Accurately Forecasting Returns to Improve Customer Experience in E-commerce

Abstract

E-commerce returns management presents both significant challenges and strategic opportunities for online retailers. While returns have traditionally been viewed as an unavoidable cost center, advances in forecasting methodologies are transforming this perspective. Returns forecasting differs fundamentally from traditional demand forecasting due to its dual dependency on sales patterns and return behaviors; variable time lags across product categories, and multidimensional influencing factors. Building effective returns forecasting models requires comprehensive data integration across transaction records, returns information, product metadata, customer behavior patterns, and policy changes. Various modeling approaches—from time series with lagged variables to gradient-boosted decision trees and hybrid models—offer different advantages depending on specific forecasting needs. Implementation demands robust technical infrastructure, thoughtful feature engineering, and targeted deployment strategies. When properly leveraged, returns forecasts enable dynamic policy optimization and personalized return experiences that enhance customer satisfaction and operational efficiency. Success measurement requires balanced consideration of operational metrics like forecast accuracy and customer experience indicators such as post-return purchase rates, creating a comprehensive framework for continuous improvement.