing you for that query in the first place. ### How do I turn SQP data into a PPC keyword strategy? Start by sorting SQP by purchase count for your ASINs, then filter for queries where your purchase share meaningfully exceeds your click share — these are proven, high-converting terms that are likely underexposed and worth aggressive Sponsored Products bidding to capture more impression share. Separately, flag queries with high category-wide purchase volume but low or zero purchases attributed to you — these represent demand you aren't capturing at all, worth testing with new campaigns. Finally, flag queries where your click share is high but purchase share is low relative to the category — these need a listing fix (image, price, or copy) before more ad spend is justified, since higher bids on a conversion-broken query only inflates ACoS.