Data Aggregation and Demand Prediction

Cohen, Maxime C.; Zhang, Renyu; Jiao, Kevin

doi:10.2139/ssrn.3411653

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…Other valuable covariate information, such as leading‐indicator products, distribution channel, advance purchases, social media, and Google trend, has also been exploited in sales forecasting (Aytac & Wu, 2013; Boone et al., 2018; Cui et al., 2018; Moe & Fader, 2002; Neelamegham & Chintagunta, 1999). In addition, clustering techniques and machine learning models have been leveraged to boost forecasting performance of new product sales (Baardman et al., 2017; Cohen et al., 2022b; Ferreira et al., 2016); the readers may find a comprehensive and recent review of forecasting techniques in Cohen et al. (2022a).…”

Section: Introductionmentioning

confidence: 99%

New product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach

Lei

Geng

et al. 2023

Production and Operations Management

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New products are highly valued by manufacturers and retailers due to their vital role in revenue generation. Product life cycle (PLC) 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 PLC curve forecasting framework based on Bayesian functional regression that accounts for useful covariate information, including product attributes and promotion. The functional model treats PLC 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 are collected. We validate the superior performance of our method through extensive numerical experiments using three real‐world data sets. Our forecasting framework reduces the forecasting error by 5.35%–30.76% over JD.com's current model and outperforms alternative models significantly. Furthermore, the estimated promotion effect function provides useful insights into how promotional activities interact with sales curves.

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