Enhancing Demand Forecasting in Retail: A Comprehensive Analysis of Sales Promotional Effects on the Entire Demand Life Cycle

Sales promotions pose challenges to retail operations by causing sudden fluctuations in demand, not only during the promotional period but also across the entire sales promotional life cycle. Previous research has predominantly focused on promotional and nonpromotional periods, often overlooking the postpromotional phase, where demand decreases due to consumer stockpiling during promotions. To address this research gap, we investigate both traditional statistical forecasting methods and contemporary approaches, such as global models, implemented using gradient boosting and deep learning techniques. We assess their performance throughout the entire demand life cycle. We employ the base‐lift approach as our benchmark model, commonly used in the retail sector. Our study results confirm that machine learning methods effectively manage demand volatility induced by retail promotions while enhancing forecast accuracy across the demand life cycle. The base‐lift model performs comparably to alternative machine learning methods, albeit with the additional effort required for data cleansing. Our proposed forecasting framework possesses the capability to automate the retail forecasting process in the presence of sales promotions, facilitating efficient retail planning. Thus, this research introduces a novel demand forecasting framework that considers the complete demand life cycle for generating forecasts, and we rigorously evaluate it using real‐world data.