Search Query Performance can show you that one in three people searching your top keyword clicks your listing and
fewer than one in ten buys it. Most brands see those numbers and increase their ad budget. That is exactly the wrong
response. The data is telling you the problem is the listing, not the traffic. Acting on that correctly is the
difference between a brand that scales and one that keeps spending its way into a plateau.
Amazon Brand Analytics is where that level of clarity lives. It is free, it runs on first-party Amazon data, and
most brand-registered sellers use roughly ten percent of what it can tell them. This article covers what the tool
actually contains, how to read each report as a decision, not just as a data point, and where the gaps are that
still require outside tools to fill.
What Amazon Brand Analytics Is and Who Can Access It
The Tool and Its Reports
Amazon Brand Analytics is available exclusively to sellers enrolled in Amazon Brand Registry. It lives inside Seller
Central under the Brands tab. The suite includes five core reports: Search Query Performance, Search Catalog
Performance, Market Basket Analysis, Item Comparison and Alternate Purchase Behavior, and Repeat Purchase Behavior.
A Demographics report is also available in most markets.
Why First-Party Data Changes the Calculation
Amazon introduced Brand Analytics as a beta program in 2019 and has expanded its depth significantly since. The data
is verified by Amazon, which means it reflects what shoppers actually typed, clicked, and purchased rather than what
third-party tools estimate they might do. That distinction matters more than most brands realize. Every keyword
research tool on the market is modeling demand. Brand Analytics is measuring it. Those are not the same thing, and
strategy built on measurement consistently outperforms strategy built on modeling.
Why Most Brands Only Use the Surface Layer
The Default Behavior
The most commonly accessed report is the Search Terms report, which shows which keywords shoppers used before
purchasing your product. Brands pull this data to build keyword lists or validate what they already know. That is
the floor, not the ceiling.
What the Data Is Actually For
The reports that move strategy require understanding what each metric measures at a funnel level, what a healthy
number looks like in your category, and what a specific problem number should trigger. Without that framework, the
data accumulates without producing decisions. After managing $300M in marketplace revenue across more than 100
consumer brands, our team treats Brand Analytics as a diagnostic system. Every report maps to a question. Every
question maps to an action.
Search Query Performance: Your Real Market Share, Not Your Perceived One
What It Measures
Search Query Performance shows how your ASINs perform for any given search query across the full funnel:
impressions, clicks, cart adds, and purchases, with your share of each step broken out against the total query
volume. You are not just seeing whether you rank. You are seeing how much of the available opportunity you are
capturing at each stage.
The Share-of-Voice Diagnostic
Most sellers use SQP to find keywords to target. The more precise use is diagnosing conversion quality. When you
pull SQP data for your top 10 to 20 queries and compare click share against purchase share, you get a specific
signal. High click share with low purchase share means one of two things: your listing is winning attention but
losing the buy decision, or your PPC is pulling the wrong buyer to the page. The action is different in each case.
SQP tells you which problem you have.
The Rank-3 to Rank-10 Decision
For any query where your ASIN ranks between position 3 and 10, you are visible but not dominant. SQP shows exactly
how far your purchase share sits below the top two positions. That gap has a specific size, and it maps to a
specific decision: a PPC push, a listing optimization pass focused on that query's intent, or both. Agencies
identify these opportunities systematically across the entire catalog. The decision is not "should we try to rank
higher." It is "here is the exact gap, here is what closing it requires, and here is what the revenue upside looks
like."
Search Catalog Performance: Identifying Exactly Where Buyers Are Dropping Off
Reading the Funnel as a Sequence
Search Catalog Performance focuses on what happens after a shopper encounters your specific listing. It breaks down
impressions, clicks, cart adds, and purchases at the ASIN level. The discipline is reading these as a sequence, not
as isolated numbers. The drop-off point tells you the category of problem. The size of the drop-off tells you the
urgency.
Three Drop-Off Points, Three Decisions
A low click-through rate from impressions to clicks points to the main image, the title, the price, or the
competitive context visible in the search row. The shopper saw the listing and chose not to engage. The decision is
a creative or pricing test.
A low add-to-cart rate from clicks points to the listing content itself: the bullets, the A+ content, the review
quality, or the perceived value. The shopper arrived and was not convinced. That is a conversion problem tied to
listing execution, something our team routinely diagnoses through Amazon account management at CMO. The decision is a listing audit with a specific hypothesis to test.
A low purchase rate from cart adds is the most nuanced signal. The shopper intended to buy and reconsidered. This
often reflects price sensitivity, active comparison shopping, or concern about delivery. Each of these problems has
a different fix, and SCP gives you the evidence to build the right one rather than guessing.
Market Basket Analysis: Three Strategies in One Report
What Co-Purchase Data Actually Produces
Market Basket Analysis shows which products Amazon shoppers purchase in the same session as your product. Agencies
treat it as a monthly priority because it generates three distinct, actionable outputs simultaneously rather than
requiring separate analysis to surface each one.
Bundle Architecture
If a meaningful share of your buyers also purchases a complementary product in the same session, that product is a
bundle candidate. Bundling as a single ASIN can increase average order value and improve contribution margin through
consolidated fulfillment fees. One referral fee. One pick-and-pack fee. The margin improvement on a well-constructed
bundle is immediate and compounding.
Sponsored Display Targeting
The ASINs appearing in your Market Basket data are prime Sponsored Display targets. You are reaching buyers who have
demonstrated behavioral affinity with your category through actual purchase behavior, not inferred interest. This
makes product targeting through Market Basket data consistently more efficient than interest-based targeting in most
categories.
Mapping Your Real Competitive Set
The products co-purchased with yours reveal what your buyer considers part of the same solution. That is often a
more accurate competitive landscape than BSR category ranking, and it frequently surfaces competitors that do not
appear in standard keyword searches. Knowing your real competitive set changes how you position, price, and target.
Item Comparison and Alternate Purchase Behavior: A Precise Map of Every Sale You Lost
What the Report Shows
Item Comparison and Alternate Purchase Behavior shows which products shoppers viewed alongside yours and which
product they purchased instead. It is the most direct measurement of competitive conversion loss available on
Amazon. It tells you who is winning the sale when you are not, and it removes the guesswork from competitive
strategy.
Turning Loss Data Into a Prioritized Fix List
If the same two or three competitors consistently appear as alternate purchases across your top ASINs, those
products represent your primary conversion threat. Auditing their listings against yours across price point, main
image, review count, and title structure tells you precisely where the buyer's decision is tipping. That audit
produces a specific improvement list with a measurable revenue case behind each item. The decision is not "we should
improve the listing." It is "here are the three specific gaps that are costing us conversions, ranked by likely
impact."
Repeat Purchase Behavior: The Loyalty Signal That Changes Long-Term Strategy
What Healthy Numbers Look Like
Repeat Purchase Behavior shows how many buyers made a second purchase within a defined time window. For consumable
categories including supplements, personal care, food and beverage, and pet products, this is one of the most
important account health indicators available. A repeat purchase rate of 25% or above within 180 days is a positive
signal. Below 15% warrants investigation into listing accuracy, product experience, Subscribe and Save enrollment,
and whether competitors are capturing the reorder on high-intent replenishment queries.
The Brand-Level View
For non-consumable categories, the relevant metric shifts from per-ASIN repeat rate to brand-level repeat behavior.
A buyer who purchases one product from your brand and never returns represents a missed compounding opportunity.
Repeat Purchase data at the brand level tells you whether your catalog is building customer relationships or just
processing individual transactions. The decision is whether a cross-sell campaign, an email sequence, or a product
expansion is the right response.
What Brand Analytics Cannot Tell You
Its Structural Limits
Brand Analytics only covers brand-registered products. ASINs not enrolled in Brand Registry are invisible in the
suite. Data thresholds mean that low-volume queries and newer ASINs are often suppressed because Amazon does not
display data below a minimum transaction count. This creates blind spots during launch phases when the data would be
most useful.
The Timing Problem
Brand Analytics is backward-looking by design. It tells you what worked after the fact, not what will win the next
ranking shift. Fast-moving categories require pairing Brand Analytics with real-time tools to bridge that lag. The
suite also does not show absolute search volumes. SQP gives you rank and share data, not raw demand estimates. For
total search volume, third-party keyword tools remain necessary alongside Brand Analytics, not instead of it. Used
together, the two data sources cover what the other cannot.
Building a Review Cadence That Produces Decisions, Not Just Observations
Weekly:
Search Query Performance for top 20 queries. Flag any query where click share increased but purchase share did not.
Monthly:
Market Basket Analysis for all active ASINs. Update bundle architecture and Sponsored Display targeting based on
current co-purchase data.
Quarterly:
Repeat Purchase Behavior and Demographics. Assess loyalty trends and validate that listing messaging matches the
actual buyer profile.
On demand:
Item Comparison and Alternate Purchase Behavior after any significant listing change, price adjustment, or new
competitive entry.
The cadence only works if each review ends with a documented decision. Observation without action is the most common
way Brand Analytics fails to produce results, and it is entirely avoidable. The framework our team applies through Amazon account management at CMO
treats every data point as an input to a decision, not a number to be noted and filed.
Final Thoughts
Most brands have access to Brand Analytics and use a fraction of it. The ones that go deeper find a different
picture of their business than standard dashboard metrics were showing them. Amazon gives you this data for free.
Using it well is not about access. It is about knowing what each report is supposed to produce, reading it as a
sequence of funnel decisions, and acting on what it tells you before the window closes.
That is the difference between a brand that uses Amazon and a brand that understands it.
If you are not using Brand Analytics to its full capacity, you are making strategy decisions with incomplete
information.
Book a consultation with our team
to find out what your data is telling you that your current process is missing.
What the Science of Amazon Brand Analytics Actually Answers
What is Amazon Brand Analytics and who can access it?
It is a free reporting suite inside Seller Central available exclusively to brand-registered sellers. Access
requires active Brand Registry enrollment and the correct account permissions.
What is Search Query Performance and how is it different from keyword tools?
SQP shows your impression, click, and purchase share for a query using Amazon's first-party data, making it more
reliable than third-party estimates that model demand rather than measure it.
What is the best immediate use of Market Basket Analysis?
Pull your top 10 ASINs and identify the top three co-purchased products for each. Then ask whether a bundle, a
Sponsored Display targeting campaign, or a competitive repositioning is the right response.
What is a healthy repeat purchase rate on Amazon?
For consumables, 25% or above within 180 days is a strong indicator. Below 15% signals a problem worth investigating
in product experience, Subscribe and Save enrollment, or competitive reorder capture.
How often should Amazon Brand Analytics be reviewed?
Search Query Performance weekly, Market Basket Analysis monthly, Repeat Purchase Behavior quarterly, and Item
Comparison on demand after any meaningful listing or pricing change.
