Hierarchical Bayesian Modeling for Clustering Sparse Sequences in the Context of User Profiling in Customer Loyalty Program

An algorithm is proposed in this paper to identify the latent groups among users based on similar behavior pattern and to generate group-profile for every latent group discovered. The behavior history of every user is represented by a sequence of zeros and positive numbers. Our goal is to create a hierarchical Bayesian model of the sequence data and soft-assign the sequences to a number of clusters. Profiles representative of each cluster are generated by the data model. This model is a mixture model where every user’s sequence is effectively a mixture of the distributions for each cluster. However, the novelty of this algorithm is that it is not required to assume a Gaussian mixture as is the case with EM, instead it could be any distribution that fits the data. In this paper Poisson distribution is used to model individual users sequence. The data used in this paper are real industrial data, collected from a collaborating marketing company, which promotes businesses through loyalty programs. Loyalty program helps businesses engage users by sending weekly rewards, coupons etc and the number of rewards redeemed per week for a period of two years forms the users sequence, where every entry in the sequence represents number of redemptions every week. The similarity in the real-life motivators such as schedules, locations, tastes, and expectations gives rise to similar pattern in redemption of these rewards. Based on this inherent similarity the latent groups evolve in the data.