D
Retail Reverse Logistics

Product Return Rates by Category

Published:
Jun 15, 2026
Updated:
May 17, 2026

Short answer

Online apparel returns remain industry challenge at 30%

Source: NRF

Key findings

  • Online apparel returns remain industry challenge at 30%
  • Virtual try-on reducing fashion returns by 20%
  • Return costs averaging $33 per item across categories

Download AI-ready context

Free

Get a structured Markdown file with key findings, chart data, sources, caveats, and prompts for AI research. Built for Product Return Rates by Category.

Context file preview

The downloaded Markdown file includes:

  • Key findings
  • Data table
  • Methodology
  • Source links
  • Suggested AI prompts 
# Product Return Rates by Category

## Research Question
## Short Answer
## Key Findings
## Data Table
## How To Use This With AI
## Suggested Prompts
## Sources
## Caveats

Data table

Product Return Rates by Category — data table
online inStore category
30 8 Apparel
15 5 Electronics
12 6 Home Goods
8 3 Beauty
4 1 Grocery

Analysis

Product returns represent a massive hidden cost in e-commerce, with online return rates 3-4x higher than in-store across every category. Apparel leads due to fit uncertainty, driving investment in virtual try-on and AI sizing tools. Retailers are increasingly monetizing returns through outlet channels rather than eating the full cost.

How to use this with AI

Use this context file to start a research conversation with an AI tool. It includes the key findings, chart data, sources, and caveats so the model starts from structured context instead of a blank prompt.

Use cases

  • Build a market research memo.
  • Create a short executive brief.
  • Compare this topic with another sector or geography.
  • Generate follow-up research questions.
  • Turn the findings into a slide outline.

Suggested prompts

  • Using this Deepstory context, create a market research memo with key findings, strategic implications, risks, and follow-up questions.
  • Using this dataset, explain the trend, identify the likely drivers, and list the caveats that should be checked before making a decision.
  • Turn this research into a 5-slide presentation outline for a business audience.
  • Create 10 follow-up research questions based on the data and identify what additional sources would be useful.

Sources

Source quality:
Primary source
Last reviewed:
Updated May 2026

Caveats

  • This research is based on available public data and should be used as context, not as professional advice. Check source methodology before making decisions.

How relevant was this information?

#returns #logistics #category-analysis