from search impressions to clicks, cart adds, and purchases. That distinction helps a brand identify the actual constraint behind slow growth. Amazon introduced the dashboard to answer a direct question: which queries led customers to a brand’s products? The report includes brand- and ASIN-level performance, making it useful for both catalog analysis and listing decisions.
1. Build four rates from the funnel
For each material query, calculate or compare these stages:
- Impression share: brand impressions divided by total query impressions
- Click share: brand clicks divided by total query clicks
- Cart-add share: brand cart adds divided by total query cart adds
- Purchase share: brand purchases divided by total query purchases
The shares reveal where the brand gains or loses ground. Suppose an ASIN has 8% impression share, 6% click share, 5% cart-add share, and 3% purchase share. Visibility is not the only problem. The brand loses share after every stage, with the largest commercial gap appearing between cart and purchase. Now consider a second query with 2% impression share, 5% click share, and 6% purchase share. The ASIN converts well when customers find it. The priority is qualified visibility through indexing, organic ranking, or advertising rather than a full listing rewrite.
2. Match the gap to the action
Funnel gap What to inspect Likely action Low impression share Indexing, organic rank, ad coverage Add relevant listing language and expand controlled keyword targeting Click share below impression share Main image, title, price, rating, offer Improve search-result appeal and test the highest- impact element Cart share below click share Content clarity, variation choice, delivery promise Resolve objections on the detail page Purchase share below cart share Price, coupon, stock, delivery, checkout friction Check offer competitiveness and fulfillment reliability Avoid changing every element at once. If the click stage is weak, start with the main image or title. If the purchase stage is weak, changing backend search terms will not address the immediate problem.
3. Prioritize queries by commercial impact
A small share gap on a high-volume query can matter more than a large gap on a low-volume term. Create a simple opportunity score: Amazon Brand Management Blog Collection 6 Opportunity units = Total query purchases x (Target purchase share - Current purchase share) If a query generates 2,000 purchases and the brand wants to move from 3% to 5% purchase share, the theoretical gap is 40 orders. This is not a forecast because competitors and customer behavior will change. It is a consistent way to rank opportunities. Review high-priority queries every four weeks, using the same reporting window. Separate branded and non-branded terms because their expected click and purchase behavior differs. Also annotate price changes, coupons, stockouts, review shifts, and major bid changes so the team does not mistake correlation for cause. Search Query Performance works best as a decision system, not a keyword export. Identify the weak funnel stage, estimate the opportunity, make one relevant change, and compare the next equivalent period. That process turns Amazon search data into accountable growth work.
4. Create an action queue from the report
Limit each ASIN to three priority query actions per review cycle. Record the query, current funnel shares, diagnosed gap, proposed change, owner, and review date. A useful entry might read: “Click share trails impression share by 2.4 points; test a clearer main image and review after four full weeks.” This structure stops high-volume exports from becoming unmanageable task lists. It also separates observed data from the team’s hypothesis, which is essential when a later result does not match the expected outcome.
