Return scoring without a data team: the five signals that matter first
Return scoring sounds like a machine learning project, which is why most brands never start. In practice, five behavioral signals pulled straight from your own order data will catch the large majority of abuse. No models, no data science team, just a spreadsheet and your return records.
Signal one: lifetime return rate. Not the rate on the last order, the rate across every order the customer has ever placed. A customer who returns 60 percent of what they buy over a dozen orders is a different case from one who returned one item last month. Lifetime rate is the backbone of every scoring system because it is stable, simple, and hard to game. Customers cannot reset it by opening a new email address if you also track shipping addresses.
Signal two: claim frequency. How often does this customer file damage, loss, or wrong-item claims relative to their order count. Honest customers almost never file claims; the pattern of serial claimants stands out immediately. One claim per twenty orders is normal noise. One claim per three orders is a story.
Signal three: item condition on return. This one takes a little warehouse discipline, but it pays for itself. Track whether returned items come back resalable, worn, damaged, or with tags removed. A customer whose returns consistently arrive worn is telling you exactly what they are doing with your products. Condition data turns vague suspicion into documented fact.
Signal four: timing patterns. Returns filed the week after every holiday, the same category returned right after seasonal events, orders placed and returned on a suspiciously regular cycle. Timing is where intent shows. Nobody accidentally returns five dresses the week after wedding season three years in a row. A simple flag for event-adjacent returns catches wardrobing that rate-based rules miss entirely.
Signal five: order-to-return velocity. How fast does the item come back after delivery. Wardrobed items often return at the edge of the return window, worn once for the occasion. Bracketed items return fast, in multiples, in several sizes. Velocity is easy to compute from timestamps you already have, and combined with the other four signals it separates fitting behavior from wearing behavior.
Score these five with simple weights. Lifetime return rate and claim frequency carry the most weight. Condition and timing adjust the score up or down. Velocity is the tiebreaker. The output is a score per customer that updates with every order and return, and it routes decisions: low scores get instant approval, mid scores get manual review, high scores get warned or denied. Start with round numbers, watch the outcomes for a month, and adjust. The system improves as you feed it decisions, because every reviewed return teaches you where the line should be.
None of this requires new software to begin. A weekly export of orders and returns, five columns, and someone who looks at the top of the list will find the accounts that are costing you real money. The sophistication can come later. The signals are already in your data.