KNNs and Sequence Alignment for Churn Prediction

Abstract

Large companies interact with their customers to provide a variety of services to them. Customer service is one of the key differentiators for companies. The ability to predict if a customer will leave in order to intervene at the right time can be essential for pre-empting problems and providing high level of customer service. The problem becomes more complex as customer behaviour data is sequential and can be very diverse. We are presenting an efficient sequential forecasting methodology that can cope with the diversity of the customer behaviour data. Our approach uses a combination of KNN (K nearest neighbour) and sequence alignment. Temporal categorical features of the extracted data is exploited to predict churn by using sequence alignment technique. We address the diversity aspect of the data by considering subsets of similar sequences based on KNNs. Via empirical experiments, it can be demonstrated that our model offers better results when compared with original KNNs which implies that it outperforms hidden Markov models (HMMs) because original KNNs and HMMs applied to the same data set are equivalent in terms of performance as reported in another paper.