Holiday returns create a second operational peak that tests IT systems, workflows, and cross-team coordination. With 17% of all holiday sales expected to come back in 2025, teams need a structured plan to manage load, prevent fraud, and keep return workflows stable from December through February.
Holiday returns now rival major sales events in scale and operational impact. January is effectively a second peak season, driven not by purchases but by the wave of returns that follows the holidays. Many gifts aren’t opened until late December or early January, so issues like sizing mismatches, duplicate gifts and unwanted items surface at the same time.
The National Retail Federation projects that 17% of all holiday sales will be returned in 2025, meaning retailers must treat December through February as a defined “returns season” with its own demand curve and system expectations. For IT and operations leaders, this creates sustained pressure across the entire stack: return portals, identity checks, OMS and WMS integrations, refund engines, and carrier-tracking workflows.
This article shows you how to map those dependencies, tune holiday return policies, and prepare IT and operations so the January surge stays controlled and predictable.
Key takeaways:
January is effectively a second peak season, this time driven by returns. Hence, it requires its own operational plan. For consumers, holidays may feel like the finish line, but for most retailers, a second surge begins after December 25. Since many holiday purchases aren’t opened until late December or early January, once they are, sizing issues, product mismatches, and exchanges surface all at once.
This creates a concentrated wave of inbound activity: return requests, portal traffic, refund processing, eligibility checks, and system updates. With the National Retail Federation forecasting 17% of all holiday sales are going to be returned in 2025, instead of treating returns as a reactive task, IT and operations leaders benefit from treating December through February as a defined “returns season.”
Holiday returns create prolonged traffic across return portals, order-lookup APIs, refund processors, identity verification endpoints, and carrier-tracking integrations. Unlike short promotional spikes, this surge lasts for weeks, requiring the same level of performance testing and monitoring as the Cyber Week sales.
A return event touches more systems than a purchase. OMS, WMS, POS, inventory, refund engines, and customer-notification tools all require synchronized updates. Any mismatch between these systems during January’s peak leads to inaccurate stock levels, delayed resale availability, and reconciliation backlogs.
Holiday windows allow more time and opportunity for fraud patterns such as empty-box returns, counterfeit swaps, and receipt-less claims. IT systems must reinforce identity checks, return-history limits, and SKU-based eligibility rules to protect revenue during this high-risk period.
Returns activate more internal teams than purchases: IT maintains system performance and logic, operations handle inbound processing, finance manages refunds and customer support (CX) teams handle the spike in “Where is my return?” inquiries. Treating January as a coordinated peak season streamlines workloads and minimizes delays for customers.
With the reasons stated, teams should treat the returns season with its own demand curve, performance thresholds, and staffing plan. Preparing systems, workflows, and cross-team coordination ahead of this surge prevents January from becoming an avoidable bottleneck.
A store’s standard return policy is designed for predictable, everyday transactions: fixed windows, simple eligibility checks, stable item condition rules, and linear processing paths. Holiday return policies, on the other hand, operate differently. They have extended return windows, introduce date-based exceptions, and push more customers toward exchanges or store credit instead of refunds.
Because of this, a holiday return policy doesn’t just adjust customer-facing rules, it also impacts one’s operations. Each requirement affects point-of-sale (POS) logic, order management system (OMS) workflow, identity validation, refund routing, and warehouse operations.
Holiday return policies typically include four core components, each with direct implications for IT and operations. I discuss them in detail below.
Extended return windows matter because 19.3% of online sales are expected to be returned in 2025, according to the NRF, and much of that volume lands after holiday gifts are opened in late December. This creates customer expectations for flexible timing, while forcing retail systems to distinguish between holiday-eligible purchases and standard purchases using date-based logic.
The customer sees a straightforward promise (say, “You have until January 31st”), but IT teams must maintain branching rules across POS, return portals, refund engines, and fraud checks so the correct window applies every time.
Example: A smartwatch purchased on November 8th qualifies for an extended holiday window through January 31st, while the same model purchased on January 2nd falls under a standard 30-day policy. The return portal must recognize these cases automatically to avoid manual overrides and customer friction.
Return expectations rise with convenience — 82% of consumers say free returns influence where they shop — meaning customers now expect multiple no-cost options such as label-free QR drop-offs, lockers, and in-store returns.
On the backend, each option requires a distinct workflow: mail-in returns depend on carrier scan events, in-store returns require POS validation, and marketplace returns must follow seller-of-record rules. What feels like a simple choice to the customer demands accurate routing, correct integrations and consistent event updates behind the scenes.
Example: A customer selects a UPS Store QR code drop-off. The system must generate the QR code, create a Return Merchandise Authorization (RMA) event, notify the OMS, and assign the correct inbound warehouse; otherwise, the return stalls and the refund is delayed.
Speed drives satisfaction. According to the NRF, 76% of shoppers prefer return options that offer instant refunds or exchanges, especially after gift-heavy seasons when sizing or color mismatches surface.
Customers experience this as convenience (and demand this feature), but IT and operations must coordinate inventory checks, fraud screening, credit-balance updates, and refund routing across multiple systems. Exchange-first flows are only successful if stock availability, pricing adjustments, and refund exceptions are synchronized in real time.
Example: A customer starts an exchange for a different shoe size. The OMS checks inventory, the payment service reconciles any price difference, and the WMS queues a replacement shipment, all without waiting for the original item to arrive.
Channel consistency matters because a majority of shoppers (71%, per the NRF survey) say a poor return experience reduces their likelihood of buying again, regardless of where they purchased the item.
From a customer standpoint, a return is a return, but backend systems must handle different refund service level agreements (SLAs), identity requirements, and routing paths for store purchases, ecommerce orders, marketplace transactions, and partner drop-offs. The challenge is ensuring the experience appears unified while technical workflows remain channel-specific.
Example: A shopper returns an item purchased through a marketplace storefront. The system must identify the order as marketplace-sourced, follow marketplace refund timing, and route the item to the correct warehouse. Otherwise, the brand risks penalties and slows the customer’s refund.
Return events move through more internal systems than a standard purchase. A single return needs to go through POS validations, ecommerce lookups, RMA creation, warehouse routing, fraud scoring, refund decisions, and analytics updates. Understanding how these systems interact prevents delays, inconsistent data, and refund errors during January’s peak.
The sections below outline the components of an effective returns tech stack and how each contributes to a stable, predictable workflow.
Step 1: Connect POS, ecommerce, OMS, and WMS workflows. These systems must share order IDs, item condition, return reasons, timestamps, routing codes, and refund paths. Even minor mismatches (for example, a missing variant ID or incorrect timestamp) can slow processing or create inventory inaccuracies during the January rush.
Step 2: Configure return portals, self-serve flows, and RMA engines. Return portals should automatically verify eligibility, surface the correct return options, and send standardized RMA events to OMS and WMS. The RMA engine must record item disposition (restock, refurbish, recycle, or liquidate) so inventory and finance stay aligned.
Step 3: Strengthen fraud detection and abuse-prevention rules. Identity checks, SKU-level rules, return-velocity limits, and behavioral scoring are essential. With 9% of all returns estimated to be fraudulent, systems must screen return events as rigorously as purchase events to protect revenue from January abuse spikes.
Step 4: Build analytics pipelines and warehouse data feeds. Return reasons, refund times, fraud flags, item conditions and disposition data should feed your BI or data warehouse for real-time dashboards. Clear, structured data supports more accurate forecasting, shrink reduction, and future policy adjustments.
Holiday returns place measurable pressure on reverse logistics, carrier coordination, fraud checks, and system availability. January becomes a sustained period of inbound activity, and system load must be treated as peak, not routine, traffic. IT’s role is to anticipate these bottlenecks and ensure capacity, accuracy, and visibility across the entire return flow.
Holiday return fraud increases because extended windows and gift-driven purchases create more opportunities for misuse. The same NRF report says 71% of retailers report empty-box or “box of rocks” scams, so abuse prevention must be built into workflows, not bolted on later.
Effective return-flow design balances customer convenience with the controls needed to detect substitution, counterfeit swaps, and return-without-receipt attempts.
Holiday returns require coordination across IT, fraud, operations, CX, logistics, and finance. Treating December–February as a defined season with its own preparation cadence helps teams stay ahead of the surge. This checklist breaks the season into time-based milestones to ensure systems, staff, and workflows are ready.
Since 49% of retailers plan to rely more on third-party logistics partners during the holidays. 3PL and partner dependencies should be tested early to prevent January delays. At this stage, teams should:
Example: A load test reveals that label-generation slows under peak volume. IT can address the bottleneck before the RMA queue builds up in January.
Nearly half (43%) of retailers plan to hire seasonal staff specifically to manage returns. Front-line staff need updated scripts and policy logic to handle volume. Teams should:
Example: A refund test shows that certain Buy Now Pay Later refunds route incorrectly. Fixing this early prevents hundreds of manual cases during peak.
Real-time monitoring ensures bottlenecks don’t snowball. Retail Insight Network reports that more than a third (37%) of retailers plan to extend return windows during this period, increasing system load.
Teams must:
Example: A sudden rise in RMA creation errors surfaces at 10 a.m. Monitoring catches the spike, allowing IT to resolve the issue before it impacts thousands of customers.
This stage informs improvements for the next season and identifies areas to automate. Teams should:
Example: Post-season analysis shows the highest defect claims came from a specific supplier. Operations and procurement can act on this before the next holiday cycle.
Returns put measurable pressure on systems, logistics, and customer satisfaction. To keep the holiday return season under control, IT and business leaders need clear KPIs, defined SLO,s and an agreed view of what “good” looks like during December to February.
Retail Insight Network says that 40% cite higher operational costs for processing returns, 40% point to increased carrier shipping costs, and 64% say updating their returns process in the next six months is a priority. So monitoring the metrics mentioned will help retail businesses make better-informed decisions.
The tables below organize the most useful metrics into three groups:
| Median time to refund | Time from item check-in (or scan) to refund completion | Directly affects customer trust and repeat purchase likelihood during a sensitive period |
| Median time to resale-ready | Time from return scan to item being available for sale again | Impacts inventory accuracy, stock health, and the speed of revenue recovery |
| Exchange rate vs refund rate | Proportion of returns that end as exchanges vs cash refunds | Indicates how well your flows protect revenue and keep customers in the ecosystem |
| % of exceptions needing manual review | Share of returns that cannot follow automated flows | Highlights process friction, system gaps and areas that may need better rules or automation |
| Fraud false-positive rate | Legitimate returns incorrectly flagged as fraud | Shows whether fraud rules are too aggressive and harming CX or agent productivity |
| Return portal uptime | 99.9% uptime during Jan 1-31 | Outages or slowdowns that block self-service and push volume to support |
| API latency for return lookups | p95 < 500 ms | Spikes that slow down portals, POS lookups or agent tools |
| Error rate for RMA creation | < 0.5% failed RMA attempts | Patterns that suggest integration issues, bad payloads or logic problems |
| Processing time for refund events | 95% of refunds processed within X minutes/hours | Delays that cause refund backlogs and increase “Where is my refund?” contacts |
| Return reason codes | Understand why items come back | Update product pages, sizing, packaging, and QA priorities |
| SKU / category-level return rates | See which products drive the most returns | Adjust assortments, negotiate with suppliers, or add extra guidance |
| Channel-based return patterns | Compare store, ecommerce, and marketplace returns | Tune policies and routing per channel; align with 3PLs and partners |
| Exception and dispute patterns | Identify where policies or systems are unclear | Simplify workflows, clarify rules, and refine training for next season |
| Fraud flags and confirmed fraud | Separate abuse from normal returns | Refine risk rules, thresholds, and manual-review triggers |
You can reuse your existing portal if it supports date-based eligibility rules, multiple return paths, and holiday-specific messaging. If it cannot handle extended windows, marketplace logic, or channel-specific flows, it is usually better to create a separate “holiday mode” configuration (even if it uses the same underlying app).
You should retain return-event logs for at least the full holiday return window plus an additional 90 days. That gives you enough time to handle disputes, chargebacks, fraud investigations, and post-season analysis without hitting storage or access issues.
Fraud checks work best in a centralized risk engine that all channels can call, rather than scattered rules across POS, ecommerce, and OMS. That way, in-store and online returns share the same risk signals, history and thresholds, and you avoid conflicting decisions across systems.
Most organizations aim for refunds to be completed within three to five business days from check-in for standard returns. Higher-risk categories or manual reviews may take longer, but those exceptions should be clearly documented and monitored so they don’t become the default.
Marketplace orders must follow the marketplace’s refund timelines, communication rules and dispute process, even if your direct-channel policy is more flexible. Your systems should tag these orders at creation time so return flows, routing, and notifications apply the correct rules automatically.
If returns start backing up, focus on: median time to refund, error rate for RMA creation, return-portal uptime, and the share of returns going to manual review. Spikes in any of these usually indicate a bottleneck in a specific integration, queue or rule set that you can fix before it spreads.
Agatha Aviso is a seasoned expert in retail, eCommerce, and order fulfillment, with a specialization in payments, POS systems, and eCommerce software. She has collaborated with startups and service-based entrepreneurs on content strategy, offering digital marketing expertise and guiding small business owners in launching their online storefronts. Beyond consulting, Agatha applies her knowledge firsthand—building her own website as well as ecommerce sites for the platforms she reviews.
