Return Anomalies
Catch return rate spikes early; sherlock's Return Anomalies case flags products whose return rate jumped at least 50% above the 90-day baseline.
Return Anomalies is one of Sherlock's investigation cases. It compares each product's last 30 days of returns against its prior 90-day baseline so you can spot quality, sizing, or fulfillment issues before they show up in margin reports. Use this page to triage the spikes worth investigating with operations or your supplier.
What's on the page
At the top:
- The rule: "Products where last 30-day return rate is at least 50% above the previous 90-day baseline, with 3+ returns and positive impact."
- Total potential leak: The combined dollar impact of every flagged product.
- Refresh: Re-run the check with the latest data.
Below, the table:
| Column | What it means |
|---|---|
| Product | Product image, title, marketplace flag, ASIN (with copy icon) and brand |
| Summary | Plain-English description of the jump, plus the marketplace and baseline date range |
| 30d rate | Return rate over the last 30 days |
| Baseline | Return rate over the prior 90 days |
| Returns | Number of returns in the last 30 days |
| Net impact | Dollars lost to the spike |
The list is sorted by Net impact, biggest first.
How to read a row
Return rate rose from 1.8% to 15.7% with 19 returns. Amazon-US · Baseline 2026-01-12 to 2026-04-11 30d rate: 15.7% · Baseline: 1.8% · Returns: 19 · Net impact: $220.99
The product's return rate is almost 9× its usual rate, costing about $221 in lost sales over the last month.
Open the product investigation
Click any row to open the same investigation view you get from Signals (Overview, Timeline, Metrics).
For return spikes specifically, the most useful places to look:
- The Metrics heatmap → Sales Performance → Units Refunded row. Red cells show you when the returns clustered.
- The Timeline tab. Recent product changes often line up with the spike.
- The Suggestions in the Overview tab.
What to do about it
When a product is flagged, the cause is usually one of these:
- Listing mismatch. Images, title, or bullets don't match what the customer receives.
- Quality issue. A bad batch, damaged packaging, or a recent supplier change.
- Sizing or fit. Common with apparel and home goods.
- Wrong customer. Ad targeting or keywords are bringing in shoppers who aren't a fit.
Use Sherlock's data to narrow it down:
- Read review and feedback comments in Seller Central to see why people are returning.
- Check the Timeline in the investigation for recent changes.
- Compare Units Refunded against ad spend pushes: If returns spiked after an ad ramp, the targeting may be off.
If you push out a fix (e.g., updated images, supplier change), open the Sherlock assistant and save a seller annotation. Future investigations will see that note in the Timeline and weigh it in their reasoning.
Tips
- A high return rate on a low-volume product can look alarming. Use Returns and Net impact to focus on items that actually move the needle.
- Re-check after each fix. Sherlock recalculates with every refresh.
Where to go next
What is Sherlock
Overview of Sherlock's investigations, cases, and how it works.
Signals
Your daily filterable list of every meaningful product movement.
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