Amazon Brand Analytics: turn reports into commercial decisions

Amazon Brand Analytics is a Seller Central suite that organizes aggregate customer search and purchase data into several dashboards. Start with 1 commercial question, choose the dashboard that isolates the relevant customer-journey stage, and cross-check price, availability, ads, catalog, and contribution before assigning the repair. The challenge is rarely finding another export.

By Naeela

What is Amazon Brand Analytics?

Amazon Brand Analytics is a collection of dashboards inside Seller Central. Amazon's current Brand Analytics page says the suite uses aggregate customer search and purchase data to help brands understand the customer journey, search behavior, loyalty, and purchase patterns.

The current public suite spans 4 families: Customer Journey Analytics, Customer Loyalty Analytics, Search Analytics, and Consumer Behavior Analytics. Within those families, Amazon names dashboards such as Search Catalog Performance, Search Query Performance, Top Search Terms, Repeat Purchase Behavior, Demographics, Market Basket Analysis, and customer-loyalty views.

That list is useful, but it is not a workflow. Picture a meeting with 11 tabs and 47 highlighted cells after somebody exported every dashboard. Everybody has an observation. Nobody owns a decision.

Brand Analytics describes the customer journey. It does not assign the owner of the repair.

Access generally requires a Professional selling account and a Brand Representative role for a brand enrolled in Brand Registry. If the access problem is the current bottleneck, resolve the eligibility and role questions in the Amazon Brand Registry guide before designing a reporting process.

Which dashboard should you use?

Choose from the question, not the report menu.

Use Search Catalog Performance for product-level funnel loss

Search Catalog Performance helps when the question begins with a product or catalog: “Where do customers drop out after our products appear in search?”

Amazon's guide describes impressions, clicks, cart adds, purchases, rates, and median price across the search journey. That sequence lets a team locate the stage that weakened.

If impressions fall while clicks and purchases remain stable, investigate discoverability, query coverage, indexation, stock, category placement, and advertising reach. If impressions hold but clicks fall, inspect the search-result offer: main image, displayed title, price, rating, delivery promise, coupon, and competitive fit. If clicks hold but purchases fall, move deeper into the detail page, reviews, variation, offer, and fulfillment experience.

Do not hand every problem to the listing team. If click rate falls after an 8% price increase, a copy rewrite is not the first assumption to test.

Use Search Query Performance for query-level competition

Search Query Performance helps when the question starts with a customer phrase: “For this query, where is our brand losing share between impression and purchase?”

Amazon says the dashboard includes query volume and brand performance across impressions, clicks, cart adds, and purchases, with share measures against total query activity. It can be viewed at brand or ASIN level where available.

The useful move is to compare adjacent stages. Strong impression share with weak click share points toward the visible search-result offer. Strong click share with weak purchase share points toward the detail page, price, delivery, availability, reviews, variation, or an intent mismatch. Weak impression share may point toward relevance, rank, assortment, paid coverage, or inventory.

A falling share metric is a symptom, not a department.

Query share is also not the same as total market share. It describes a defined search journey inside Amazon's dataset. Customers can arrive through browse, external traffic, branded navigation, recommendations, or other routes. Keep the claim inside the metric's boundary.

Use Top Search Terms for broad demand and competitor context

Top Search Terms starts with Amazon-wide search behavior rather than only your brand's catalog. Amazon describes search frequency rank plus top-clicked products, categories, or brands and their click and conversion shares.

Use it to find important language, rising product themes, and the products that win attention for a query. Then test whether the term belongs in your catalog, content, or advertising plan.

Do not treat a popular query as a mandate. Volume without product fit can attract the wrong click. A high conversion-share competitor may win because of price, reviews, availability, brand familiarity, pack size, or a product difference you cannot copy with keywords.

When the query belongs in your product strategy, the Amazon SEO guide explains how to move from diagnosis into content, attributes, and offer decisions.

Use Repeat Purchase Behavior for retention questions

Repeat Purchase Behavior helps answer whether customers return to buy the same product and how repeat sales contribute to the business. Amazon's current page names repeat orders, repeat sales, and repeat customers among the available views.

The obvious interpretation is that more repeat purchase is better. The commercial answer is narrower. Repeat behavior matters when the product should be replenished, the timing makes sense, and the repeat order remains profitable.

A subscription-like consumable and a durable kitchen appliance should not share the same repeat-purchase expectation. Nor should a higher repeat rate be celebrated if it came from a promotion that erased contribution.

Use Market Basket Analysis for cross-sell and bundle hypotheses

Market Basket Analysis shows products customers often buy together. It can reveal useful adjacency: a refill beside a dispenser, a replacement part beside the core item, or two products that solve the same occasion.

Treat the pair as a hypothesis. Check whether both products are available in the same marketplaces, whether the combined economics work, whether the pairing is operationally feasible, and whether the association persists beyond a promotion.

Then choose the smallest test: a virtual bundle, a detail-page cross-sell, a Sponsored Brands landing experience, or coordinated inventory planning. A surprising pair makes a good conversation. It becomes a decision only when the brand can act on it.

Use loyalty and demographic views for audience strategy

Customer Loyalty Analytics and Demographics can help frame segments, re-engagement, tailored promotions, and audience assumptions. Amazon says its Demographics view is US-only and requires at least 100 unique customers for an ASIN in the selected period. These are aggregate views, not a license to invent an intimate customer profile.

Use them to challenge a hypothesis. If the internal team imagines a narrow customer but the aggregate view points elsewhere, investigate creative, assortment, media, and product positioning. Do not jump from an age or income band to a stereotype.

The right dashboard is the one that narrows the next decision.

How do you diagnose a weak Search Query Performance result?

Move through 4 stages in order.

1. Is the product earning impressions?

Low impression share can come from relevance, rank, paid coverage, category placement, availability, or assortment. Check whether the product was buyable throughout the period. Mark suppressed listings, stock constraints, lost Buy Box periods, and variation changes.

Then inspect the query itself. A broad term may describe a large category where the product is only a partial fit. Trying to force relevance can produce impressions that never become profitable sales.

2. Are impressions becoming clicks?

When impressions are present but click share is weak, the search-result offer is losing attention. Review the main image, displayed title, price, coupon, rating, review count, delivery date, and competing pack sizes.

Compare like with like. If your 24-pack appears beside 12-packs, a raw price comparison will mislead. If a competitor has same-day delivery and yours arrives in 4 days, copy edits may not repair the gap.

3. Are clicks becoming cart adds?

A click means the search-result promise earned attention. A weak cart-add step suggests the detail-page experience or offer did not sustain it.

Inspect images, Item Highlights, bullets, A+ Content, variation logic, specifications, claims, price, promotions, reviews, and availability. Read recent questions and return reasons where available. Ask whether the product actually solves the query's job.

4. Are cart adds becoming purchases?

When cart adds are healthy but purchase share is weak, inspect final price, delivery, stock, Buy Box, checkout friction, and competitive switching. Cart behavior can also span time, so avoid treating a one-week change as a complete causal story.

Connect the purchase step to contribution. A discount can improve conversion while making the order worse for the business. Profit is the constraint; scale is the outcome.

What should you cross-check before changing the account?

Use at least 3 surrounding facts before assigning a repair.

First, price and promotion. Record the effective price, coupon, deal, and meaningful competitor changes across the same period.

Second, availability and delivery. Mark stock cover, suppression, lost Buy Box, delivery promise, and variation issues. For example, search performance data collected during a 6-day stock constraint should not become the evergreen benchmark.

Third, advertising and offer context. Check whether spend, branded defense, placements, targeting, or campaign structure changed. Search Query Performance includes customer behavior beyond one campaign, so do not assume a PPC edit owns the whole movement.

For a broader business decline, use the Amazon sales-dropped diagnostic to check traffic, conversion, inventory, price, catalog, and account health together.

Add unit economics before approving the action. The report can tell you that purchases changed. It cannot tell you whether another purchase at the current price, fee load, return rate, and ad cost creates contribution.

How do you run a weekly Brand Analytics review?

Keep one page with 5 fields.

1. Signal: what changed, in which dashboard, for which ASIN or query, over what period?
2. Hypothesis: what mechanism could explain it?
3. Action: what single change or investigation follows?
4. Owner: who decides and who executes?
5. Review date: when will the team evaluate the expected effect?

Suppose purchase share fell for a priority query. The weak version says, “Improve conversion.” The useful version says, “Purchase share fell while click share held. Price rose 6% and delivery moved from 2 days to 5. The retail owner will restore the tested price for 7 days on in-stock ASINs, while operations investigates the delivery promise. Review contribution and purchase share next Thursday.”

That record creates accountability. It also protects teams from rewriting the story after the result appears.

Limit the weekly review to the few queries, ASINs, and customer behaviors that can change a material decision. Rotate the focus when a question closes. A standing dashboard should earn its place by improving decisions, not by producing familiar slides.

What does Brand Analytics not tell you?

It does not tell you why a number moved. It narrows the diagnosis.

It does not show every customer journey outside Amazon or every route inside Amazon. It reports within defined dashboards, scopes, and aggregation rules.

It does not calculate your complete contribution profit. Fees, landed cost, returns, promotions, agency cost, and cash consequences require additional data.

It does not make an old report current. Independent guides often preserve retired names, fields, or navigation. Amazon's own public documentation and the live account should control current availability.

It does not replace an experiment. A correlation between a content edit and a share increase is useful, but seasonality, competition, advertising, stock, and price may also have changed.

No single dashboard gets to own a whole-business conclusion.

Where does ALFI fit, and where does it not?

ALFI is a fit for brands generating $1M+ in annual revenue that need Brand Analytics connected to advertising, listings, price, inventory, reviews, catalog, and profit. Our role is not to admire the reports. It is to decide which signal matters, assign the repair, and stay accountable when the outcome is messy.

We would refuse a broad dashboard tour that ends with dozens of observations and no owner. That refusal can shrink the reporting scope and reduce billable analysis. It protects the client's attention and keeps the work attached to a decision.

Choose an analytics engineer or software provider if the job is API retrieval, warehousing, or dashboard construction. Keep the process in-house if your retail, advertising, finance, and catalog teams already share definitions and authority. Early sellers with thin data may get more value from clean listings, inventory control, and basic advertising discipline.

Full service is not a longer menu. It is the condition for accountability when a search signal may belong to price, stock, ads, creative, or catalog.

What to do this week

Choose 1 priority ASIN and 3 commercially important queries. Export Search Catalog Performance and Search Query Performance for a stable period. Mark price, promotion, stock, delivery, advertising, and catalog changes on the same timeline.

Find the first stage where the journey weakens: impression, click, cart add, or purchase. Write one hypothesis, one action, one owner, and one review date. Do not fix 5 variables at once.

After 30 days, ask whether the action changed the customer signal and the commercial outcome. If it did, preserve the learning. If it did not, revise the mechanism rather than decorating the dashboard.

For established brands that need those decisions connected across the Amazon operation, see how ALFI works. Bring the report that is causing the argument. We will start by deciding what the business needs to know.