E-Commerce & Logistics

Return Reason Analyzer

AI analyzes return reasons and identifies product issues

Return Reasons Are Lost Goldmines

E-commerce stores lose 20-30% revenue to returns but do not analyze why. Return forms have free-text fields that go unread. Product defects and sizing issues repeat across hundreds of returns. Teams lack time to manually categorize and analyze return data.

20-30%
Revenue lost to returns
<5%
Return reasons analyzed
40%
Returns with preventable issues

Turn Return Data Into Product Improvements

AI return analyzer that reads customer return reasons from free-text forms, categorizes issues (sizing, quality, expectations, wrong item), identifies trending problems by product, surfaces root causes, and suggests fixes--enabling data-driven return reduction.

Integrates with return portals (Loop, Happy Returns, custom)

Reads free-text return reasons and categorizes automatically

Identifies trends: "Product X has 40% returns for sizing too small"

Root cause analysis: sizing chart inaccurate, product photos misleading

Suggests fixes: update size chart, add measurement video, improve photos

Tracks return rate changes after implementing fixes

From Return Data to Reduced Returns

1

Collect Return Reasons

Customers fill return form: "Too small, I ordered M but fits like S." System captures free text.

2

AI Categorization

Claude categorizes: sizing issue, suspected cause: size chart inaccurate.

3

Trend Analysis

Dashboard shows Product X has 38% sizing returns, mostly "runs small." Aggregates feedback.

4

Action & Monitor

Team updates size chart, adds "Order size up" note. Monitors return rate: drops from 38% to 22%.

Key Features

Root Cause Detection

Not just "sizing issue." Identifies "Photos show model in size M, but model is 6'2\" - misleading for average customer."

Product Comparison

Compares return rates across products. "Hoodie A has 12% returns, Hoodie B has 35% - investigate B."

Supplier Quality Tracking

Identifies which suppliers have higher defect rates. "Vendor X: 8% defect returns vs. 2% average."

Cost Impact

Calculates ROI of fixes. "Fixing sizing issue saves $15K annually in returns and re-shipping."

Return Analysis Stack

Claude 3.5 Sonnet
Return portal APIs
NLP categorization
Trend analysis engine
Dashboard (React)
A/B testing framework

Who Benefits?

Apparel Brands

Reduce sizing returns 30% with data-driven size chart improvements. Save thousands in return shipping.

Electronics Sellers

Identify defective batches early. Recall before hundreds of returns and bad reviews.

Furniture E-Commerce

Discover photos do not show scale accurately. Add dimension overlays, reduce \"too big/small\" returns.

Ready to Get Started?

Get in touch to see Return Reason Analyzer in action.