Complete AI Training
Sign inGet my AI kit

Your job's AI kit

Get your AI kit

Tell us who you are and what you do. We show you your kit right away and email you the link: skills, prompts, AI agents, MCP servers and courses for your job.

500+ jobs ready, and we make a kit for any other job. No payment needed to look.

Share

AI agent for e-commerce managers

Return Reason Analysis Agent

Lower return rates through fixes to the root causes

Return Reason Analysis Agent: what goes in, what the agent does and what you get

What it does

Returns eat profit, and return reasons are logged but rarely used to fix product pages, sizing or quality. Each week this agent reads return reasons, customer comments and reviews. It groups returns by product and reason, such as size, damage or not as described, and compares return rates with the category. A high rate on low volume is watched before action. For outliers it looks for a fixable cause: a misleading photo, a wrong size chart or a packaging problem. It checks the cause against comments, reading more if they do not support it. It drafts fixes such as page edits. After changes, it checks whether returns fell. The ecommerce manager approves every change.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedYes, continueNoNo 1 STARTS WHEN Weekly review 2 USES A TOOL Pull returns, reasons and comments 3 DOES Calculate return rates and find outliers 4 DOES Find likely fixable cause 5 CHECKS THE RESULT Do comments support the cause? If not: read more comments and revise the cause. Back tostep 4. 6 DOES Draft page, size chart or packaging fixes 7 YOU APPROVE Manager approves fixes 8 CHECKS THE RESULT Did the return rate fall after four weeks? If not: look for a second cause and draft a new fix.Back to step 4. 9 RESULT Return fix log
Read the steps as a list
  1. Weekly review
  2. Pull returns, reasons and comments
  3. Calculate return rates and find outliers
  4. Find likely fixable cause
  5. Do comments support the cause?If not: read more comments and revise the cause. Back to step 4.
  6. Draft page, size chart or packaging fixes
  7. Manager approves fixesThe agent waits here for your OK.
  8. Did the return rate fall after four weeks?If not: look for a second cause and draft a new fix. Back to step 4.
  9. Return fix log

How it decides

It flags products with return rates well above category and enough volume.

  • Minimum 30 returns to act
  • Compare with category
  • Check rate after fix

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Minimum returns
  • Outlier threshold
  • Reasons list

What keeps you in control

It always asks you first

  • Page and product changes

Hard limits

  • Never edits live pages

It stops when

  • Done
  • Stop: low data

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensA jacket had a 31% return rate against 12% for the category. The agent first suspected the photo color, but the comment check failed: most comments said it ran small. It read 40 more and revised the cause. It drafted a size chart note to size up, which the manager approved. Over four weeks returns fell to 17%.

More agents for e-commerce managers