Seasonal return scoring: why your thresholds need a Q4 setting
Return scoring models are usually calibrated on average months, then left alone. November and December are not average months. Order volume triples, gifting changes why people return things, and every threshold tuned for September starts misfiring in both directions at once.
The fix is not a new model. It is a seasonal configuration: the same scoring logic with adjusted baselines, multipliers, and queue priorities for the holiday window. Think of it as winter tires for your returns operation.
Baselines move, so thresholds must move
A customer returning forty percent of a holiday order looks like a bracketing abuser against a September baseline. Against a December baseline, where gifting and size-guessing push everyone's return rate up, that same customer is ordinary. Static thresholds flag the merely seasonal and miss the genuinely abusive, because the genuinely abusive accounts hide inside the seasonal noise.
Recalibrate the key baselines for the holiday window using last year's Q4 data: average return rate by category, average order-to-return lag, normal wardrobing-adjacent signals like wear indicators. Score December behavior against December norms, not September ones.
Category-specific seasonal multipliers
Not every category seasons equally. Gifting categories like accessories and outerwear see return rates spike as recipients exchange; basics and replenishment categories barely move. A single global seasonal multiplier overcorrects some categories and undercorrects others.
Set the multiplier per category from last year's data, and review it weekly through the season. Early December data will tell you quickly if a category is running hotter than last year, which is also when abusers probe for the categories where your thresholds are loosest.
Tighten the high-risk signals, loosen the honest ones
Seasonal adjustment is not just raising every threshold. The right move is asymmetric: loosen thresholds on signals that go seasonal for honest reasons (higher return rates, faster repeat purchases, gift messages), while tightening thresholds on signals that do not go seasonal for anyone (empty-box claims, wrong-item claims on sealed products, new accounts with immediate high-value returns).
Abusers count on holiday chaos to cover exactly those high-risk signals. A December empty-box claim deserves more scrutiny than a September one, not less, because the honest version of that claim does not spike with the season.
Protect the review queue from drowning
The operational risk of Q4 is queue collapse: volume triples, the review team does not, and either everything gets auto-approved or the queue backs up for weeks. Seasonal scoring should explicitly manage queue load: raise the auto-approve ceiling for low-risk segments so reviewers spend their hours on the high-risk tail.
Set a queue-depth tripwire. If manual reviews pending exceed a set number, the system should automatically narrow the review criteria to the highest-risk signals rather than letting the backlog age. A two-week-old review is barely a control at all.