Return analytics: how to turn return reasons into business decisions

Why return data is a goldmine most stores ignore

Returns research consistently finds the same thing: the majority of returns are not fraud or regret — they are information failures. The product did not match the photo, the size chart was optimistic, the color was darker in real life. Narvar frames the return as a 'reverse checkout': the moment a customer tells you, in structured form, exactly why your product page failed them.

A store handling 500 returns a month is collecting 500 answers to the question 'what went wrong?'. Aggregated over 90 days, that is more actionable product feedback than most review tools produce in a year.

• Return reasons are customer-reported defects in your product content.
• Variant-level patterns (e.g. 'size M always comes back') expose broken size charts.
• Country patterns expose shipping, customs or market-fit problems.
• Exchange targets show what customers actually wanted — free assortment research.

The five decisions return analytics should drive

Return data only pays back when it changes something. These are the five decisions it should feed every month.

• Product pages: if 'smaller than expected' dominates a product, rewrite the size guide and the fit copy — before buying the next batch.
• Purchasing: a product with a high return rate and low exchange rate is a candidate for delisting, not discounting.
• Photography: 'looked different from the photo' is a shoot-brief fix, not a product defect.
• Market rules: if one country's return rate is double the others, review carrier, delivery times and expectations on that market.
• Exchange nudging: if most exchanges move one size up, default the portal's suggestion accordingly.

What to measure — the minimum viable dashboard

You do not need a data team. Four numbers, reviewed monthly, cover most of the value.

• Return rate by product and variant — not just store-wide.
• Reason mix over time — is a change you made actually moving the number?
• Exchange rate — the share of returns resolved as exchanges (retained revenue).
• Refunded value — what returns actually cost after fees and exchanges.

Where AI actually helps

AI is not magic here — it is a reader. Its job is to compress hundreds of return reasons, variants and customer comments into a short list of actions a merchant can take this week. In Nexly, the AI reads the aggregated statistics per store and per product and answers with a headline, ranked insights and concrete next steps: which size chart to fix, which product page to rewrite, which product to flag to your supplier.

The practical gain is time-to-decision. A pattern that takes an afternoon of pivot tables to find — 'size M returns cluster in one color, and the comments mention the sleeves' — surfaces in seconds, in the language the store runs on.

A monthly routine that works

Block 45 minutes on the first Monday of the month. Open your return analytics, sort products by return count, and for the top five answer one question: is this a content problem, a size problem, or a product problem? Write down one fix per product. That is it — the stores that do this consistently see their reason mix shift within a quarter, because the biggest return drivers are usually stable and fixable.

Nexly builds this routine in: the Analytics panel aggregates reasons, products, variants, countries and trends automatically, and the AI recommendations turn them into a written action list you can hand to whoever owns the catalogue.

About the numbers

The NRF figures are survey-based US retail data and are used here for scale, not as a Danish benchmark. Return-reason distributions vary by category — apparel skews heavily toward size and fit, electronics toward expectations and defects. Use your own reason mix as the truth; external benchmarks only tell you what is plausible.

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