returns-reduction

Name: returns-reduction
Availability: InStock
Author: Gorgias

Cluster customer return reasons by SKU from Gorgias ticket data.

2|Updated Jun 4, 2026

Search Tags:#analytics #ecommerce #root-cause #customer-experience #returns #merchandising

Ready to run

npx skills add https://github.com/gorgias/mcp-cookbook --skill returns-reduction

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: returns-reduction
Source: https://github.com/gorgias/mcp-cookbook/tree/main/recipes/returns-reduction
Command: npx skills add https://github.com/gorgias/mcp-cookbook --skill returns-reduction

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the silent margin leak caused by product returns by analyzing customer feedback to uncover specific quality, sizing, or expectation gaps.

Core Features & Use Cases

Root Cause Clustering: Automatically groups return reasons like sizing issues, quality defects, or misleading product descriptions.
Actionable Insights: Maps return trends to specific products to prioritize fixes for PDPs, sizing guides, or QA processes.
Use Case: Use this to analyze the last 90 days of return data for your apparel category to determine if high return rates are due to sizing inconsistencies or inaccurate photography.

Quick Start

Use the returns-reduction skill to analyze all products over the last 90 days and identify the top three root causes for returns.

Frequently Asked Questions about returns-reduction

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I identify the root causes of ecommerce product returns?▼

To identify the root causes of product returns, this Skill analyzes customer ticket data and return language to cluster return reasons by SKU. It detects specific quality, sizing, or expectation gaps to help mitigate margin loss.

Can I use customer support tickets to find out why specific products are returned?▼

Yes, you can use customer support tickets to find out why products are returned by applying heuristic labeling to ticket data. It automatically groups return reasons and maps trends to specific SKUs to prioritize fixes.

How do I analyze return trends for my apparel category over the last 90 days?▼

You can analyze return trends for your apparel category over the last 90 days by processing historical customer ticket data. This reveals if high return rates are due to sizing inconsistencies or inaccurate photography.

Do I need Gorgias ticket data to analyze return reasons and margin loss?▼

Yes, you need access to Gorgias MCP ticket and customer data to perform heuristic labeling and trend analysis. This access is required to uncover return reasons and identify margin loss.

What is the best way to prioritize fixes for product detail pages using return data?▼

The best way to prioritize fixes for product detail pages is to map actionable return insights to specific products. Analyzing return data helps prioritize updates for PDPs, sizing guides, or QA processes based on actual customer feedback.