A multi-stage hidden Markov model of customer repurchase motivation in online shopping

摘要

Product promotions and liberal return policies are two effective signals that can increase customers' repurchase behavior when online shopping, and how these signals work at different stages of market growth for various customer groups is an important topic for research and applications. Thus, to help online merchants make more effective decisions regarding the use of such signals, this paper proposes a multi-stage hidden Markov model (MS-HMM) to explore the motivational process behind customer repurchase behavior through the lens of the Signaling Theory. The customer-merchant relationship (CMR) is represented as the latent state in the hidden Markov model and is coupled with stage-heterogeneity in terms of state transition probabilities and state-dependent choice probabilities. Moreover, extensive experiments with real-world data are conducted to validate the effectiveness of the MS-HMM.