> ## 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.

# Returning Customer Refund %: Track Repeat Buyer Returns

> Measure the percentage of returning customer orders that result in refunds. Compare to new customer refund rates and identify retention risks.

<Tip>
  The share of returning customer orders that resulted in a refund.
</Tip>

## Formula

<Info>
  **Returning Customer Refund %** = **Returning Customer Refund Count** ÷ **Returning Customer Order Count** × 100
</Info>

### Formula Components

| Metric                                                                                                       | Definition                                       |
| ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------ |
| [**Returning Customer Refund Count**](/reference/metrics/orders/adjustments/returning-customer-refund-count) | Number of returning customer orders with refunds |
| [**Returning Customer Order Count**](/reference/metrics/orders/counts/returning-customer-orders)             | Total returning customer orders                  |

| Metadata        |                             |
| --------------- | --------------------------- |
| **Type**        | Percentage                  |
| **Data Source** | [Shopify](/sources/shopify) |
| **Aggregation** | Ratio                       |

***

## Example

Your store processed **1,850 returning customer orders** in Q1, with **148 resulting in refunds** (8.0%):

| Month    | RC Orders | RC Refunds | RC Refund % |
| -------- | --------- | ---------- | ----------- |
| January  | 580       | 41         | 7.1%        |
| February | 620       | 50         | 8.1%        |
| March    | 650       | 57         | 8.8%        |

***

## How It Works

Divides refunded returning customer orders by total returning customer orders. A returning customer is anyone who previously completed an order. Refunds are attributed to the original order date and include both full and partial refunds.

***

## When to Use

| Scenario                           | Action                                                                    |
| ---------------------------------- | ------------------------------------------------------------------------- |
| Comparing customer segments        | Higher RC refund rate may signal product quality issues for repeat buyers |
| Identifying retention risks        | Rising refund rates among repeat buyers can predict churn                 |
| Product category analysis          | Segment by product to find categories with higher repeat-buyer returns    |
| Benchmarking against new customers | Compare to NC Refund % to understand segment behavior                     |

***

## Related Metrics

| Metric                                                                                                   | Relationship                                     |
| -------------------------------------------------------------------------------------------------------- | ------------------------------------------------ |
| [Refund %](/reference/metrics/orders/adjustments/refund-percent)                                         | Overall refund rate across all customers         |
| [New Customer Refund %](/reference/metrics/orders/adjustments/new-customer-refund-percent)               | Compare acquisition vs retention refund behavior |
| [Returning Customer Refunds](/reference/metrics/orders/adjustments/returning-customer-refunds)           | Dollar amount refunded to returning customers    |
| [Returning Customer Refund Count](/reference/metrics/orders/adjustments/returning-customer-refund-count) | Numerator in this formula                        |

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