Executive Summary
The Rise of Retention-Led Ecommerce
Rising operational costs and higher customer expectations mean it takes more than luck, or even a great product, for ecommerce merchants to stay ahead.
The brands winning today share a common focus. Instead of chasing growth alone, they prioritize revenue retention. By recovering lost revenue and creating new value through the post-purchase experience, they are building resilience that helps them navigate uncertainty and protect margins.
The post-purchase experience, especially exchanges, refunds, and returns, plays a critical role in this strategy. Returns are not just operational moments. When handled well, they directly influence customer loyalty and whether revenue stays in the business.
Improving revenue retention helps offset rising customer acquisition costs, protect margins, and drive repeat purchases. The strongest merchants design returns intentionally, using them to reduce friction, build trust, and keep customers coming back.
In this report, we dig into what high-performing brands are doing differently by leveraging data, tools, and technology to turn returns into retention. You’ll see how streamlined, transparent processes; customer-centric design; and smart automation make revenue retention a repeatable advantage.
Loop is uniquely positioned to publish these benchmarks. As a leading commerce operations platform trusted by more than 5,000 Shopify brands, Loop supports merchants across both pre- and post-purchase moments. This end-to-end view provides rare insights into how top-performing brands retain customers and protect revenue at scale.
By connecting returns, exchanges, fraud prevention, order tracking, delivery promise, and analytics, Loop helps brands turn everyday commerce operations into growth levers. While this report focuses on retention through returns, the insights reflect a broader, full-journey approach to building durable ecommerce growth.
"Ecommerce is changing quickly, and customers are gravitating towards the brands who get them. These brands are gathering data and using it to operate smarter. They’re proactively sharing recommendations to their customers, automating the return and exchange process, and making customer service more delightful. The numbers in this report show the incredible revenue opportunities for brands that invest in the “boring” parts of their ecommerce businesses, which are really the parts that customers notice the most."
Hannah Bravo, CEO at Loop
Loop by the numbers
Methodology and Key Definitions
How we collected, analyzed, and categorized the data
This report uses Loop data from over 23.4 million returns from more than 4,000 Shopify merchants globally using Loop’s Returns Portal, spanning nine verticals, collected from November 1, 2024 to October 31, 2025. This data includes a set of metrics analyzed across several dimensions, including vertical and geographic region, as defined below.
Our metrics exclude any returns blocked before submission, including final sale items and those stopped by workflows. Counting blocked returns would artificially inflate reported performance metrics. We avoid that practice to keep our reporting transparent and ensure accuracy across all return-related measurements on returnable products.
Definitions of key metrics
Repeat Purchase Rate
This metric measures whether shoppers who made a return went on to purchase again later. Results exclude exchanges and merchants without sufficient data or key feature adoption
Adopted
Merchant has used the feature on at least 50 returns in the last year
Variant Exchanges
A return outcome where a shopper exchanges an item for a different variant of the same product, such as a new size or color. In this report this definition also includes Advanced Exchanges (which act as variant exchanges but with more customizable exchange item options under the hood for the shopper to choose from)
Bonus Credit
A dollar credit to incentivize an outcome such as exchange and store credit to improve revenue retention
Shop Now
Shop Now allows shoppers to exchange an item they bought for any other product in the store catalog
Shop Later
Shop Later allows customers to convert a refund into store credit, after the refund has already been requested. This method enables brands to convert more refunds into retained revenue
Average Revenue Retained per Shop
Total Revenue Retained ($) ÷ Total Shops, within the timeframe
Average Revenue Retained per Return
Total Revenue Retained ($) ÷ Total Returns, within the timeframe
Attributed Orders/Revenue
The estimated total orders ÷ revenue which is attributed to a user's interaction with the tracking page
% of Merchants Offering Exchanges
Merchants who have adopted exchanges, Shop Now, and/or store credit as an option for shoppers ÷ total number of merchants
% of Merchants Offering Shop Now
The number of merchants who have adopted Shop Now ÷ total merchants eligible for Shop Now
% Shop Now Merchants Offering Bonus Credit:
The number of merchants who have adopted Bonus Credit on Shop Now ÷ total merchants with Shop Now
Average Shop Now Bonus Credit
The average amount of Bonus Credit used on Shop Now returns, normalized to USD
% Merchants Charging Fees on Returns
The number of merchants who have adopted fees on returns ÷ total number of merchants
Average Fee Charged on Returns
The average fee amount charged to shoppers on returns, normalized to USD
Methodology
Overall brand criteria
Revenue retention top performers methodology
Loyalty methodology
Quantifying revenue retained methodology
Benchmarks and Key Findings
Benchmarks that provide context, not prescriptions
These benchmarks are designed to provide context. They show where strategies converge, where they diverge, and how high-performing brands approach policies, fees, exchanges, fraud prevention, and post-purchase flexibility across the full customer journey.
There is no single “right” approach to returns and post-purchase strategy. What works depends on your vertical, customer expectations, operational constraints, and stage of growth.
This section establishes baseline benchmarks to help you understand how your returns and post-purchase performance compares to brands most similar to yours. Using aggregated data from more than 4,000 merchants, powered by insights from our Return Policy Insights tool, we examine common patterns and meaningful differences across key dimensions, including vertical and geographic region.
The sections that follow in this report build on these benchmarks with deeper analysis focused specifically on retention through exchanges and returns. Here, the goal is orientation: giving you a clear point of comparison before diving into strategy.
Across the board
Baseline trends across all merchants
Returns are no longer refund-first
Most merchants have already moved beyond refund-only policies. 73.6% of Loop merchants now offer exchanges, and 49.2% offer Shop Now, giving shoppers a way to stay engaged instead of exiting the experience. Among merchants using Shop Now, over half (51.7%) pair it with Bonus Credit, with an average incentive of $11.28, showing that brands are not just adding options, but actively guiding customers toward higher-retention outcomes.
Fees are becoming targeted, not universal
Free returns are no longer the default. 65.2% of merchants charge return fees on at least some outcomes, with an average fee of $9.04. At the same time, merchants continue to offer generous decision windows, with an average of 39 days to request a refund and 41 days for an exchange. This reflects a more intentional balance: preserving customer trust while protecting margins and managing reverse logistics costs.
Fraud prevention is moving earlier in the process
As return volume increases, so does the need for proactive risk management. Across the dataset, 11.4% of return value is flagged as high risk, with an average fraudulent return value of $120. Rather than applying blanket restrictions, merchants are increasingly using data-driven controls to identify abuse early, allowing them to maintain flexible, customer-friendly policies for the majority of legitimate shoppers.
Policy settings and feature usage across all merchants
Proactive Fraud Prevention
Returns fraud and policy abuse continue to represent a significant and growing challenge for ecommerce brands, contributing to billions in annual losses across the industry. As return volumes increase, so does the need for earlier and more accurate identification of potentially abusive behavior.
Across the dataset analyzed, nearly 12% of return attempts were flagged as high risk, with an average value of $120 per high risk return. These flags are generated through Loop’s fraud detection tooling and represent a meaningful source of revenue leakage, particularly for brands with higher average order values or more flexible return windows.
Patterns in the data show that fraud risk is not evenly distributed. Certain verticals and regions experience higher rates of flagged returns, while others see fewer incidents but significantly higher average fraud value. These differences highlight the importance of tailoring fraud prevention strategies based on business model, product type, and customer behavior, rather than relying on “one-size-fits-all” controls.
As brands look to protect margins without introducing unnecessary friction for legitimate customers, proactive and data-informed fraud detection has become an essential component of a modern post-purchase strategy.
How to use this data
01
Identify your peer group
Find the segments that most closely match your business across vertical and primary region. These benchmarks are most useful when viewed in the context of brands facing similar customer expectations and operational constraints.
02
Compare your returns strategy
Evaluate how your feature usage, return fees, and refund and exchange windows stack up against peers in those segments. Look for areas where your approach meaningfully over- or under-indexes.
03
Assess risk and exposure
Review your fraud rate and the average value of flagged returns relative to peer benchmarks. Differences here can signal opportunities to tighten controls or safely expand flexibility.
04
Prioritize intentional improvements
Once you understand where your strategy diverges, identify which changes will have the greatest impact on retention, loyalty, or margin protection.
The remainder of this report builds on these benchmarks with deeper analysis, showing how top-performing brands turn these patterns into measurable retention-focused outcomes.
Best-in-Class Strategies
What top-performing brands do differently
Top-performing brands don’t treat returns as a necessary cost. They treat them as a moment of leverage.
Across revenue retention, loyalty, and repeat purchase behavior, the same pattern emerges: brands that design returns with flexibility, incentives, and intentional controls consistently outperform those that rely on rigid, refund-first policies. The next sections below break down how these strategies show up in practice, and where they create the biggest impact.
