Refund amounts attributed to the original order date, enabling cohort analysis of which order batches experienced high returns.
Refund Amount (by Order Date) = SUM ( Refund Value ) GROUPED BY Original Order Date
| Metadata | |
|---|
| Type | Currency |
| Data Source | Shopify |
| Aggregation | Sum |
Example
Orders placed in January generated $12,340 in refunds over the following months:
| Month Ordered | Orders | Refund Amount | Refund Rate |
|---|
| January | 1,847 | $12,340 | 8.2% |
| February | 2,103 | $9,450 | 5.5% |
| March | 1,956 | $6,120 | 3.8% |
How It Works
Unlike standard refund metrics that group by refund date, this metric attributes refunds back to when the original order was placed. A refund processed in April for a January order appears in January’s totals. This reveals which order cohorts generated the most returns.
When to Use
| Scenario | Action |
|---|
| Cohort return analysis | Compare refund rates across order date cohorts |
| Campaign quality assessment | Identify if specific promotions drove low-quality orders |
| Seasonal pattern detection | Spot high-refund periods like post-holiday returns |
| LTV accuracy | Account for future refunds when calculating customer lifetime value |
| Metric | Relationship |
|---|
| Refund Amount | Standard refund total (grouped by refund date) |
| Refund Amount (by Refund Date) | Refunds grouped by when issued |
| Refund Count | Number of orders with refunds |
| Net Revenue | Gross revenue minus refunds and discounts |
