Analyze my returns. Top returners by product and reason, and 3 changes to reduce return rate.
Want more control? See the expanded prompt ›
Analyze my returns. Group by reason (sizing, color match, expectation gap), identify which products and categories return most, and recommend the top 3 changes to reduce return rate.
- Returns grouped by reason code and category.
- Identifies the highest-return SKUs.
- Recommends specific product-level fixes (sizing chart, photography).
- Ranks each fix by how many returns it addresses.
What you're trying to do
Returns eat margin invisibly. A high return rate doesn't show up in your dashboard the way a low conversion rate does. A returns analysis ties returns to specific causes (sizing gap, color match, expectation mismatch) so you can fix the SOURCE, not just the symptom.
Things to watch out for
- Data: needs a return reason recorded for each return; turn reasons on in Shopify's returns settings first.
- Category sensitivity: apparel, footwear and beauty all have different baseline rates.
- Customer-side vs product-side: Fudge distinguishes 'wrong size' (sizing chart fix) from 'arrived broken' (packaging fix).
- Net margin: Fudge estimates the real cost of each return, including shipping both ways, from the costs you give it.
How Fudge does it
Fudge reads the return reasons Shopify records, alongside your orders and product pages, without changing anything, and ranks the causes by how many returns each explains. Ask it to fix one, such as a size guide or a clearer description, and it drafts the change in your theme for you to preview and publish.
What a return rate analysis surfaces
A return rate analysis groups returns by reason (sizing, color mismatch, expectation gap, defect, change of mind), identifies which products and categories return most, and recommends specific changes to reduce return rate. Every avoided return saves the full shipping, processing and restocking cost.
When to run this analysis
Run the analysis if your return rate is above what’s usual for your category. Especially worth doing before adding new product categories or changing sizing.
The analysis requires return-reason data. Without it, you’re working from anonymous returns and the analysis is shallow.
What makes a great analysis
- Return reason categorization — sizing, color, expectation, defect, change of mind. Each tells a different story.
- Product and category breakdown — which specific SKUs and categories return most.
- Top 3 changes ranked by impact — typically: better sizing guide, better imagery, better descriptions, better recommendation quizzes.
- Cohort analysis — first-time vs. repeat customers, paid vs. organic. Different cohorts return differently.
- Cost analysis — return rate × average return cost = recoverable margin.
Common mistakes to avoid
The biggest mistake is treating all returns the same. A sizing return suggests fit-guide investment; an expectation-gap return suggests imagery + description investment. Different causes, different fixes.
The second mistake is hiding from the data. Honest engagement with return reasons — even when it points to your own marketing missteps — is the only way to fix the underlying causes.
Pair this with fit/size quiz and variant-aware stock urgency — return reduction is a systems play, not a single-fix.