E-commerce
June 28, 2026
Some products generate far more inquiries than others: pre-purchase questions, usage errors, returns, warranties, compatibility, installation, or disappointment upon receipt. These tickets are not just a support cost.
They often reveal an issue with the product page, quality, packaging, tutorial, or marketing promise.
This guide shows how to detect products that generate too much support and what to do next.
Summary
Why track tickets by product?
A product that sells well will naturally have more contacts. The interesting signal is the volume of tickets compared to sales, returns, and satisfaction. It is this ratio that shows if the product is creating abnormal friction.
Support then becomes a product issue sensor.
A product that generates too much support sends a signal about the actual experience.
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What causes should be looked for?
Look for a fuzzy product sheet, poorly explained sizing, uncertain compatibility, missing tutorial, quality defect, ambiguous packaging, difficult installation, overly strong marketing promise, or complex return policy.
It is important to distinguish a comprehension issue from a real product issue.
How to analyze tickets?
Group tickets by product, reason, journey stage, channel, purchase date, batch, and resolution. Compare with sales and returns to identify the products that consume the most effort.
A useful analysis must lead to a concrete action.
If the same reason appears just after delivery, the problem often comes from usage, packaging, or the user guide. If it appears before purchase, you should rather look at the product sheet or filters.
How to fix the problem?
Depending on the cause, correct the product sheet, add a tutorial, change a filter, improve the packaging, modify the FAQ, report a quality defect, or adjust the advertising promise.
Support should not indefinitely absorb avoidable friction.
How to follow up after correction?
Measure tickets before and after the fix. If requests do not decrease, the problem was not addressed in the right place or the fix is not visible at the right time.
Agents must continue to tag reasons accurately.
Progress is seen in the reduction of customer effort.
Support can also separate avoidable tickets from useful ones. Some requests show strong pre-purchase interest, while others reveal missing information or a design flaw.
This distinction avoids wanting to reduce all contact without nuance.
Which flow to follow?
The flow must connect support and product.
Identify product, volume sold, tickets, returns, reasons, batches, channels, and period.
Analyze product sheet, quality, usage, compatibility, tutorials, packaging, delivery, and warranty.
Prioritize products according to customer effort, support cost, risk, and conversion impact.
Correct content, product, process, chatbot, tutorial, packaging, or quality escalation.
Measure tickets, returns, satisfaction, resolution, support cost, and reduction in reminders.
Which examples should be used?
“This product generates a lot of installation tickets; we need to add a quick start guide.” “The returns mostly come from wrong expectations regarding the size.”
The data must lead to a correction.
When to transfer?
Transfer is necessary due to quality defects, safety issues, suspected batches, sudden spike in tickets, strategic product, public complaint, massive warranty claims, or regulatory risk.
The bot must transmit product, reason, volume, examples, batches, impact, and recommendation.
Which KPIs should be monitored?
Track tickets by product, rate per sale, returns, warranties, follow-ups, resolution time, CSAT, reasons, and support cost.
These data show where to invest in product improvement.
Which mistakes should be avoided?
Avoid looking only at raw volume, tagging too vaguely, ignoring feedback, or leaving a problematic product without action.
Support must fuel continuous improvement.
How can Qstomy help?
Qstomy can connect the chatbot to support tickets, products, proforma invoices, promotions, artwork approvals, orders, carriers, proofs of delivery, refunds, and escalation procedures to respond accurately.
The chatbot helps the customer understand a problematic product, a proforma, a promotion, an artwork approval, or a proof of delivery without inventing a discount, invoice, approval, or proof that needs to be verified.
Explore AI support, the AI sales agent, or request a demo.
Key takeaways
Key Takeaways
Products that consume too much support must be analyzed by reasons, sales, returns, batches, content, quality, and customer cost.
What the customer must understand
The customer must benefit from visible corrections, not just repeated answers.
The right limit of the chatbot
The chatbot can tag and direct, but it must transfer quality defects, security issues, and abnormal spikes.
Enzo
June 28, 2026
