Holiday Return Score Spikes: Separating Seasonal Shoppers from Abusers
Every Q4, return scores spike. Gift recipients exchange sizes, holiday shoppers buy in bulk, and January brings the great return wave. The problem for scoring systems is that seasonal behavior looks exactly like abuse behavior: high return rates, short keep times, multiple sizes of the same item. A scoring model trained on the rest of the year will flag half your holiday customers as suspicious. The fix is not to turn scoring off for the holidays. It is to teach the model what a season looks like.
Why holiday returns break naive scoring
Return scoring works by comparing a customer's behavior to baselines. But holiday baselines are different from normal baselines in predictable ways. Return rates roughly double in January. Multi-unit orders spike because people buy gifts in several sizes. First-time buyers surge, and first-time buyers return more because they do not know your sizing yet. A model that does not know it is December treats all of this as risk. The result is false positives on your most valuable seasonal cohort: the new customers you paid to acquire in November.
The cost of getting this wrong runs both directions. Score too aggressively and you insult gift buyers with warnings and withheld refunds, which is how you lose a customer in their first month. Score too loosely and the actual abusers, who also know the holidays are chaotic, hide their patterns in the noise. January is the highest-volume fraud window of the year precisely because everyone expects the numbers to look strange.
Calibrating scores for the season
The cleanest approach is seasonal baselines: compute expected return behavior separately for the holiday window, roughly mid-November through January, and score customers against the seasonal expectation instead of the annual one. A customer returning 40 percent of a December order is normal. The same rate in March is a signal. Separate the baselines and both statements stay true.
Second, weight gift indicators. Orders shipped to a different address than the billing address, gift messages, gift wrapping, and purchases made by someone other than the account holder all predict legitimate gift returns. These should dampen the score, not raise it. A gift recipient exchanging a size is the single most legitimate return in retail, and your model should know that.
Third, watch what does not change with the season. True abuse patterns keep their shape through the holidays: the same account returning worn items, the serial returner whose rate stays high into February, the wardrobing pattern repeating across event weekends. Seasonal calibration should make these stand out more clearly, not less, because the legitimate noise around them gets filtered out.
What to do with the January wave operationally
Scores are only useful if operations can act on them. Before the season, set explicit holiday thresholds and communicate them to the customer service team, so agents know which flags are real and which are seasonal noise. Prepare for the January surge in review queues; the absolute number of flagged accounts will rise even with good calibration, because volume rises. And run a post-season review in February: which flags were correct, which were gift buyers, and what does that teach the model for next year. The brands with the best holiday scoring are the ones that learn from every January instead of dreading it.
One last calibration that pays off: keep a separate watchlist for accounts created during the holiday window that show abuse-shaped patterns by February. The holidays bring a wave of new accounts, and a small fraction are created specifically to exploit seasonal chaos. They look like enthusiastic new customers in December and like serial returners by Valentine's Day. Catching that transition early is what separates a scoring system from a scoreboard.