Most Brands Are Looking at Returns Data the Wrong Way

Here's what they should be focussing on

Returns are an inevitable part of retail, but too many brands see them as a cost to be minimised rather than an opportunity to optimise. In previous discussions, we've explored how returns data can inform everything from product development to customer experience. But this time, we’re focusing on something more immediate: how returns data should guide critical business decisions—specifically, whether to implement paid returns policies.

While most businesses obsess over sales figures, their approach to returns data is often flawed—leading to knee-jerk reactions, ill-advised policies, and unnecessary revenue loss. The problem? They're looking at the wrong numbers.

The Wrong Way: Viewing Returns Through a Financial Lens

A common mistake is analysing returns as a purely financial metric—comparing sales and refunds in the same month. This skews the reality of return rates, particularly around major sales periods. Take Black Friday: a brand may see a spike in December refunds and panic, assuming that Black Friday shoppers are serial returners. In reality, these returns are simply a delayed consequence of the November sales boom. The data looks worse than it is, leading to rash decisions like introducing paid returns without real justification.

The Right Way: Cohort-Based Returns Analysis

To get an accurate picture of returns, brands need to stop measuring returns by when they happen and start measuring them by when the original purchase was made. This is known as cohort-based analysis, and it provides a far more accurate view of product performance, customer behaviour, and return rates.

How to Implement Cohort-Based Returns Analysis

1. Track Data at the Order Level – Capture purchase dates, product details, and return dates to create a true timeline of returns.

2. Integrate Returns & Sales Data – Use tools like Retool to combine returns data with sales data from platforms like Shopify, ensuring a full-picture view.

3. Use Visualisation Tools – Business intelligence platforms like Metabase or Looker can help track return rates by purchase cohort, giving brands a clearer sense of when and why returns happen.

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Using Analytics and Insights on Returns Data

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For Instance: The Black Friday Returns Myth

Let’s take for instance, a major fashion retailer assumed Black Friday customers were returning at unsustainable rates. However, a cohort-based analysis revealed that their Black Friday return rates were actually lower than other periods. The spike in December returns was simply a reflection of November’s higher order volume, not an indication of poor purchase intent. By analysing the data correctly, the brand avoided rolling out an unnecessary paid returns policy.

In this mock-up we can see that the overall return rates don’t necessarily line up to a month-by-month financial comparison. While this is a mock-up, it’s very typical of something we’ll see in brand.

Why This Approach Works

• Better Business Decisions – Cohort-based analysis helps brands avoid reactionary policies that hurt customer experience.

• Smarter Inventory Management – Knowing the real return rate for each product informs better stocking decisions.

• Reduced Return Rates – Identifying why customers return products (e.g., sizing issues, poor product descriptions) allows brands to make proactive improvements.

Final Thoughts

Returns data isn’t just about damage control—it’s a source of strategic insight. Brands that rely on traditional financial reporting risk drawing the wrong conclusions and making costly mistakes. Those that embrace cohort-based analysis, however, gain a clearer view of return patterns, enabling them to reduce costs, improve customer experience, and ultimately, drive more profitable growth.

For more on optimising returns, check out our articles on The Anatomy of an Online Return and Using Analytics and Insights on Returns Data.