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New Churn Prediction Strategies in the Telecom Industry
Abstract
In the telecom industry, high installation and marketing costs make it six to 10 times more expensive to acquire a new customer than it is to retain an existing one. Prediction and prevention of customer churn is therefore a key priority for industrial research. While all the motives of customer decision to churn are highly uncertain there is a lot of related temporal data generated as a result of customer interaction with the service provider. The major problem with this data is its time discontinuity resulting from the transactional character of events they describe. Moreover, such irregular temporal data sequences are typically a chaotic mixture of different data types, which further hinders its exploitation for any predictive task. Existing churn prediction methods like decision trees typically classify customers into churners and non-churners based on the static data collected in a snapshot of time while completely ignoring the timing of churn and hence the circumstances of this event. In this work, we propose new churn prediction strategies that are suitable for application at different levels of the information content available in customers’ data. Gradually enriching the data information content from the prior churn rate and lifetime expectancy then typical static events data up to decay-weighted data sequences, we propose a set of new churn prediction tools based on: customer lifetime modelling, hidden markov model (HMM) of customer events, and the most powerful k nearest sequence (kNS) algorithm that deliver robust churn predictions at different levels of data availability. Focussing further on kNS we demonstrate how the sequential techprocessing of appropriately pre-processed data streams lead to better performance of customer churn prediction. Given histories of other customers and the current customer data, the presented kNS uses an original combination of sequential nearest neighbour algorithm and original sequence aggregation technique to predict the whole remaining customer data sequence path up to the churn event. On the course of experimental trials, it is demonstrated that the new kNS model better exploits time-ordered customer data sequences and surpasses existing churn prediction methods in terms of performance and capabilities offered.
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