How to Mine Amazon Search Query Performance for Hidden Keywords
Amazon's SQP report shows first-party impressions, clicks, and purchase share per search term. Export it weekly, filter click-vs-purchase share gaps, and turn hidden keywords into PPC wins.
TL;DR
Amazon's Search Query Performance report (Brand Analytics) shows first-party data for every search term: impressions, clicks, cart adds, and purchases, with your brand's share of each. Mining it means exporting the weekly report, filtering for gaps between click share and purchase share, and deploying what you find into PPC campaigns and listing copy. Here's the exact workflow and four frameworks for reading the data.
Key Takeaways
First-Party Truth: Unlike third-party estimation software, the SQP report provides first-party data across every funnel stage: impressions, clicks, add-to-carts, and final purchases.
The Click vs. Purchase Share Gap: Comparing click share against purchase share isolates listing conversion issues from traffic acquisition bottlenecks.
Uncovering Long-Tail Intent: Mining low-volume, high-converting queries exposes long-tail keywords that competitors overlook, lowering Advertising Cost of Sales (ACoS).
Listing and PPC Alignment: Integrating SQP search terms directly into Sponsored Products campaigns and backend search terms improves organic indexing and Ad Rank.
Automated Intelligence: Leveraging AI agents to continuously monitor SQP API feeds eliminates manual spreadsheet exports and optimizes bidding in real time.
The Shift from Third-Party Keyword Guesswork to First-Party Truth
For years, Amazon sellers relied almost exclusively on third-party keyword research software to estimate search volumes, search trends, and competitor keyword ranks. While third-party tools provide valuable directional data, they rely on scraped search results and reverse-engineered algorithms rather than actual transaction records.
The introduction of the Search Query Performance (SQP) report inside Amazon Brand Analytics changed keyword strategy fundamentally. SQP provides verified, first-party data detailing exactly what shoppers type into the search bar, how many times your listings appeared, how many clicks your brand received, and how many purchases were completed for each query.
Despite access to this goldmine of shopper intent, many brands treat the SQP report as an occasional diagnostic check rather than an active growth engine. Learning how to mine SQP systematically allows e-commerce brands to uncover hidden keywords, identify listing conversion bottlenecks, and outmaneuver category competitors.
Understanding the Search Query Performance Funnel
To mine hidden keywords effectively, brand managers must evaluate search queries across the full customer purchase journey rather than looking at search volume in isolation.
The SQP dashboard breaks query data down into four distinct funnel stages:
Query Impressions: The total number of times shoppers entered a search term and saw search results, alongside your brand's share of total impressions.
Clicks: The total clicks generated by the query, your brand's total click count, and your brand's percentage share of all clicks.
Add-to-Carts: The total number of times products were added to carts following that search query, along with your brand's specific add-to-cart count and share.
Purchases: The final conversions attributed to the query, detailing your brand's purchase count and purchase share relative to the entire marketplace.
Analyzing the relationships between these funnel stages exposes hidden search terms that standard advertising reports fail to capture.
How to Pull and Prepare SQP Data (Step by Step)
1. In Seller Central, go to Brands > Brand Analytics > Search Query Performance.
2. Select ASIN View or Brand View and set the reporting range (weekly for trend-spotting).
3. Export the CSV ("Generate Download").
4. Sort by Search Query Volume descending, then add two calculated columns: Click Share minus Purchase Share, and Purchase Share minus Click Share.
5. Filter each column for gaps above ~5 percentage points as those rows are your mining targets for the frameworks below.
4 Strategic Frameworks for Mining Hidden Keywords in SQP
Uncovering high-converting search terms requires applying structured analytical models to your raw Brand Analytics exports.
1. The Click Share vs. Purchase Share Gap
According to Velocity Sellers Amazon SQP Analysis, comparing click share against purchase share represents one of the most actionable metrics in Brand Analytics. Comparing these two percentages reveals clear operational signals:
Click Share > Purchase Share: Shoppers click your listing when searching this keyword, but choose a competitor at the purchase stage. This signals a conversion barrier, such as an uncompetitive price point, weak review sentiment, or missing feature callouts in your listing copy.
Purchase Share > Click Share: Shoppers who click your listing convert at a high rate, but your overall traffic share remains low. This indicates a traffic acquisition opportunity. Adding this search term into high-placement Sponsored Products campaigns captures immediate market share.
2. Mining High-Impression, Low-Click Terms for Listing Creative
Search terms that generate massive search query volume but low brand click share represent missed visibility. If your ASIN appears in search results thousands of times but captures less than 2% of total clicks, your main image, price point, or primary title phrasing is failing to generate click-through interest.
Mining these high-impression terms allows creative teams to update main images, add badges, or refine title hooks to capture lost shopper attention.
3. Uncovering High-Intent, Long-Tail Gems
Main search terms like "protein powder" carry intense ad competition and inflated Cost Per Click (CPC) rates. Mining SQP data often uncovers long-tail, highly specific customer phrases - such as "unflavored plant protein powder for baking" - that third-party tools frequently underestimate.
These hidden long-tail phrases reflect high buyer intent. Targeting these terms in exact-match PPC campaigns yields lower ACoS while strengthening organic keyword rank for broader root terms.
4. Identifying Competitor Defections at the Add-to-Cart Stage
When an ASIN exhibits strong click share and solid add-to-cart rates but experiences a sharp drop-off at final purchase, shoppers are abandoning carts at checkout. This pattern frequently points to pricing shocks, delayed delivery estimates compared to Prime competitors, or active coupon undercutting by rival brands.
Translating Search Query Data into Actionable PPC and Listing Optimization
Data mining delivers value only when insights are translated into active account changes. Once high-value search terms are identified in SQP, implement a systematic three-part deployment workflow:
Official documentation from the Amazon SP-API Changelog on SQP Reports confirms that direct API access to Search Query Performance metrics allows brands to automate query-level tracking across impressions, clicks, cart adds, and purchases.
Integrating SQP keyword insights across both paid advertising and organic listing attributes creates a compounding growth flywheel. Higher ad conversion rates on high-intent terms signal relevance to Amazon's search algorithm, driving organic ranking gains.
Automating SQP Mining with AI Agents and Real-Time Governance
Manually downloading, formatting, and analyzing weekly SQP CSV spreadsheets across large catalog portfolios introduces severe operational friction. By the time a human specialist processes spreadsheet data, market trends may have shifted or ad budgets may have been misallocated.
The alternative is automating the pipeline. Platforms like the Lumian Agent stream first-party Brand Analytics data via secure SP-API connections, processing query metrics continuously around the clock.
By combining autonomous data ingestion with human strategist oversight, automated agents identify emerging search terms, flag conversion gaps, and adjust keyword bids in real time. Exploring the full suite of Lumian Platform Features demonstrates how automated SQP keyword mining operates alongside dedicated account management.
To discover how integrated AI governance and keyword intelligence can expand your brand's market share, review real-world brand results across Lumian Case Studies or consult with an e-commerce specialist through Lumian Amazon Management Services.
Frequently Asked Questions
What is the main difference between the Amazon Search Term Report and the Search Query Performance Report?
The Sponsored Products Search Term Report shows performance data strictly for your paid ad campaigns. The Search Query Performance (SQP) report inside Brand Analytics provides first-party data across all search traffic - including both organic and paid impressions, clicks, add-to-carts, and purchases - at the total marketplace level.
How often is Search Query Performance data updated in Brand Analytics?
Amazon updates SQP data on weekly, monthly, and quarterly reporting cadences. Most growth teams analyze weekly data views to spot rapid shifts in consumer search trends and competitor market share.
Can I view Search Query Performance metrics for individual ASINs?
Yes. The SQP report offers two primary views: Brand View (aggregating search performance across all products in your brand registry) and ASIN View (allowing you to drill down into query performance for a specific parent or child SKU).
How can mining SQP keywords help lower my Amazon ACoS?
SQP mining reveals high-intent long-tail keywords where your listing converts at a high rate but competition is lower. Bidding on these validated, high-converting terms reduces wasted ad spend compared to bidding blindly on broad, expensive root keywords.
Do I need Brand Registry to access the Search Query Performance report?
Yes. Access to Search Query Performance and broader Brand Analytics dashboards requires active Amazon Brand Registry enrollment for your Seller Central or Vendor Central account.
Is Search Query Performance data free?
Yes. SQP is included with Brand Registry at no extra cost, which makes it notable that most brands pay for third-party keyword tools while leaving this first-party dataset unmined. The report covers only queries where your brand received impressions, so third-party tools still help for whitespace research on terms you don't yet rank for.
Co-Founder & CEO, Lumian
Robin Lobo is Co-Founder and CEO of Lumian. He built and sold a seven-figure eyeglasses brand on Amazon and spent several years on the client side of traditional agencies before founding Lumian, an AI-native Amazon agency backed by $3M led by Bowery Capital.
