> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upstackdata.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Refund Amount (by Order Date): Refunds Attributed to Orders

> Track refund amounts attributed to original order dates. Analyze cohort return rates, identify high-refund order batches, and improve forecasting.

<Tip>
  Refund amounts attributed to the original order date, enabling cohort analysis of which order batches experienced high returns.
</Tip>

## Formula

<Info>
  **Refund Amount (by Order Date)** = **SUM** ( Refund Value ) **GROUPED BY** Original Order Date
</Info>

| Metadata        |                             |
| --------------- | --------------------------- |
| **Type**        | Currency                    |
| **Data Source** | [Shopify](/sources/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%        |

January's higher refund rate signals a potential product or fulfillment issue during that period.

***

## 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 |

***

## Related Metrics

| Metric                                                                                               | Relationship                                   |
| ---------------------------------------------------------------------------------------------------- | ---------------------------------------------- |
| [Refund Amount](/reference/metrics/orders/adjustments/refund-amount)                                 | Standard refund total (grouped by refund date) |
| [Refund Amount (by Refund Date)](/reference/metrics/orders/adjustments/refund-amount-by-refund-date) | Refunds grouped by when issued                 |
| [Refund Count](/reference/metrics/orders/adjustments/refund-count)                                   | Number of orders with refunds                  |
| [Net Revenue](/reference/metrics/orders/revenue/net-revenue)                                         | Gross revenue minus refunds and discounts      |

[See all Adjustments metrics →](/reference/metrics/orders/adjustments)