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As retailers around the world increase efforts in developing targetedmarketing campaigns for different audiences, predicting accurately whichcustomers are most likely to churn ahead of time is crucial for marketing teamsin order to increase business profits. This work presents a deep survivalframework to predict which customers are at risk of stopping to purchase withretail companies in non-contractual settings. By leveraging the survival modelparameters to be learnt by recurrent neural networks, we are able to obtainindividual level survival models for purchasing behaviour based only onindividual customer behaviour and avoid time-consuming feature engineeringprocesses usually done when training machine learning models.

Modelling customer churn for the retail industry in a deep learning based sequential framework