Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 12, 2022 erstellt
Beschreibung:
New products are highly valued by manufacturers and retailers due to their vital role in revenue generation. Product life cycle curves often vary by their shapes and are complicated by promotional activities that induce spiky and irregular behaviors. We collaborate with JD.com to develop a flexible product life cycle curve forecasting framework based on Bayesian functional regression that accounts for useful covariate information, including product attributes and promotion. The functional model treats product life cycle curves as target variables and includes both scalar and functional predictors, capturing time-varying promotional activities. Harnessing the power of basis function transformation, the developed model can effectively characterize the local features and temporal evolution of sales curves. Our Bayesian framework can generate initial curve forecasts before the product launch and update the forecasts dynamically as new sales data is collected. We validate the superior performance of our framework through extensive numerical experiments using three real-world data sets. Compared to the forecasting method of JD.com, our framework can reduce the forecasting error by more than 17%. The improvements are consistently observed across other data sets. Furthermore, the estimated promotion effect function provides useful insights into how promotional activities interact with sales curves