Brand Analytics Product Gap Analysis for Amazon Launches

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Where whitespace hides in your own search data

Most brands use Brand Analytics for keyword work: they find terms, write them into listings and bid on them. That's useful, but the dashboard Amazon describes as a way to spot catalog expansion opportunities usually gets ignored for product decisions. According to Amazon's Brand Analytics overview, the Search Query Performance dashboard reports query volume, impressions, clicks, cart adds and purchases "to spot ways to expand your catalog." The same Brand Analytics page quotes thefitguy's co-founder saying quarterly sales jumped over 40% after the brand optimised around the Search Terms and Search Query Performance dashboards.

The gap signal is simple. When your brand has a high impression share and a low click share on a query, Amazon thinks you're relevant, but shoppers can see you don't sell the exact thing they typed. That's different from a listing problem. A listing problem shows up as a click-share drop on queries where you do sell the matching product.

Pulling the right reports

You need a Professional selling account and Brand Representative access to a brand enrolled in Brand Registry. Once you have that, the workflow uses three dashboards together.

1. Export Search Query Performance (Brand View): Pull the last three months at the monthly level so one-off spikes don't mislead you. As Amazon's Brand Analytics guide explains, you can switch between Brand View and ASIN View, and search query volume shows how often customers used a keyword in a given time frame.
2. Filter for the gap pattern: Keep queries where your impression share is above your category average and your click share is less than half of it. Then remove anything that describes a product you already sell. What's left is a list of adjacent intents.
3. Look up each surviving query in Top Search Terms: This dashboard shows the three top-clicked products for each search term, along with their click share and conversion share. Search Query Performance can't tell you who is winning the click. Top Search Terms can.
4. Check Market Basket Analysis: Set the products filter to include other brands. If a competitor's product from your gap list keeps showing up in your customers' baskets, that's proof the adjacency exists in real buying behaviour as well as in search.

Save the export. You'll come back to the total purchase counts when you size revenue.

Is the incumbent actually beatable?

A high-volume query with a strong incumbent is a market. A high-volume query with a weak incumbent is an opportunity. Open the top three clicked ASINs from Top Search Terms and score each one on four signals.

• Review depth: Under a few hundred reviews on a query with real volume suggests the leader got there through timing or ad spend rather than loyalty.
• Star rating: Anything under 4.2 in supplements or beauty leaves room for a better-formulated product to take conversion share.
• Click share vs conversion share: A product with a high click share and a much lower conversion share has a strong main image and a listing that doesn't close the sale. That's the easiest kind of leader to displace.
• Listing quality: Look for thin bullet points, missing A+ content, few images and no video. You can fix all of these on day one.

When at least two of the three top-clicked ASINs fail on two or more signals, move the query to the shortlist.

Cross-referencing Opportunity Explorer and semantic query clusters

Single queries are noisy. Amazon search matches on meaning, so you should plan around groups of related queries rather than one keyword. Amazon's own research on semantic product search explains why exact-word matching falls short: it misses synonyms and hypernyms and breaks on variants like "woman" vs "women." In practice, "magnesium gummies for sleep," "sleep magnesium chewable" and "magnesium glycinate gummy" are one buying intent. If you size them separately, you undercount the demand.

Product Opportunity Explorer already groups queries this way. According to Amazon's Opportunity Explorer page, niches are built by grouping search terms based on the products customers view or buy after searching. The tool also surfaces "unmet demand" opportunities: groups of similar search terms that convert below the benchmark, which suggests high demand and weak supply. Put your gap queries into Opportunity Explorer and see which niche each one maps to.

What to look for in the niche view

Amazon's guidance points to rising yearly search volume paired with a high average out-of-stock rate, a low share of Prime offers, or few products running Sponsored Products ads. Amazon's Opportunity Explorer walkthrough notes that the Insights feature shows how many products are in the top 80% of clicks within a niche. If only a handful of ASINs take most of the clicks and they're the weak listings you scored earlier, you've found a growing, underserved cluster. Then use Customer Review Insights to see which review themes pull down star ratings. Those complaints are your product spec.

For a deeper scoring method, our niche validation scoring guide sets weights for each Opportunity Explorer metric. Our guide to semantic search listings covers how to write for the cluster once you launch.

Pre-launch economics

A gap is only worth filling if the numbers work after cannibalization. Run these three calculations before you request a sample.

Addressable revenue

Search Query Performance shows total purchases across all brands for each query. Add up those totals for every query in the cluster, multiply by the expected selling price, and you have the monthly purchase value flowing through that cluster. Then apply a realistic share. Keep that share conservative for a new ASIN entering a contested cluster, and raise it only where the top-clicked incumbents are as weak as the ones you scored.

Cannibalization risk

Switch Search Query Performance to ASIN View and list the queries where your existing products already convert. Wherever those queries overlap with the new cluster, estimate how many of your current purchases would move to the new product. Net incremental revenue is projected new-ASIN revenue minus the revenue that moves over. If more than a third of the new product's sales would come from your own catalog, you're paying launch costs to move revenue from one ASIN to another.

Standalone ASIN or variation?

• Choose a variation: When the new product matches the parent's search intent and differs by an attribute shoppers compare side by side, such as size, scent, shade or count. It inherits the parent's reviews and ranking from day one.
• Choose a standalone ASIN: When it targets a separate query cluster, has a different formula or use case, or might pick up review complaints (taste, texture) that would drag down the parent's rating.

Supplements, home and beauty in practice

These examples are illustrative. They show how each step changes the decision.

Supplements: capsules to gummies

A magnesium glycinate capsule brand sees a strong impression share and almost no click share on a high-volume gummy query cluster. The top three gummies have 3.9 to 4.1 stars and under 500 reviews, and Customer Review Insights shows complaints about sugar and taste. Multiply the cluster's total monthly purchases by the expected gummy price and the purchase value comes in well above the brand's launch threshold. Even a modest purchase share against these incumbents covers the launch budget. ASIN View shows the capsule converts on only two of the cluster's queries, so cannibalization is low. The decision is a standalone ASIN, because a gummy has a different supplement facts panel, serving size and review profile. This is the kind of format extension behind our supplement brand case study.

Home: diffusers to refills

A reed diffuser brand finds that Market Basket Analysis keeps pairing its products with a competitor's refill oil. Search Query Performance confirms high impressions and almost no clicks on refill queries. Cannibalization is close to zero because refills are a repeat purchase that comes after the diffuser. The decision is a variation under the existing parent, with refills in each scent, so the new listing inherits reviews and catches buyers on the detail page. Our home fragrance case study shows how catalog depth adds up in this category.

Beauty: serum to eye cream

A vitamin C serum brand maps a growing "vitamin c eye cream" cluster in Opportunity Explorer, and the leaders are rated 3.6 to 3.9. The catch is that the cluster shares head terms with the serum, and ASIN View shows the serum converting on "vitamin c brightening" queries that both products would compete for. The model shows a real share of the eye cream's projected sales coming from the serum, but net incremental revenue stays clearly positive. The decision is a standalone ASIN with its own keyword set and negative-keyword fencing in PPC, so the two products don't bid against each other.

Your first step this week: export three months of Search Query Performance in Brand View, filter for high impression share and low click share, and check the top-clicked ASINs for your 10 biggest gaps. If two or more look beatable, that's your launch shortlist.

Your Brand Analytics data already shows the adjacent products your customers want; the hard part is turning query gaps into launches that add revenue rather than move it between your own ASINs. Hyperzon's product research team builds the cluster map, revenue model and catalog plan so your next launch adds net sales from week one. Get a free Amazon audit.

Article was originally published on 11 November, 2026