This page explains the Product Details drawer, which shows how AI answers describe and recommend one product. The drawer gives the full story of the product:
  • How the answer engines describe it.
  • The shopping questions in which it appears.
  • The exact conversations that named it.
  • Where the answer engines say that you can buy it.
The Products page tells you which products need attention. The drawer tells you why. Usually, it also tells you what to do.

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Open the Product Details drawer

The drawer opens for each product, from two places:
  • The Products page: click a product row.
  • A leaderboard: click a product in its ranked position.
The drawer opens over the current view. When you close it, you go back to the same position in the list. Thus, you can examine many products in one session.

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How the answer engines see the product

The drawer starts with the perception of the product. This is how the answer engines describe it in their own words. Why this is important: your product page says one thing, and the answer engine can say a different thing. Buyers hear the perception. For example, an answer engine can describe your premium model as “the budget option”. More money for ads does not solve this. The solution is in the sources that the answer engines read. The perception of a product works the same as the perception of a brand. Refer to Perceptions.

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The SWOT of the product

The drawer includes a SWOT for the product: the strengths, weaknesses, opportunities, and threats. Genezio makes the SWOT from what the answer engines say about the product. Why this is important: the SWOT changes a large quantity of AI text into four groups that you can act on:
  • Weaknesses tell you which objection your content must answer.
  • Threats usually name the competitor products that win against you.
  • Strengths tell you which messages already work. You can use them more.
Refer to SWOT Analysis for how Genezio makes a SWOT.

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The shopping queries of the product

The drawer shows the shopping queries in which the product appears. These are the real buying questions that showed the product. Why this is important: this is intent data. It tells you the buying moments that your product already owns. It also tells you, by their absence, the buying moments that it does not own. For example, you positioned a product for travel, but it never appears in travel queries. Then your positioning did not get to the answer engines.

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The query fanouts of the shopping queries

For these queries, the drawer shows the query fanouts. A query fanout is a follow-up question that an answer engine makes when it researches a buying question. Why this is important: buyers do not ask one question and stop. Query fanouts show the chain: the comparison, the price check, the “is it worth it” question. At each step of the chain, an answer engine can include your product or remove it. Each step is a target for content. Refer to Query Fanouts.

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The conversations that named the product

The drawer shows each conversation in which the product occurred. You can open the full transcript. These transcripts show the products clearly:
  • The transcript tags and highlights the products in the text. Thus, you can quickly find your products and the products of your competitors.
  • Tables show in the transcript. Thus, you read the comparison grids as the buyer saw them.
  • Maps show in the transcript. Thus, you can read answers about locations.
Why this is important: the metrics tell you that a product loses. The transcript shows you the sentence where it lost. You can see the exact moment when the answer engine recommended your product. Or you can see when it named your product and then selected a different one, with its reason in the text. Correct the cause that this reason shows. Refer to Conversations. The drawer shows the retailer offers that the answer engines give for the product, with prices and links. Why this is important: shopping answers do not only name a product. They send the buyer to a store. The purchase occurs in that store. This section also shows price problems first. For example, the answer engine can give an old price. Or it can send the buyer to a grey-market listing that has a lower price than your own store.

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The retailers that sell the product

Next to the offers, the drawer shows the retailers that sell the product, as the answer engines say. Why this is important: this list is a distribution check from the system that your buyers ask:
  • A missing retailer means that the answer engine does not know that the retailer sells your product.
  • An unexpected retailer means that a company lists your product and you do not know about it.
  • If your own direct-to-consumer store is not on the list, the answer engine sends each buyer to a third party.
For the view of the retailers across all products, refer to Merchants and Retailers.

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A practical sequence to diagnose a product

For a product with high visibility and low recommendation:
  1. Read the perception. Make sure that the answer engines describe the product as you intend.
  2. Read the SWOT weaknesses and threats. Find the objection and the competitor.
  3. Look at the shopping queries. Make sure that the product appears in the correct buying moments.
  4. Open two or three conversations. Find the sentence where the answer engine did not select the product.
  5. Look at the retailer offers. Find if a problem with price or availability is the real cause.
After step five, you usually have a specific brief that you can write, not only a metric.

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Availability of Product Details

Shopping and Product Visibility is an enterprise feature. The Genezio team enables it for a brand, as your contract specifies. There is no self-serve switch. Genezio detects the products automatically, from the conversations with answer engines only. There is no catalog upload, no feed, no crawl, and no manual entry.

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In the API

Get a product gives one product. What the answers say about a product gives its perception, and the sources of a product gives its sources.