The e-commerce return rate is a key KPI for understanding a product catalog’s performance, the quality of the customer experience, and the impact of returns on profit margins. But to be useful, it must be calculated using a consistent method and interpreted according to the industry, time period, and type of return. This article explains how to calculate the return rate, compare 2026 benchmarks, and identify the causes to analyze in order to address preventable returns.
The e-commerce return rate is often tracked simply as a percentage. However, that same number can mask very different realities: a significant issue, a fulfillment error, a failing supplier, a delivery delay, or simply a product category that is naturally more prone to returns.
- What is the e-commerce return rate?
- How do you calculate your e-commerce return rate?
- What will the average e-commerce return rate be in 2026?
- What is a good return rate for your e-commerce business?
- How do you analyze an excessively high return rate?
- What causes a high return rate?
- How can you address preventable returns?
- Which KPIs should you track alongside the return rate?
- How can you ensure the reliability of return data?
Before trying to determine whether your rate is “good” or “bad,” you need to understand what it actually measures. Whether calculated by item, by order, or by value; the analysis period; industry benchmarks; or segmentation by SKU or reason—the method completely changes how the results are interpreted.
The goal of this article is to help you develop a reliable metric, compare it accurately, and—most importantly—use it to identify the causes that warrant action.
What is the e-commerce return rate?
The return rate measures the proportion of sales that result in a return. However, there are several different methods, depending on the objective.
Definition of the Return Rate
To analyze products, the most useful method is to compare the number of returned items to the number of items sold:
- Return rate per item = number of items returned / number of items sold × 100
This definition allows you to analyze an SKU, a size, a category, or a collection.
Other companies tend to track orders that resulted in a return or the financial value of returns. These metrics are relevant, but they don’t measure exactly the same thing.
Why this KPI should be interpreted with caution
A high rate isn’t necessarily bad. A fashion brand facing sizing issues doesn’t have the same returns structure as a furniture retailer. A company that prioritizes exchanges may also record a high number of physical returns while retaining more revenue.
To interpret this KPI correctly, three factors must remain consistent:
- the unit of measure: item, order, or value;
- the time frame: sale date, return date, or cohort;
- the scope: category, channel, country, warehouse, or customer population.
Without these details, two companies could both report a 10% rate while actually measuring two different realities.
How do you calculate your e-commerce return rate?
Calculating the return rate depends on the question you want to answer.
Formula for the return rate per item
If you sold 10,000 items and 800 were returned: 800 / 10,000 × 100 = 8%
Your return rate per item is 8%.
This method is particularly useful for identifying products or categories that generate an unusually high number of returns.
Rate per order, per item, or per value: which method should you choose?
| Method | Formula | Use for |
| Per item | Returned items / sold items | SKU, size, category |
| Per order | Orders with returns / delivered orders | Frequency of return cases |
| By value | Returned value / sold value | Financial impact |
An order containing five items, only one of which is returned, represents a 20% return rate when viewed by item. When viewed by order, it counts as an order with a return.
This is why a company must document its internal policy and maintain it over time.
What time period should be used to avoid a misleading calculation?
Dividing the returns received in September by the sales made in September creates a bias.
Some returns received in September stem from purchases made in August. Conversely, sales made in late September may result in returns in October.
When the data allows it, prioritize a cohort analysis: link returns to the sales from which they originated. This method is particularly important after sales events, Black Friday, or Christmas.
Calculation Example
You sold 20,000 items in April. Once the return window had passed, 1,600 of those items were returned.
- 1,600 / 20,000 × 100 = 8%
Your e-commerce return rate per item is 8%.
If these returns involve 1,000 orders out of 12,500 delivered orders, your return rate is also 8% in this example. This coincidence is purely coincidental: in a real-world scenario, the two rates generally differ.
What will the average return rate be in e-commerce in 2026?
There is no universal average e-commerce return rate. The available benchmarks use different methods and scopes.
Benchmarks Available in France and Internationally
For France, Shopify reports that in 2026, approximately 17.6% of online sales were returned. This figure is influenced by various factors, such as dissatisfaction with the product, sizing issues, or discrepancies between the online description and the product received.
At the European level,the European Environment Agency estimates that 20% of clothing purchased online is returned. For shoes, the average estimate is 30%. The agency defines this rate as the number of returned items relative to the number of items sold.
Returnless, for its part, reports an average return rate of 23.5% for orders in 2025, based on a benchmark that combines data from more than 1,250 European stores, data from its platform, and market data.
Return Rates by Sector
| Scope | Average rate |
| Apparel, France | between 18% and 25% |
| Apparel, Europe | 20% |
| Footwear, Europe | approx. 30% |
| E-commerce, Europe | 23.5% |
| Online retail, United States | 19.3% |
These figures show why a 2026 e-commerce returns benchmark must always be accompanied by its methodology.
Why aren’t benchmarks always comparable?
A rate of 20% per item, a rate of 23.5% per order, and a rate of 19.3% by value can all be accurate. They simply do not measure the same thing. Geographic region, product type, the sample studied, the time period, and how returns are handled can also significantly alter the results.
Therefore, never turn an overall average into an automatic target.
What is a good return rate for your e-commerce business?
The right question isn’t, “What rate should I aim for?” but rather, “Is my rate consistent with my business, and is it trending in the right direction?”
Compare Yourself to Your Industry
Start by comparing similar categories.
A 12% rate may be high for a category with low return rates and lower than the benchmarks available in the fashion industry. The more diverse the catalog, the less useful an overall average is.
Building Your Own Internal Benchmark
Your historical data is often more relevant than an external average.
If a category increases from 6% to 9% using the same formula and scope, that’s a signal worth analyzing—even if 9% is still below the industry benchmark.
In particular, track changes following a switch in suppliers, a new product listing, a launch, or a campaign.
To make this benchmark truly useful, always use the same methodology and compare, in particular:
- trends for a specific SKU or category over time;
- normal periods, such as sales, Black Friday, or product launches;
- performance before and after a change in supplier or product listing;
- different channels, countries, or customer cohorts.
The goal is not to systematically look for the lowest rate. It is to detect an abnormal trend and identify its cause.
Incorporating Margin, Product Value, and Sales Strategy
Two returns do not have the same cost.
A high-margin item quickly restocked may have a limited impact. A low-margin product requiring inspection, repackaging, and a price reduction can be much more costly.
It’s also important to distinguish between returns and lost revenue. An exchange retains more value than a full refund.
How do you analyze an excessively high return rate?
The overall rate serves as a warning sign. The analysis begins when you break it down into segments.
To move from a number to an actionable cause, prioritize examining:
- the SKU and category, to identify the products with the highest number of returns;
- the channel, country, and campaign, to identify issues related to sales or customer acquisition;
- the reason for return, to distinguish between size, quality, fulfillment, or delivery;
- the supplier, to detect a quality issue common to several SKUs;
- the cohort, to pinpoint exactly when the decline began.
An overall return rate of 12% becomes much more useful when you discover, for example, that a single SKU has had a 28% return rate since switching suppliers.
Segment by SKU and category
Identify the SKUs that account for the majority of returns.
An SKU with a 25% return rate in a category with an 8% return rate is much more actionable than an overall average of 10%.
Next, narrow down by size, style, or collection.
Segment by channel, country, and campaign
The same product may perform differently on your website, a marketplace, and another channel.
A campaign can also attract a different customer profile or create expectations that are misaligned with the product.
Finally, the country helps identify differences related to purchasing habits, delivery, or sizes.
Segment by reason for return
Return reasons turn a rate into an explanation.
“Too small,” “damaged item,” “wrong product received,” and “product different from photos” call for four different actions.
If a large proportion of customers select “other,” the problem may also stem from the quality of your data.
Analyze by supplier and by cohort
A spike concentrated among several items from the same supplier may indicate a quality issue. Cohort analysis also makes it possible to pinpoint exactly when the rate began to decline: a new collection, a change in supplier, a product sheet redesign, or a change in logistics operations.
What factors contribute to a high return rate?
A return may be due to the product, preparation, delivery, or purchasing behavior.
Size, features, and product information
In fashion and footwear, size and style are important factors. The European Environment Agency reports that 70% of clothing returns analyzed in the sources it summarizes are related to perceived issues with size or style. Inaccurate measurements, misleading photos, or poorly described materials can also create a gap between expectations and reality.
Quality, Nonconformity, and Fulfillment Errors
A defective product is more likely related to quality or the supplier. A wrong SKU, the wrong size, or an incorrect quantity point to issues with logistics execution.
Delivery and Customer Promise
A product may be in compliance but still be returned because it arrives too late or is damaged. It is therefore important to cross-reference returns with shipping data rather than automatically attributing the problem to the product.
Bracketing and Planned Behaviors
Some customers order multiple sizes or variants with the intention of keeping only one.
These behaviors must be distinguished from returns caused by a product defect or a logistics error. The goal is not to penalize all returns, butto identify their cause.
How can we address preventable returns?
Reducing the return rate is not an end in itself. We must focus on the causes that the company can actually address.
| Reason | Data to be verified | Action |
| Incorrect size | SKU, size, pattern | Check size guide |
| Product does not meet expectations | Product description, photos, design | Correct product details |
| Wrong product received | Order, fulfillment | Strengthen picking controls |
| Defective Product | Batch, supplier | Quality control |
| Return after delay | Carrier, lead time | Review commitment and performance |
| High bracketing | Variants, history | Adapt route and rules |
Do not tighten a return policy to correct a planning error. The cause should determine the action.
What KPIs Should You Track Along With the Return Rate?
The e-commerce return rate should be supplemented with financial and operational metrics.
Refund Rate and Exchange Rate
The e-commerce refund rate measures the percentage of orders that result in a refund. The exchange rate indicates the ability to retain the sale in another form.
Average cost per return
The cost per return may include shipping, processing, inspection, repackaging, customer service, and markdowns. Two companies with the same return rate may therefore experience very different financial impacts.
Processing and Restocking Time
The time to restock measures how long it takes for a returned item to become available for sale again. The longer this time is, the longer inventory is tied up, and the greater the likelihood of markdowns.
Percentage of Resalable Products
Also track the percentage of returns that are returned directly to inventory, reconditioned, marked down, or deemed unsellable. This metric helps measure the actual value recovered.
How can you ensure the reliability of returns data?
With high volumes, the main challenge is no longer just counting returns. You need to be able to link each case to its cause and resolution.
Standardize reasons and resolutions along the customer journey
Reasons must be simple for the customer but precise enough to be analyzed. The same logic applies to resolutions: refunds, exchanges, or store credit must be structured.
Baback intervenes on the customer journey side to organize requests, reasons, and resolutions.
Link the request, order, product, and logistics fulfillment
The customer’s statement isn’t always enough to understand the problem.
To analyze a “wrong product received” issue, you need to trace it back to the order, the SKU, and what actually happened during order fulfillment, quality control, and shipping.
This is where a structured logistics framework comes in handy. At Shippingbo, theOMS centralizes and orchestrates orders, the WMS manages inventory, order fulfillment, quality control, and restocking, while the TMS manages shipping and carrier flows. The challenge is not to simply pile on tools, but to link return data to the entire logistics process.
This continuity makes it possible to determine whether a return is due to a product defect, a packaging error, a shipping incident, or another issue along the supply chain.
Using Data to Address Root Causes
Data becomes valuable when it triggers action: correcting a product listing, auditing a supplier, modifying a preparation step, or revising a delivery promise.
That’s when the return rate becomes a true operational KPI.
3 fonctionnalités pour mieux suivre et piloter vos retours e-commerce
Pour analyser correctement votre taux de retour, encore faut-il disposer d’un parcours retour structuré : demande client, motif, étiquette transport, suivi du colis et traitement associé. Shippingbo aide à fiabiliser ces étapes pour transformer chaque retour en donnée exploitable.
Gestion des retours omnicanal
Structurez vos demandes de retour, suivez les motifs, analysez les résolutions et transformez vos retours clients en données exploitables pour identifier les causes récurrentes.
Découvrir →Impression d’étiquettes transport
Facilitez le renvoi des produits avec des étiquettes transport prêtes à imprimer ou à transmettre aux clients, afin de fluidifier les retours, échanges et réexpéditions.
Découvrir →Suivi de colis
Suivez l’acheminement des colis retour, rassurez vos clients et gardez une meilleure visibilité sur les retours en transit, reçus ou en attente de traitement.
Découvrir →Moving from Return Rate to Diagnosis
An e-commerce return rate is only meaningful if the methodology remains consistent.
Define your unit of measurement, time frame, and scope. Use benchmarks to gauge your performance, then analyze your historical data to detect anomalies. Above all, drill down to the SKUs, reasons, channels, suppliers, and cohorts that actually drive the average. As volumes increase, this analysis requires linking what the customer reports to what is actually happening in the logistics process.
Baback helps organize requests, reasons, and resolutions related to the customer journey. Shippingbo helps link this data to orders, logistics tracking, and restocking.
Request a demo of our returns management solution to better organize return reasons, link customer requests to logistics operations, and ensure the reliability of the data needed for management:
FAQ
For a product analysis, divide the number of returned items by the number of items sold, then multiply by 100. If possible, link returns to their original sales period.
There is no universal average. For example, the European Environment Agency estimates the rate at 20% for clothing sold online in Europe, while the NRF estimates that 19.3% of U.S. online sales will be returned by 2025. Since the methods used differ, these figures should not be compared directly.
A good rate is one that aligns with your product category, sales history, profit margin, and business strategy. External benchmarks serve as a reference point, not a universal goal.
The rate per item is preferable for analyzing the catalog. The rate per order is more useful for measuring the frequency of returns. You can track both, but be sure to label them clearly.
First, segment your returns by SKU, category, reason, channel, country, supplier, and cohort. Then address the preventable causes identified: product information, quality, fulfillment, or delivery. Glossary Bracketing: the practice of ordering multiple sizes, colors, or variants of the same product with the intention of returning some of them. Cohort: a group of orders or customers sharing a common characteristic, such as a purchase period, a campaign, or a product launch. Denominator: The basis used to calculate a rate. In the case of returns, this can be the number of items sold, the number of orders, or the sales value. KPI: key performance indicator used to track progress toward a goal or activity. Picking: the process of retrieving products from the warehouse to prepare an order. SKU: unique identifier used to precisely identify a product or product variant in inventory. Return rate by value: the proportion of the financial value of returned products relative to the total value of products sold. Wardrobing: the practice of purchasing a product, using it temporarily, and then returning it to obtain a refund.
Glossary
Bracketing
The practice of ordering multiple sizes, colors, or variations of the same product with the intention of returning some of them.
Cohort
A group of orders or customers sharing a common characteristic, such as a purchase period, a campaign, or a product launch.
Denominator
The basis used to calculate a rate. In the case of returns, this can be the number of items sold, the number of orders, or the sales value.
KPI
Key performance indicator used to track progress toward a goal or activity.
Picking
The process of retrieving products from the warehouse to fulfill an order.
SKU
A unique identifier used to precisely identify a product or product variant in inventory.
Return Rate by Value
The proportion of the financial value of returned products relative to the total value of products sold.
Wardrobing
The practice of purchasing a product, using it temporarily, and then returning it for a refund.