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More Orders, More Fraud. Prepare for Both.
The revenue you book over the weekend isn’t the revenue you keep once January’s returns land. Swapping refunds for exchanges to keep the sale, timing your team for the exact days the return wave hits, and routing only high-risk returns to manual review so good customers stay fast.
The “Record BFCM” Reality Check
A record BFCM looks different once January’s returns are in. The revenue you booked over the weekend isn’t the revenue you keep.
Loop is the operations platform built for retention. It powers returns for more than 5,000 Shopify merchants and has processed 23.4M+ returns. That data helps provide a view of a real shopper and fraud behavior most brands never get. Its core argument is simple: the vast majority of returners are well-meaning, and the return is the moment you either hand cash back or keep the revenue. The single biggest lever on a returns-adjusted P&L is whether that return resolves as a refund or an exchange.
Return rate is a planning assumption. Loop publishes retained-revenue benchmarks, not 30/60/90-day return curves. Refund-first treats returned revenue as lost; exchange-first retains up to 72% of it (best case), before the $22 average upsell per Shop Now exchange.
The gap between the two columns is the whole chapter: same returns, very different net revenue. Attributed to Loop (Data Partner).
The Wave Has a Date: Boxing Day
Returns don’t trickle in evenly across the month. They cluster on a specific day, and that day keeps setting new records. Boxing Day is the single biggest return day of the year, and December 26, 2025 set Loop’s all-time record. Smart brands set their defenses for this date before the season starts, not the morning it arrives.
Boxing Day 2025. The wave has a known date and a known shape, which makes it plannable. Attributed to Loop (Data Partner).
Exchange-First Is the Lever
Refund-first hands the money back; exchange-first keeps it, and often grows it, since shoppers tend to add more when they exchange. It’s why exchange-first flows are becoming the default: 73.6% of Loop merchants now offer exchanges and 49.2% offer Shop Now, with 51.7% of Shop Now merchants adding a Bonus Credit incentive (avg. $11.28) to nudge shoppers toward exchange over refund.
| Refund-first | Exchange-first | |
|---|---|---|
| The return | Money leaves the business | Up to 72% of the revenue is retained |
| The shopper | Gets cash back, then gone | Often adds more, $22 upsell per Shop Now exchange |
| The return becomes | A pure cost | A repeat-purchase moment |
| On the P&L | Wins the sale, loses the margin | Converts the wave into retained revenue |
The same return, two very different outcomes. Attributed to Loop (Data Partner).
An Increase in Orders = An Increase in Fraud
With an increase in orders and fraud, brands can expect an uptick in manual workload without the proper workflows in place to help. Reducing that manual workload means more time interacting with good, high-lifetime-value customers.
Return volume rises around the holidays: December vs the Aug–Sep baseline was +64% in 2024 and +71% in 2025. Fraud flag counts rise with it (+28% Oct→Dec 2024, +49% Nov→Dec 2025). Because CX teams are slammed at BFCM, flagged returns pile up unreviewed and become a January backlog.
| Metric | With active workflow | Without a workflow |
|---|---|---|
| High-risk flags reviewed | 31.6% | 10.9% |
| Median days to action, through BFCM | 2 days | 8 → 12 days |
| January activity that's backlog catch-up | 22% | 51% |
| Average lag on that backlog | 13 days | 35 days |
The fix isn’t harsher blanket rules. Those punish good customers along with the abusers. It’s segmentation: route only Fraud-Model-flagged returns to manual review and leave everyone else’s experience fast.
“Your best customer shouldn’t have the same experience as someone who has abused your policy five times.”Lex Perlmutter · Head of Intelligence, Loop
What Brands Get Wrong
The same handful of mistakes turn a manageable January into a margin leak.
| The mistake | The fix | |
|---|---|---|
| Waiting too long | Building defenses only once the season has started. | Set the workflows up once, ahead of the season, including the holiday return-window extension. |
| Unfunded free returns | Refund-first, free-returns policies with no friction or funding. “Nothing at stake” invites bracketing and wardrobing. | Fund the safety net (consumer-paid / Checkout+) and run exchange-first. Free returns win the sale but lose your margin. |
| Returns as cost | Not offering exchanges or Shop Now; treating every return as a pure loss. | Exchange-first retains up to 72% of the returned revenue and turns the wave into repeat purchases. |
| Blanket fraud rules | One-size-fits-all rules that punish good customers alongside abusers. | Segmented, data-driven controls that send only flagged returns to review. |
| Under-staffed | No plan for the January wave; no self-service to prevent avoidable returns. | Self-service cuts avoidable returns. Order Editing drove an 80% decline in early-cycle returns; Origin USA saw a 61% drop in refund-related tickets. |
Five avoidable mistakes, and what to do instead. Attributed to Loop (Data Partner).
Before the Season: Set the Defenses
Everything above is configured once, in advance. A short pre-season checklist covers most of the risk.
This chapter’s returns and risk frameworks were supplied by Loop.