Return Data Cleanup: Fixing the Reason Codes Your Team Stopped Trusting
Direct answer: Return reason data rots in a predictable way: the code list grows by exception, staff pick the fastest option under queue pressure, and other quietly becomes a top three reason, which means a top three reason is unknown. A cleanup starts with a count. Pull twelve months of reasons, rank them, and read two hundred free text comments under other by hand. Then merge true duplicates, split the one overloaded code everyone hides inside, and rewrite labels in customer language. Protect history by mapping old codes to new ones in a table, never by editing past records.
Ask a returns team what their top return reason is and watch the pause. They know the report says size. They also know size is where three other problems go to be filed quickly.
Bad reason data does not just mislead reports. It misdirects product fixes, sizing work, and fraud rules built on top of it. Cleaning it is unglamorous and compounding.
Audit before you touch a single label
Pull a year of returns with reason codes and free text where it exists. Rank codes by volume and by refund value, because they tell different stories. Then read the free text under other and under your top code, two hundred entries minimum, and tally what they actually describe. Most teams find a missing code in the first fifty comments: arrived damaged split from defective, or changed mind split from ordered by mistake.
Interview the people who assign codes, if your flow has staff assigning them. They will tell you which code is the fast one, which two codes overlap so much they coin flip, and which label customers never pick because nobody talks that way. That interview is worth more than the dashboard.
Merge, split, and rename, in that order
Merge duplicates first: codes that describe the same event in different words, often a legacy code and its replacement coexisting for years. Split second: one overloaded code hiding distinct causes, classically quality, which might mean stitching, fabric feel, or a zipper that fails in a week, three fixes owned by three different teams.
Rename last, in the words customers use. A code labeled dimensional discrepancy will be avoided by everyone, shopper and staff alike, in favor of size. Plain labels get picked accurately. Every rename and merge goes into a mapping table, old code to new code, kept permanently, so history stays comparable.
Protect the time series
The fear that cleanup breaks year over year reporting is legitimate and manageable. Keep the raw code on every historical record untouched. Report going forward on new codes, and report trends through the mapping table, accepting that a merged category shows a step in the chart the month you merged it. Annotate the step. A chart with an honest annotation beats a dashboard that stayed comparable by staying wrong.
Run old and new code sets in parallel for one return cycle if your system allows it, staff selecting both. The overlap month quantifies how much volume moves between categories, and turns arguments about the new taxonomy into arithmetic.
Keep it clean with a small governance habit
Reason lists rot because adding a code is free and nobody owns the list. Assign an owner, require a written case for new codes, and review the other rate monthly, with a hard line: if other passes a set share of returns, the taxonomy gets a review that quarter, automatically.
Resist codes that describe blame. Customer error as a category teaches nothing and poisons every downstream conversation. Describe events, not fault: ordered two sizes, arrived after event date, seal broken on arrival. Data written neutrally gets used by product, support, and fraud teams without a translation fight.
Related reading
Related reading: Flagging Serial Returners Without Punishing Your Best Customers in Disguise, Return Status Messages That Cut Where Is My Return Tickets, and Return to Vendor Triage: Deciding What Leaves Your Building and When.
Frequently asked questions
Why does other become a top return reason?
Because the list lacks a code for a real, common event, or the right code is labeled in language nobody uses, so shoppers and staff pick other or the fastest familiar code. Reading the free text comments under other usually reveals the missing category within fifty entries.
Will cleaning up reason codes break historical reporting?
Not if you map rather than edit. Leave historical records untouched, keep a permanent mapping table from old codes to new ones, and report trends through it. A merged category will show a visible step in the month of the change, which should be annotated rather than hidden.
How many return reason codes should a store have?
Enough to separate causes that different teams act on, few enough to pick accurately under time pressure. Many stores land between twelve and twenty primary codes, with structured sub reasons for the top categories. If staff cannot hold the list in memory, it is too long.
Who should own the return reason taxonomy?
One named owner, usually in operations or customer experience, with product and support consulted. New codes require a written case and an expected volume. Without an owner, the list grows by exception until other is a top reason again.