E-commerce
June 28, 2026
Product filters should help customers quickly find the right item. Yet, support conversations often reveal missing filters: compatibility, size, usage, material, budget, availability, or type of need.
The chatbot can flag these signals to improve navigation, provided they are grouped, anonymized, and transformed into truly useful criteria.
This guide shows how to use customer conversations to make product filters more effective.
Summary
Why do conversations reveal better filters?
Clients often express their needs using words that the catalog does not anticipate: “compatible with my model”, “fragrance-free”, “easy to gift”, “for travel”, “under 50 euros” or “delivery tomorrow”.
These requests highlight the criteria that filters should sometimes offer. They reveal the gaps between the internal structure of the catalog and client logic.
A good filter reflects the way the client searches, not just the way the team classifies products.
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What signals should be looked out for?
Useful signals include searches with no results, repeated questions, comparisons, compatibility requests, usage criteria, size constraints, allergies, materials, colors, budget, and the urgency of delivery.
Product returns should also be examined: a recurring reason for return can indicate a missing filter or information.
How to turn a question into a filter?
A question becomes a filter if it is frequent, stable, useful for decision-making, and linked to reliable data in the catalog. If the data does not exist, it must first be properly added.
Creating a filter without complete data can result in frustrating or misleading navigation.
How to manage synonyms?
Customers do not always use the catalog's words. They might say “waterproof” for “water-resistant”, “refill” for “consumable” or “small model” for “compact format”.
Conversations can enrich search synonyms and filter labels to make navigation more natural.
How to avoid too many filters?
Adding a filter for each question creates a confusing page. Priority must be given to criteria that recur frequently, reduce purchasing errors, or truly improve decision-making.
Some criteria are better handled in a guide, comparison, or conversational recommendation rather than in a permanent filter.
Which flow to follow?
The flow must connect the question, data, and navigation.
Group conversations by category, search, question, criteria, return, and abandonment.
Identify frequent criteria: usage, compatibility, size, material, budget, or lead time.
Verify that the data exists in the catalog to feed the filter correctly.
Create or rename filters using words that customers understand.
Measure search, conversion, clicks, returns, and the decrease in questions after the update.
Which examples should be used?
Repeated requests for "iPhone 15 compatible" may justify a filter by model. Questions about "fragrance-free" may justify an ingredient filter if the data is reliable.
Searches for "men's gift under 50 euros" may inspire a combination of filters or a guide page rather than a simple price sort.
When not to create a filter?
It is better to avoid a filter if the criterion is rare, subjective, poorly documented, temporary, or impossible to maintain. An empty or incomplete filter gives the impression that the catalog is broken.
In these cases, a contextual help message or a recommendation chatbot may be more suitable.
Which KPIs should be monitored?
Track filter usage, searches with no results, conversion after filtering, category abandonment, returns due to wrong choices, support questions by criterion, and click-through rates on recommendations.
These indicators show whether the filters are meeting real expectations better.
Which mistakes should be avoided?
Avoid adding filters without clean data, simply reusing internal vocabulary, creating too many options, or ignoring product feedback as a source of improvement.
Filters should make choices simpler, not turn the category into a technical table.
How can Qstomy help?
Qstomy can connect the chatbot to support conversations, attachments, authentication rules, internal alerts, the CRM, the catalog, product filters, and privacy procedures to respond clearly, and then hand over sensitive cases with an actionable summary.
The chatbot helps the customer move forward without inventing a file validation, a confirmed identity, a business alert, a persona, or a product filter that still needs to be verified by a secure and reliable source.
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Key takeaways
Key Takeaways
Customer conversations reveal missing filters: usage, compatibility, size, material, budget, lead time, allergy or format.
What the customer needs to understand
The customer must find search criteria that match their words and real needs.
The right limit for the chatbot
The chatbot can flag signals, but catalogue data must be verified before creating a filter.
Enzo
June 28, 2026
