Returns Forecasting for Peak Season Readiness

By ReverseLogix

Accurate forecasting is the first step in managing holiday returns. And the retailers that expect rather than react can prevent chaos when the holiday is over. Here are some tactical steps to ensure effective returns forecasting:

Returns forecasting begins with the compilation of accurate historical data. And past holiday seasons can provide a baseline for seasonal returns. For example, apparel brands often see25–30% of holiday purchases come back, while electronics average closer to 15%. Knowing these will guide you in making realistic estimates for future returns.

Segmenting returns improves forecasting accuracy. Categories like fashion, footwear, and electronics see higher return rates, and e-commerce returns often double in-store ones due to behaviors like bracketing and gift-buying. Breaking down returns by category and channel helps teams forecast more accurately and allocate resources efficiently.

Forecasting future returns requires awareness of consumer behavior. Given current trends — such as shoppers ordering multiple sizes or styles with the intention of returning some, and gift recipients frequently exchanging items that don’t suit their needs — many returns should come as no surprise.

Changes to the returns policy, such as extended holiday return windows, can alter the timing of reverse flows. And external factors, such as shipping delays or economic pressure, also impact return rates. By anticipating these factors, you can adjust the forecasts before bottlenecks emerge.

Forecasting returns improves by combining historical and current data. Time series and regression models help project return rates, while machine learning enhances accuracy by factoring in variables like promotions, shipping delays, and regional trends. This enables better planning for post-holiday return spikes.

Scenario planning exposes risks, and you can, for example, simulate the effect of offering free holiday returns or extending policies until February. While these policies may lift sales, they also have the potential to increase return volumes. A retailer that tests scenarios can prepare storage, staff, and refunds in advance. Stress testing ensures the system holds up when the unexpected happens such as returned items doubling or tripling in volume.

Returns forecasts are only useful when shared across the reverse supply chain. For example:

• Operations needs weekly return estimates by item type to plan effectively.
• Fulfillment must prepare storage space to avoid chaos.
• Customer service needs refund forecasts to staff properly and provide accurate info.
  
  

Returned merchandise requires space, and failing to plan for that will often result in congestion and delays. This is why capacity planning based on forecasted return rates is so essential in the returns management process.

For example, fast-moving returned items should have a dedicated space for inspection and restocking. Electronics may require separate zones for refurbishment, and non-saleable goods should have designated disposal areas to prevent contamination of saleable stock.

Labor shortages are common during peak season. Accurate return forecasts help retailers plan seasonal staffing for returns management and reverse logistics. Forecasts also guide training needs, including fraud detection, as fraudulent returns often spike post-holidays. Staff should be trained to process refunds quickly to maintain customer trust, and labor schedules should align with peak return periods—typically the first two weeks after Christmas.

Returns forecasting is the foundation, but acting on the forecast is what prevents breakdowns in post-holiday returns management.