Personalized Customer Churn Analysis with Long Short-Term Memory

In today's competitive business environment, companies aim to retain their existing customers. To achieve that, churn prediction is crucial. Predicting churning customers is not a simple task. It is even more challenging in the fast-food industry since there can be various reasons when a customer stops ordering. To overcome that situation, this study is proposed. The customer data structure is formed sequentially with customers' individual churn periods in different windowing approaches. A long short-term memory model is built with the sequential data to predict the customers' churn stages, and it is compared with the other common classification methods. The proposed model presents promising results and stands out with its personalized prediction among similar studies.