Brand Analytics is the most valuable free data set Amazon gives Brand Registry sellers — and the one most brands either ignore or use superficially.
Eight separate reports sit inside Brand Analytics: Search Query Performance, Search Catalog Performance, Top Search Terms, Demographics, Market Basket Analysis, Item Comparison, Repeat Purchase Behavior, and Alternate Purchase Behavior. Each one reveals a different facet of how customers find, evaluate, buy, and repeat-purchase your products versus competitors. Most brands stop at Search Query Performance and miss the strategic insights that come from cross-referencing reports. The brand that knows its actual customer demographics, the products its customers buy with it, the competitors its customers consider, and the search queries driving its purchases — all from the same free dataset — operates with intelligence competitors don’t have. This guide walks through every report, the mining workflow that converts data to listing decisions, the funnel diagnostic, the Rufus implications, and the monthly review cadence that makes this data operational.
What is Amazon Brand Analytics and what’s actually inside it?
Amazon Brand Analytics is a data suite available exclusively to Brand Registry-enrolled sellers, providing access to multiple reports that surface customer behavior data and competitive intelligence that brands can’t access through any other Amazon tool. The reports update with varying frequency — some daily, some weekly, some monthly — and most data is available with a 1-7 day lag depending on the specific report.
Search terms driving impressions, clicks, purchases for your selected ASINs.
Aggregated brand-level data across your full catalog and all queries.
Top 1M Amazon search terms with click share and conversion share data.
Age, income, education, marital status, household composition of buyers.
Products customers buy together with yours — same order, session, or window.
Products customers viewed alongside yours during shopping sessions.
Customer retention, repurchase cycles, and cohort patterns over time.
What customers bought instead of your product when they didn’t buy yours.
The reports work together to paint a complete picture of search behavior, customer behavior, and competitive context. Most brands use only the Search Query Performance report (and superficially at that), missing the strategic value of cross-referencing reports to identify optimization patterns. The brands that mine all eight reports systematically gain insights competitors using single reports can’t access.
Brand Analytics access is free for Brand Registry sellers but requires actively logging in and querying the reports. Amazon doesn’t push notifications about data changes. Brands that don’t establish a monthly review workflow miss the data entirely even though it’s available to them.
The Search Query Performance report explained
The Search Query Performance report is the most-used Brand Analytics report and the highest-leverage starting point for most optimization work. The report shows the actual search queries driving impressions, clicks, and purchases for your ASINs — broken down by search query, time period, and product. Brands can see exactly which search terms generate the most search funnel activity for their products.
The key metrics in Search Query Performance
- Search query — the actual term shoppers typed
- Impressions — how many times your product was shown for that query
- Clicks — how many shoppers clicked through to your product
- Click share — your share of total clicks for that query (vs competitors)
- Cart adds — how many shoppers added to cart after clicking
- Purchase share — your share of total purchases attributable to that query
- Brand search rank — your rank position for the query
What the metrics reveal in combination
Individual metrics are useful, but the strategic insights come from combinations. High impressions but low click share suggest a listing optimization problem — shoppers see your product but choose competitors. High click share but low purchase share suggests a conversion problem on the product detail page. Strong purchase share on niche queries reveals where you have category authority worth building on. Each combination signals different optimization priorities.
How does Brand Analytics data feed Rufus optimization decisions?
Brand Analytics data feeds Rufus optimization decisions in two distinct ways. First, the search query patterns reveal the conversational intent Rufus interprets when shoppers ask product questions. Queries that show up in Brand Analytics are the actual phrases shoppers use to find products — and Rufus interprets shopper questions partly through pattern-matching to similar query language.
Second, the demographic and behavioral data in Brand Analytics reveals what kinds of customers buy your products, which informs Rufus surfacing decisions. When Rufus considers whether to recommend your product to a specific shopper, it weights brand-customer behavioral patterns. Brands that understand their customer demographics from Brand Analytics can optimize listings to attract those specific demographics more effectively, reinforcing the patterns Rufus already detects.
The Rufus-specific Brand Analytics use cases
- Identify Rufus-friendly query patterns — search queries that contain conversational language (“best X for Y,” “is X good for Z”) match Rufus query patterns
- Surface noun phrases for listing optimization — Brand Analytics queries reveal the noun phrases Rufus likely uses for semantic interpretation
- Map search query gaps — high-impression queries where you have low purchase share reveal Rufus opportunity zones
- Track Rufus surfacing changes — sudden changes in click share patterns can indicate Rufus is surfacing your products differently for specific queries
Mining search query data for listing improvements
The Search Query Performance report becomes operationally useful when it drives specific listing optimization decisions. Brands that systematically mine the report follow a structured workflow that converts search query insights into title, bullet point, description, and image improvements.
Export Search Query Performance data for your top products covering the past 90 days.
Identify your highest-traffic queries first — the volume baseline.
These queries signal listing optimization opportunity — you’re shown but chosen against.
These queries signal PDP conversion optimization opportunity.
Surface phrases used by customers buying competitors’ products.
Title additions, bullet point rewrites, image priority changes, A+ content updates.
Implement changes; track click share / purchase share changes over the following 30-60 days.
The disciplined mining process typically reveals 10-30 specific listing improvements per quarter for active brands. The improvements compound — fixing one query opportunity often improves rankings for adjacent related queries through Amazon’s semantic understanding.
The impression-to-click-to-purchase funnel inside Brand Analytics
The funnel data inside Brand Analytics — impressions → clicks → cart adds → purchases — reveals exactly where in the conversion path optimization opportunities exist. Each funnel step shows different signals and demands different optimization responses.
The funnel diagnostic frames optimization as a sequential problem — each weak stage requires different responses. Brands that try to optimize everything simultaneously without diagnosing where the funnel actually breaks waste effort on stages that don’t need fixing. The structured diagnostic concentrates effort where it matters.
Demographics data: who’s actually buying your products?
The Demographics report in Brand Analytics shows the actual demographic breakdown of customers purchasing your branded products — age ranges, income levels, education levels, marital status, and household composition. Most brands have assumptions about who their customers are; Brand Analytics reveals whether those assumptions match reality.
What demographic data enables
- Listing image strategy — lifestyle photography depicting the actual customer demographic outperforms aspirational photography for different demographics
- Bullet point and description language — vocabulary, examples, and references that resonate with actual demographic vs assumed demographic
- Ad targeting strategy — Sponsored Brands, Sponsored Display, and DSP audience targeting aligned with actual demographic patterns
- Product development direction — new product launches and variations aligned with the customer base actually buying
- Off-Amazon marketing — Meta Ads, Google Ads, TikTok ads, and other off-Amazon channels can be targeted to match Amazon demographic patterns
- Pricing strategy — premium pricing or value pricing aligned with the income demographics of actual buyers
Most brands discover at least one significant gap between their assumed target customer and their actual customer base when they first review Demographics data. Closing that gap often produces immediate conversion improvements as marketing finally aligns with reality.
Market basket analysis for variation and bundle decisions
The Market Basket Analysis report shows which products customers purchase together with yours — same order, same session, or extended timeframes. The data reveals natural product combinations that customers gravitate toward, which informs variation strategy (covered in detail in the parent-child variations guide), bundle creation, and cross-sell campaign design.
What market basket data reveals
- Natural product bundles — products customers already buy together that could be packaged as official bundles
- Variation expansion opportunities — sister products that should become variants of your parent listing
- Cross-sell campaign targets — products to target for Sponsored Display cross-sell campaigns
- Product line gaps — adjacent products customers buy from competitors that your brand could offer
- Subscribe & Save opportunities — consumable products purchased together that could become subscription bundles
- Inventory planning — products that move together inform replenishment and inventory positioning
The repeat purchase behavior report
The Repeat Purchase Behavior report shows customer retention and repurchase patterns over time. Brands can see what percentage of customers come back, how long the typical repurchase cycle runs, and which products drive the strongest retention. This data is foundational for customer lifetime value modeling and Subscribe & Save strategy.
The repeat purchase insights worth tracking
- 30/60/90-day repurchase rates — what percentage of buyers come back within standard timeframes
- Average repurchase cycle length — the typical days-between-purchases for repeat customers
- Repurchase value — whether repeat customers spend more or less than first purchases
- Cross-product repurchase patterns — whether customers come back for the same product or different products in your catalog
- Subscribe & Save penetration — what fraction of repeat purchases come through subscription vs one-time purchase
- Cohort retention curves — how retention evolves over 6, 12, 18 months for different customer cohorts
Item Comparison report: what shoppers consider against you
The Item Comparison report shows the products customers viewed alongside yours during shopping sessions — competitive products they considered before deciding what to buy. This data reveals your actual competitive set as defined by shopper behavior, which often differs from the competitive set brands assume.
What Item Comparison data enables
- Real competitive set identification — competitors customers actually compare against, not just industry-published competitors
- ASIN targeting for ads — Sponsored Display and DSP ASIN targeting based on actual comparison patterns
- Conquesting strategy — identify where your product wins or loses comparison battles to inform listing and pricing strategy
- Variation strategy — products customers compared but you don’t offer may signal variation expansion opportunities
- Pricing benchmarks — competitive prices from the actual comparison set
- Feature gap analysis — features competitors offer that you don’t, surfaced by what customers considered
The Ecom Profit Box
11 step-by-step PDF guides covering AI search, conversion, content strategy, and Amazon optimization.
Grab it free →Brand Analytics Reviews
Monthly Brand Analytics mining and competitive intelligence translated into listing + ad strategy decisions.
Book a strategy call →The competitive intelligence Brand Analytics quietly gives you
Beyond the explicit Brand Analytics reports, the combined data provides competitive intelligence brands often miss. The combination of Search Query Performance, Item Comparison, Demographics, and Market Basket Analysis effectively reveals competitor strategies through the lens of shopper behavior — what queries competitors win on, what products they’re winning over yours, who their customers are demographically, and what bundles their customers create.
The competitive intelligence framework
- Cross-reference top queries with Item Comparison data — identify queries where competitors win the comparison and reverse-engineer why
- Track Demographics changes over time — if your customer demographics shift, it often reflects competitive activity attracting your former customers or expanding into adjacent demographics
- Monitor Market Basket changes — new products appearing in your basket data may indicate competitive launches
- Watch Click Share trends — declining click share on queries you previously dominated suggests competitive listing improvements you should investigate
- Track Brand Search Rank changes — drops in brand search rank for category queries often precede broader competitive shifts
The competitive intelligence value compounds with consistent monthly review. Brands that establish a Brand Analytics review cadence build a longitudinal view of competitive dynamics that one-time reviews miss. The trends matter more than the snapshots.
Building a monthly Brand Analytics review workflow
The monthly Brand Analytics review workflow converts data access into operational decisions. Without a structured workflow, brands either ignore the data entirely or review it superficially without producing action items. The workflow below produces consistent insights and actionable outputs in 2-4 hours per month.
- Export Search Query Performance for top 20 products covering the prior month
- Compare metrics to previous month and 90-day rolling averages
- Identify queries showing significant changes (impressions, click share)
- Document queries flagged for investigation
- High-impression / low-click-share → listing improvements (title, image, price)
- High-click / low-purchase → PDP improvements (bullets, A+, video)
- New high-volume queries → evaluate whether listings address the new intent
- Build prioritized action list of 5-10 specific listing changes
- Review Demographics for any trend changes
- Review Item Comparison for new competitors entering your comparison set
- Review Market Basket for new product combinations
- Update competitive intelligence document with findings
- Implement prioritized listing changes from Week 2
- Set up tracking for metrics expected to change
- Document hypotheses about expected impact
- Plan next month’s review with any additional reports to add
Common Brand Analytics misreadings to avoid
The most common Brand Analytics misreading is focusing on absolute numbers rather than trends and shares. A query showing 10,000 impressions per month might sound impressive in isolation, but if your click share on that query dropped from 8% to 3% over the same period, the metric reveals a problem despite the absolute volume. Trends and shares matter more than snapshots.
The second common misreading is over-indexing on Search Query Performance while ignoring the other reports. Brand Analytics works as a system — Demographics informs how you interpret search queries, Market Basket informs variation strategy, Item Comparison reveals competitive context. Brands reading only Search Query Performance miss the cross-report insights that produce the most strategic value.
The third is treating low-volume queries as unimportant. Low-volume queries often have higher purchase intent and lower competitive density than high-volume queries. A query with 200 monthly impressions but 60% purchase share converts dramatically better than a query with 20,000 impressions but 1% purchase share. The economics favor the low-volume / high-intent queries despite the smaller absolute numbers.
The fourth is ignoring negative trends. Brands tracking Brand Analytics often celebrate positive trends and dismiss negative ones as anomalies. Negative trends in click share, purchase share, or brand search rank usually precede larger problems and deserve immediate investigation rather than dismissal.
The fifth is failing to act on insights. Brand Analytics review without operational follow-through produces no value. The discipline of converting each month’s findings into specific listing changes, advertising adjustments, or product decisions is what separates brands that benefit from Brand Analytics from brands that just access it.
The 8 Things to Remember About Brand Analytics
- Brand Analytics is a free Brand Registry data suite with 8 reports: Search Query Perf, Catalog Perf, Top Search Terms, Demographics, Market Basket, Item Comparison, Repeat Purchase, Alt Purchase
- Search Query Performance is the highest-leverage starting report but most brands stop there — cross-report synthesis is where the strategic value lives
- Brand Analytics data feeds Rufus optimization through search query patterns and demographic behavioral signals
- The mining workflow: export 90 days → sort impressions → flag high-imp/low-click and high-click/low-purchase → map to listing elements → execute and track 30-60 days
- The funnel diagnostic identifies whether the weak stage is impressions, click share, cart adds, or purchase share — each demands different optimization responses
- Demographics reveals who actually buys vs who you assume buys — most brands discover at least one significant mismatch on first review
- Market Basket informs variations, bundles, cross-sell campaigns; Item Comparison reveals real competitive set; Repeat Purchase feeds CLV modeling
- Monthly review workflow: Week 1 data export, Week 2 opportunity mapping, Week 3 cross-report synthesis, Week 4 execution — 2-4 hours per month total