Understanding why customers returned products
Understanding why products came back, and giving teams the visibility to act on it.
Achieved results
- My Power BI reporting helped reduce customer returns by 75%.Verified outcome.
Business context
In an e-commerce BI role, customer returns carried real cost: refunds, re-processing, write-offs and unhappy customers. Returns data existed but lived in separate extracts nobody could read together.
The problem
Returns were rising, but the reasons were spread across order, product and returns data that nobody looked at together. The teams who could act on it had no clear view of which products, categories or reasons were driving the problem.
My role
I analysed the returns data end to end and built Power BI reporting that made the drivers of returns visible to the teams who could act on them.
Investigation and approach
- 01Cleaned and joined returns data with order, product and reason data.
- 02Analysed returns by product, category, reason and channel to find the real drivers.
- 03Built Power BI reporting tracking return rates and reasons over time.
- 04Shared findings with the teams responsible so actions targeted the biggest causes.
The analysis, for you to explore
Customer returns analysis
Illustrative reconstruction using representative sample data.
Units delivered
153,565
Sample units
Units returned
8,601
Return rate
5.60%
Returned ÷ delivered
Top reason (problem area)
Wrong size
34% of returns
Return rate trend by month
Returned units by reason
Return rate by category (%)
Units delivered and returned, filtered by period, category and reason.
| Month | Category | Reason | Returned units | Delivered units | Share of month-category returns |
|---|---|---|---|---|---|
| 2024-07 | Home & Living | Wrong size ordered | 68 | 3252 | 2.1% |
| 2024-07 | Home & Living | Changed mind | 44 | 3252 | 1.4% |
| 2024-07 | Home & Living | Faulty on arrival | 36 | 3252 | 1.1% |
| 2024-07 | Home & Living | Not as described | 28 | 3252 | 0.9% |
| 2024-07 | Home & Living | Late delivery | 24 | 3252 | 0.7% |
| 2024-07 | Electronics | Wrong size ordered | 111 | 3692 | 3.0% |
| 2024-07 | Electronics | Changed mind | 72 | 3692 | 2.0% |
| 2024-07 | Electronics | Faulty on arrival | 59 | 3692 | 1.6% |
| 2024-07 | Electronics | Not as described | 45 | 3692 | 1.2% |
| 2024-07 | Electronics | Late delivery | 39 | 3692 | 1.1% |
| 2024-07 | Apparel | Wrong size ordered | 180 | 4507 | 4.0% |
| 2024-07 | Apparel | Changed mind | 116 | 4507 | 2.6% |
| 2024-07 | Apparel | Faulty on arrival | 95 | 4507 | 2.1% |
| 2024-07 | Apparel | Not as described | 74 | 4507 | 1.6% |
| 2024-07 | Apparel | Late delivery | 63 | 4507 | 1.4% |
| 2024-07 | Beauty | Wrong size ordered | 45 | 3250 | 1.4% |
| 2024-07 | Beauty | Changed mind | 29 | 3250 | 0.9% |
| 2024-07 | Beauty | Faulty on arrival | 23 | 3250 | 0.7% |
| 2024-07 | Beauty | Not as described | 18 | 3250 | 0.6% |
| 2024-07 | Beauty | Late delivery | 15 | 3250 | 0.5% |
| 2024-08 | Home & Living | Wrong size ordered | 69 | 3480 | 2.0% |
| 2024-08 | Home & Living | Changed mind | 44 | 3480 | 1.3% |
| 2024-08 | Home & Living | Faulty on arrival | 36 | 3480 | 1.0% |
| 2024-08 | Home & Living | Not as described | 28 | 3480 | 0.8% |
| 2024-08 | Home & Living | Late delivery | 24 | 3480 | 0.7% |
| 2024-08 | Electronics | Wrong size ordered | 108 | 3805 | 2.8% |
| 2024-08 | Electronics | Changed mind | 70 | 3805 | 1.8% |
| 2024-08 | Electronics | Faulty on arrival | 57 | 3805 | 1.5% |
| 2024-08 | Electronics | Not as described | 44 | 3805 | 1.2% |
| 2024-08 | Electronics | Late delivery | 38 | 3805 | 1.0% |
| 2024-08 | Apparel | Wrong size ordered | 127 | 3377 | 3.8% |
| 2024-08 | Apparel | Changed mind | 82 | 3377 | 2.4% |
| 2024-08 | Apparel | Faulty on arrival | 67 | 3377 | 2.0% |
| 2024-08 | Apparel | Not as described | 52 | 3377 | 1.5% |
| 2024-08 | Apparel | Late delivery | 45 | 3377 | 1.3% |
| 2024-08 | Beauty | Wrong size ordered | 28 | 2156 | 1.3% |
| 2024-08 | Beauty | Changed mind | 18 | 2156 | 0.8% |
| 2024-08 | Beauty | Faulty on arrival | 14 | 2156 | 0.6% |
| 2024-08 | Beauty | Not as described | 11 | 2156 | 0.5% |
| 2024-08 | Beauty | Late delivery | 9 | 2156 | 0.4% |
| 2024-09 | Home & Living | Wrong size ordered | 65 | 3496 | 1.9% |
| 2024-09 | Home & Living | Changed mind | 42 | 3496 | 1.2% |
| 2024-09 | Home & Living | Faulty on arrival | 34 | 3496 | 1.0% |
| 2024-09 | Home & Living | Not as described | 26 | 3496 | 0.7% |
| 2024-09 | Home & Living | Late delivery | 23 | 3496 | 0.7% |
| 2024-09 | Electronics | Wrong size ordered | 89 | 3348 | 2.7% |
| 2024-09 | Electronics | Changed mind | 58 | 3348 | 1.7% |
| 2024-09 | Electronics | Faulty on arrival | 47 | 3348 | 1.4% |
| 2024-09 | Electronics | Not as described | 36 | 3348 | 1.1% |
| 2024-09 | Electronics | Late delivery | 31 | 3348 | 0.9% |
| 2024-09 | Apparel | Wrong size ordered | 184 | 5179 | 3.6% |
| 2024-09 | Apparel | Changed mind | 119 | 5179 | 2.3% |
| 2024-09 | Apparel | Faulty on arrival | 97 | 5179 | 1.9% |
| 2024-09 | Apparel | Not as described | 75 | 5179 | 1.4% |
| 2024-09 | Apparel | Late delivery | 65 | 5179 | 1.3% |
| 2024-09 | Beauty | Wrong size ordered | 48 | 3914 | 1.2% |
| 2024-09 | Beauty | Changed mind | 31 | 3914 | 0.8% |
| 2024-09 | Beauty | Faulty on arrival | 25 | 3914 | 0.6% |
| 2024-09 | Beauty | Not as described | 19 | 3914 | 0.5% |
| 2024-09 | Beauty | Late delivery | 17 | 3914 | 0.4% |
Findings
- A small number of reasons drove most returns — so targeted actions beat blanket policies.
- Category-level rates varied widely, so averages hid the real problem areas.
- Seeing return reasons trend over time made it obvious when an action worked.
Deliverables
- Returns analysis
- Power BI returns dashboard
- Return-driver findings by product and reason
Tools used
- Power BI
- SQL
- Excel
- Data modelling
Work with me