  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.
