PROPOSED PROJECT
Fit & Returns Insights
Bring return comments, size charts, and garment measurements together so your team can spot recurring fit problems and decide what to check or change.
“Tight at the waist”
keeps coming up.
Each comment has one main topic. These are example counts, not actual return rates.
Both describe the garment waist circumference for size M. Verify the physical sample before changing the chart.
RETAIL TEAM REVIEW REQUIREDGet from a pattern
to a specific action.
An apparel retailer is investigating its Linen Straight trouser, product code LN-204, size M. In this fictional 12-comment sample, seven comments mention the waistband. The published garment-waist chart says 80 cm, while a newer specification says 76 cm. The project should surface the conflict and route it for a physical check, rather than infer the correct size advice.
WHAT IS USUALLY DONE
An AI summary can group comments into themes such as “too small.” The merchandising team still has to find the right variant, compare different versions of the size chart, and work out which issues justify inspecting a garment.
WHAT SOVRUN CAN DO FOR YOU
Sovrun would connect comments for each product and size with the relevant measurement documents, compare document-extraction and classification models on a reviewed sample, and build a review screen with links to the original records. Conflicting measurements remain unresolved until the team checks them. Your team receives issue counts, links to the original comments, and specific tasks for checking products or updating content.
Keep the evidence
next to the decision.
A summary alone leaves the team with another investigation. The review screen links each finding to the product and size, original comment, document version, and person who needs to act.
“The legs fit, but the waistband feels tighter than the chart suggested.”
- Product and size
- LN-204 / M
- Suggested theme
- Waistband
- Review state
- Label checked in example
Inspect the waist.
Resolve the chart.
- Measure the retained size-M sample.
- Confirm which source revision is correct.
- Approve the corrected chart and support note.
Customer comments suggest where to look. They do not establish a manufacturing defect or prove why a return occurred.
Evidence your team
can act on.
A review screen, a list of issues for each product and size, and tasks for the retail team. We would test whether the feedback is grouped correctly and leads to useful actions; fewer returns are not guaranteed.
Waistband
Example evidence IDs
R-108 · R-112 · R-119
Waist circumference
Chart rev 02 / spec rev 04
PHYSICAL CHECK PENDINGVerify before updating.
- Owner
- Apparel team
- Next step
- Measure size M
- Chart update
- Awaiting approval
How we’d make it happen.
Join the right records
Match comments to product code, size, and date. Extract measurements with document versions and units, and keep uncertain matches out of aggregated findings.
Evaluate and investigate
Compare extraction and classification approaches against reviewed examples. Surface repeated concerns beside the relevant chart and garment specification.
Resolve with merchandising
Assign the physical or document check, record the decision, and prepare any chart or support-copy update for approval.
Where AI helps
Document models extract candidate chart values; language models classify the actual fit concern and retrieve supporting passages. Joins, units, counts, and denominators are checked with data rules. Ambiguous matches stay in a review queue.
What people approve
Merchandisers validate variant matches, inspect physical garments, and resolve conflicting sources. They approve any chart or product-page correction. Comment frequency alone is not treated as proof of a defect.
What we’d need to start
- Anonymized comments and stable product codes and sizes
- Order volumes, reporting periods, and return records
- Versioned charts, garment specifications, and review owners
How we’d test it
Measure variant-match coverage, reviewed label accuracy, and document-extraction errors. Check whether a merchandiser can trace each proposed action to its sources. Any later change in returns requires a separate evaluation with order-volume and timing controls.
Prepared for Retail & e-commerce. We would agree on the work after reviewing your brief, materials, and requirements.
This is a proposed project, not completed client work. The visuals and numbers show example outputs, not measured results.
