Profiling Television Watching Behavior Using Bayesian Hierarchical Joint Models for Time-to-Event and Count Data

Rafael Moral, Zhi Chen, Shuai Zhang, Sally I McClean, Gabriel Palma, Brahim Allan, Ian Kegal

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Abstract

Customer churn prediction is a valuable task in many industries. In telecommunications it presents great challenges, given the high dimensionality of the data, and how difficult it is to identify underlying frustration signatures, which may represent an important driver regarding future churn behaviour.Here, we propose a novel Bayesian hierarchical joint model that is able to characterise customer profiles based on how many events take place within different television watching journeys, and how long it takes between events. The model drastically reduces the dimensionality of the data from thousands of observations per customer to 11 customer-level parameter estimates and random effects. We test our methodology using data from 40 BT customers (20 active and 20 who eventually cancelled their subscription) whose TV watching behaviours were recorded from October to December 2019, totalling approximately half a million observations. Employing different machine learning techniques using the parameter estimates and random effects from the Bayesian hierarchical model as features yielded up to 92% accuracy predicting churn, associated with 100% true positive rates and false positive rates as low as 14% on a validation set. Our
proposed methodology represents an efficient way of reducing the dimensionality of the data, while at the same time maintaining high descriptive and predictive capabilities. We provide code to implement the Bayesian model at https://github.com/rafamoral/profiling_tv_watching_behaviour
Original languageEnglish
Article number10.1109/ACCESS.2022.3215682
Pages (from-to)113018 - 113027
Number of pages10
JournalIEEE Access
Volume10
Early online date19 Oct 2022
DOIs
Publication statusPublished online - 19 Oct 2022

Keywords

  • Bayesian modelling,
  • big data
  • churn prediction
  • clustering
  • dimensionality reduction,
  • frustration signatures
  • machine learning

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