Find opportunities to reduce preventable returns.
See which products, brands and customer segments need attention. Explore shopping behaviour around return requests and what happens afterward, so merchandising, ecommerce and customer experience teams can prioritise improvements.
See where returns begin—and what happens next.
| Analysis view | What stands out | Evidence to explore | Your next question |
|---|---|---|---|
| 01Product returns & revenue impact | Outerwear deserves a closer look.Brand A has more return-request activity than Brand B’s knitwear. | 180 units · €10,800Requested for return · potential refund value Brand B: 100 units · €6,000 |
Which products and product information should merchandising review first? |
| 02Shopping behavior & later returns | More product views, more later return requests.A difference worth investigating—not evidence that browsing causes returns. | 28% vs 16%Later same-SKU return requests within 30 days 4+ views: 28/100 assessed buyers 1 view: 24/150 assessed buyers |
What information are shoppers looking for when they repeatedly view a product? |
| 03Customer recovery after a return | Customers with less purchase history are less likely to buy again.They also take longer when they do return to purchase. | 40% vs 60% bought againWithin 60 days of a return request 1 prior purchase: 32/80 customers 2 prior purchases: 72/120 customers Median among repurchasers: 18 vs 9 days |
How does the experience after a return differ for newer customers—and which products or brands do they buy next? |
| 04Customer returns patterns | Previous return activity adds useful context.Outcomes differ between customers with two previous requests and those with none. | 30% vs 12%Later same-SKU return requests within 30 days 2 prior requests: 36/120 observations No prior requests: 24/200 observations |
Does the difference persist when comparing similar products and customer groups? |
Find where your team can make a difference.
Ecommerce returns analytics examines return patterns alongside product and shopping context. Stormly helps you move from a broad returns question to a focused investigation: identify a concentration of requests, explore the relevant behaviour, develop a hypothesis and test an improvement.
The opportunity might involve product information, imagery, discovery or expectation-setting. The report helps you decide where to look. It does not automatically detect a fit problem or reveal a shopper’s reason for returning an item.
Identify
Find the product areas and customer groups worth reviewing.
Investigate
Explore the shopping behaviour and product context behind the pattern.
Choose
Agree on one improvement to test and how to assess it.
Evaluate
Allow time for return outcomes, then use what you learn to guide the next review.
Four views to guide your returns investigation.
Start with the question your team needs to answer. The returns report brings together four types of analysis, from product-level patterns to shopping behaviour and the experience after a return. Explore the report views below, continuing the findings above with the same observation windows and denominators.
One report. Four ways to find your next question.
Product returns & revenue impact
Where are requests and potential refunds concentrated?
| Finding | Product area | Units requested for return | Potential refund value |
|---|---|---|---|
| Prioritise review | Brand A · Outerwear | 180 | €10,800 |
| Compare product context | Brand B · Knitwear | 100 | €6,000 |
| Lower request volume | Brand C · Accessories | 40 | €2,400 |
Shopping behavior & later returns
Does deeper product exploration coincide with more later requests?
| Product views | Assessed buyers | With later same-SKU request | Later-request incidence |
|---|---|---|---|
| 4+ views | 100 | 28 | |
| 1 view | 150 | 24 |
Customer recovery after a return
Who buys again, and how quickly?
| Previous purchases | Customers assessed | Bought again within 60 days | % buying again | Typical days to buy again |
|---|---|---|---|---|
| 1 | 80 | 32 | 18 | |
| 2 | 120 | 72 | 9 |
Customer returns patterns
How do outcomes differ with previous return activity?
| Previous return requests | Assessed observations | With later same-SKU request | Later-request incidence |
|---|---|---|---|
| 2 | 120 | 36 | |
| 0 | 200 | 24 |
Report-view previews use example data and a simplified selection of report fields. Each view has its own population and observation window; do not add the groups together.
Explore the shopping behaviour around return requests.
Use the before-return and after-return journey perspectives to add behavioural context. Compare brands or audiences, highlight differences, then inspect a tracked activity such as Search.
A difference in product exploration can turn “why do we have returns?” into a question about discovery or the information shoppers see. A comparison can guide that review without proving what caused the return.
Follow the difference into a specific activity.
| Tracked activity | Brand A | Brand B |
|---|---|---|
| Search | ||
| View product: other brand |
See how shopping journey comparisons work for the full brand-to-activity walkthrough.
Ask the next question without leaving Claude.
Connect Stormly through MCP and explore your returns analysis in a conversation. Ask for a comparison, dig into a finding, and get a clear explanation grounded in your project’s data.
See how to connect Claude to StormlyWhat should our team investigate first to reduce preventable returns?
Start with product information and the experience after a return.
Brand A’s outerwear has 180 units requested for return and €10,800 in potential refund exposure. Buyers with 4+ product views also show more later return requests: 28% versus 16%.
Recovery deserves attention too: 40% of customers with one prior purchase buy again, versus 60% with two. Review these groups separately before choosing a change.
Your Monday morning briefing. Already in your inbox.
Schedule your Stormly report and AI email analysis for every Monday morning. Receive the findings, conclusions and suggested next steps, ready for your team’s weekly review.
Choose the report, recipients and delivery schedule in Stormly. Email automation runs in Stormly; Claude is another way to explore the same data.
Your weekly returns briefing
Good morning. Here are the findings to bring to this week’s review.
- Prioritise Brand A’s outerwear.180 units requested for return represent €10,800 in potential refund exposure.
- Investigate repeated product viewing.Later return requests appear in 28% of assessed buyers with 4+ views, versus 16% with one view.
- Review recovery for newer buyers.Customers with one prior purchase show 40% repurchase within 60 days, versus 60% for those with two.
Bring merchandising and CX together to choose one product-information or post-return experience improvement to test.
Find your next opportunity to reduce preventable returns.
Bring your returns question. We’ll explore product impact, shopping behaviour, customer patterns and recovery after a return—so your team can choose where to focus and what to test.
We’ll also cover the data integrations relevant to your store.
Book a returns analytics demo