Refund Rate Root Cause Analysis Builder
Analyze refund spikes by product, channel, cohort, reason code, and customer segment to identify root causes and revenue-saving actions.
Prompt Template
You are a data analyst investigating refund rate changes for [business/product]. Build a root cause analysis plan and executive readout for [time period]. Available data: - Orders/subscriptions table fields: [fields] - Refunds/returns table fields: [fields] - Customer segments or cohorts: [segments] - Channels/campaigns: [channels] - Product/SKU/plan details: [products/plans] - Refund reason codes or support tags: [reason codes] - Known business changes: [launches, pricing changes, shipping delays, policy changes] Deliver: 1. Key metric definitions for refund rate, net revenue loss, and time-to-refund 2. Segmentation plan by product, cohort, acquisition channel, geography, tenure, and reason code 3. SQL or pseudo-SQL queries for the first-pass analysis 4. Driver matrix separating volume, mix, policy, product quality, fulfillment, and expectation-setting causes 5. Recommended charts for leadership 6. Prioritized actions with expected impact and owners 7. A concise executive summary template Flag data-quality risks and avoid assuming causation without evidence.
Example Output
Refund Rate RCA — April Spike
**Headline:** Refund rate increased from 4.8% to 7.1% month over month, driven primarily by first-time customers from paid social buying the Starter Bundle.
| Segment | Refund rate | MoM change | Revenue impact | Likely driver |
|---|---:|---:|---:|---|
| Starter Bundle / paid social | 12.4% | +5.9 pts | $41,200 | Expectation mismatch from ad creative |
| Pro Plan / organic | 3.1% | +0.2 pts | $3,800 | Stable |
First-pass SQL question
Compare refund rate by order cohort, SKU, channel, and refund reason for the last 12 weeks, excluding test orders and partial price adjustments.
Recommended actions
1. Update paid social landing page with clearer sizing and delivery expectations.
2. Add post-purchase education email for Starter Bundle customers.
3. Audit refund reason tagging because 31% of April refunds are marked “other.”
Tips for Best Results
- 💡Define whether partial refunds, credits, chargebacks, and cancellations count in your refund rate.
- 💡Give the model your known business changes so it can test plausible drivers first.
- 💡Ask for both customer-count impact and revenue impact; they can tell different stories.
- 💡Include refund reason code quality checks before trusting the categories.
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