---
title: "Loop Returns integration"
description: "SourceMedium syncs Loop Returns data and joins every return to its original order and customer — so you can analyze return rates, exchange vs refund split, return reasons by SKU, and how post-purchase outcomes affect long-term retention."
url: "https://sourcemedium.com/integrations/loop-returns"
---

# Returns and exchanges, connected to the full customer journey

SourceMedium syncs Loop Returns data and joins every return to its original order and customer — so you can analyze return rates, exchange vs refund split, return reasons by SKU, and how post-purchase outcomes affect long-term retention.

## Integration Snapshot

- Platform: Loop Returns
- Category: Customer Success & Operations
- Availability: Generally available
- Documentation: [View docs](https://sourcemedium.com/docs/data-inputs/platform-integration-instructions/loop-returns-integration)

## Why teams connect Loop Returns

Loop Returns is the source of truth for return events. Shopify's refund data lags and misses exchange details entirely. SourceMedium connects Loop to your full order history so you can measure true return economics and understand which return outcomes predict loyalty vs churn.

## Key capabilities

- Return outcome analysis: Exchange, refund, upsell, store credit, and mixed outcomes sync with full financial detail per return.
- Return reason hierarchy: Two-level return reason data at the SKU level reveals the root cause behind your return rates.
- Post-purchase retention: Returns join order history and customer LTV so you can measure whether exchange customers repurchase at higher rates.
- Return timing and cost: Days to return, return timing bands, and net return cost (refund minus upsell capture) sync per return.

## Example BigQuery tables

- `fct_returns`: Return fact table joined to orders and customers
- `rpt_order_returns_v1`: Order-grain Return Order and Exchange Order metrics

## Common enrichments

- Exchange vs refund split with full financial breakdown
- Return reasons by SKU at two-level hierarchy
- Days to return and return timing buckets
- Net return cost (refunds minus upsell capture)
- Return outcomes linked to 90-day repurchase rate

## Questions you can ask

- What percentage of returns result in an exchange vs a refund?
- Which SKUs have the highest return rates this quarter?
- What are the top return reasons by product category?
- Do customers who exchange repurchase at higher rates than those who refund?
- Which acquisition channels produce orders with the lowest return rates?
- How does return timing differ between exchange and refund outcomes?
- What is our net return cost as a percentage of gross revenue?

## Joins with

- [Shopify](https://sourcemedium.com/integrations/shopify)
- [ReCharge](https://sourcemedium.com/integrations/recharge)
- [Stay AI](https://sourcemedium.com/integrations/stay-ai)

## How it works

1. Connect Loop Returns: Share your Loop Returns API key. We pull return records, outcomes, and line-item data.
2. Return data sync: Returns sync with outcome, financials, reason hierarchy, and exchange variant data.
3. Post-purchase analysis: Return data joins your order and customer models for full lifecycle analysis.

## Related integrations

- [Shopify](https://sourcemedium.com/integrations/shopify)
- [ReCharge](https://sourcemedium.com/integrations/recharge)
- [Stay AI](https://sourcemedium.com/integrations/stay-ai)
- [Gorgias](https://sourcemedium.com/integrations/gorgias)