How to identify products shoppers want but you do not sell from on-site AI conversations

An ecommerce team can identify products or features shoppers want but do not currently sell by treating on-site AI conversations as demand research, not just support transcripts. Capture the shopper’s exact request, classify its intent, connect it to products and outcomes, then separate a true catalog gap from a product that exists but is hard to find or poorly described. Repeated, high-intent requests are candidates for merchandising validation—not automatic proof of demand.

What an on-site AI conversation can reveal

An on-site AI conversation can expose demand that clicks and purchases cannot: the use case a shopper has in mind, the constraint your catalog does not meet, and the reason an otherwise suitable product gets rejected.

A shopper may ask for “a jacket that works in heavy rain but packs small” or “a moisturizer without fragrance for reactive skin.” Those questions contain more than keywords. They describe a job to be done, a combination of attributes, and sometimes a product category the shopper expects you to carry.

Look for four kinds of signals:

Signal in the conversation	What it may indicate
“Do you sell anything that…”	A possible missing product or attribute combination
“Which product works with…”	A compatibility, accessory, or bundle opportunity
“I need X, but none of these…”	A catalog, filtering, or recommendation gap
“Why don’t you have…”	Explicit unmet demand, though it still needs validation

The question alone is not enough. A shopper might be describing a niche need, misunderstanding your assortment, or asking for something your business cannot profitably provide. Use conversations to form hypotheses, then test those hypotheses against behavior and commercial constraints.

How to turn conversations into product-gap signals

To find products shoppers want but do not sell, classify each conversation consistently and aggregate requests by need rather than by wording.

Start with a useful record for every relevant interaction:

Shopper need: what the person is trying to accomplish
Requested product or feature: the missing item, attribute, size, material, compatibility, or service
Existing alternatives shown: products the agent recommended, if any
Conversation stage: discovery, comparison, objection, or post-purchase
Outcome: product click, add to cart, purchase, escalation, or exit
Reason for rejection: price, fit, availability, specification, delivery, or an explicit missing feature

Normalize different expressions into one demand theme. “Waterproof daypack,” “rainproof small backpack,” and “a pack for wet commutes” may represent one request. Preserve the original language as well: it helps merchandising and product teams understand how shoppers describe the need.

Then rank themes using evidence rather than raw conversation volume. A practical prioritization model asks:

Frequency: How often does the need appear, across distinct shoppers and time periods?
Specificity: Is the request concrete enough to guide sourcing or product development?
Commercial intent: Did shoppers compare products, click recommendations, add items to cart, or ask about buying?
Current failure: Did the agent fail to find a suitable product, or did the shopper reject every available option?
Strategic fit: Can the brand serve the need with its capabilities, price position, and customer base?

A repeated question with strong buying behavior is more actionable than a high-volume question about shipping or returns. Those operational questions matter, but they do not usually identify a new product category.

Separate a missing product from a missing answer

Many apparent product gaps are information or discovery gaps. Before adding an item to the roadmap, check whether the requested product already exists in the catalog but is hidden by incomplete data, weak filtering, or an unclear product page.

Use this diagnostic:

What the conversation shows	Likely issue	First action
The product exists, but the agent cannot identify it	Product data or retrieval problem	Improve attributes, synonyms, and catalog mapping
The product is recommended, but shoppers ask the same basic question	Content gap	Add clear specifications, use cases, or comparison guidance
Several products fit, but shoppers cannot choose	Merchandising gap	Improve recommendations, bundles, or comparison pages
No current product meets the stated requirement	Potential assortment gap	Test demand before sourcing or developing
The request combines two existing products	Bundle or accessory opportunity	Test a kit, cross-sell, or guided path

This distinction prevents an expensive response to a cheap problem. A new “travel-friendly” product may not be necessary if the existing products lack dimensions, weight, packability, or relevant use-case labels.

Review the agent’s source data as part of the analysis. An AI answer is only as useful as the product information it can retrieve. Missing or contradictory specifications can make a strong product look absent, unsuitable, or difficult to compare.

Validate demand before changing the assortment

Conversation patterns should produce a testable demand hypothesis, not an immediate purchase order. Validate the strongest themes with signals outside the conversation itself.

Useful checks include:

On-site search queries with no results or weak results
Product-page exits after shoppers ask about the missing attribute
Clicks on “similar” or substitute recommendations
Add-to-cart and purchase rates for shoppers with adjacent needs
Customer-support tickets, reviews, returns, and survey responses
Competitor assortment and pricing
Preorders, waitlists, landing-page tests, or a limited-batch experiment

Match the validation method to the uncertainty. If you are unsure whether shoppers understand the need, improve the product page and recommendation path first. If you know the need is real but not whether shoppers will pay, test an offer or waitlist. If the request is clear and repeated but operationally complex, interview a sample of shoppers before committing to inventory.

Keep a qualitative sample alongside the aggregate count. Five nearly identical conversations from shoppers who reached the same product page can reveal a sharper product requirement than hundreds of vague requests. Remove unnecessary personal information and restrict analysis to what the merchandising decision requires.

Turn findings into merchandising actions

The right response to a conversation-derived gap may be a new product, a product feature, better assortment structure, or a clearer buying journey.

A simple decision brief can keep teams aligned:

Demand theme: the normalized need and representative shopper language
Evidence: conversation pattern, behavior, and supporting customer data
Current response: what the site and AI agent recommend today
Failure point: where the current assortment or experience breaks down
Proposed test: content change, recommendation rule, bundle, waitlist, or new SKU
Success measure: qualified clicks, add-to-cart rate, conversion, revenue, or validated signups
Owner and review date: who will act and when the result will be assessed

For example, a recurring request for a product that meets two competing requirements might lead to three different actions: add an attribute filter, create a comparison guide, or source a new item. The conversation identifies the tension; the test determines which intervention deserves investment.

Feed the result back into the AI experience. If a new product is added, update the catalog data and teach the agent when to recommend it. If the answer was a content problem, make the information available on the product page as well as in the conversation. The goal is not merely to collect requests; it is to make the next shopper’s decision easier.

Where Anagram fits in the workflow

Anagram positions its branded Site Agent to answer product questions and guide recommendations while shoppers compare options and decide what to buy. Its homepage also describes a loop of engaging shoppers, learning from their questions, and improving the site and AI visibility.

That makes the Site Agent interaction a useful input to the workflow above: the team can study what shoppers ask, where they hesitate, and what creates friction, then decide whether the fix belongs in product data, content, recommendations, or the assortment.

Anagram’s shopper-question and growth approach is most relevant when the team wants the conversation to do more than resolve an individual question. Its AI Visibility tools address a related but distinct question: how the brand appears in ChatGPT, how it compares with competitors, and what customer questions suggest the team should focus on next.

The practical standard is simple: treat on-site AI conversations as first-party evidence of shopper intent, not as a demand forecast. Cluster the requests, diagnose the real gap, validate the commercial opportunity, and make the smallest change that can prove or disprove the hypothesis.