Predicting customer churn from multi-product data

Abstract

Predicting churn from a multi-product data using sequential modeling includes collecting, by a journey module, data from a plurality of platforms to build a journey defined by one or more events, and creating, by a sequential deep learning (DL) model, relationship data identifying between events in a chronological order, capturing temporal dependencies present in the data. This also includes correlating, by a churn prediction module, sequences of events to a churn event, and identifying when the churn event is going to occur based on pattern learnt to differentiate between 'churn' and “non-churn” journeys.