Predicting Stages in Omnichannel Path to Purchase: A Deep Learning Model
研究利用消费者线上线下行为数据，通过深度学习模型预测其未来购买路径，发现全渠道数据能显著提升预测准确性，并对不同类型在线企业均有经济价值。
The proliferation of omnichannel practices and emerging technologies opens up new opportunities for companies to collect voluminous data across multiple channels. This study examines whether leveraging omnichannel data can lead to, statistically and economically, significantly better predictions on consumers’ online path-to-purchase journeys, given the intrinsic fluidity in and heterogeneity brought forth by digital transformation of traditional marketing. Using an omnichannel data set that captures consumers’ online behavior in terms of their website browsing trajectories and their offline behavior in terms of physical location trajectories, we predict consumers’ future path-to-purchase journeys based on their historical omnichannel behaviors. Using a state-of-the-art deep-learning algorithm, we find that using omnichannel data can significantly improve our model’s predictive power. This enhanced predictive power benefits various heterogeneous online firms, regardless of their size, offline presence, mobile app availability, or whether they are selling single- or multi-category products. Using an illustrative example of targeted marketing, we further quantify the economic value of the improved predictive power and the value of data.