Churn Prediction with Sequential Data Using Long Short Term Memory

Ahmet Tuğrul Bayrak · Asmin Alev Aktas · Orkun Susuz · Okan Tunalı

11 citations0/0 files storedInternational Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Companies aim to keep their current clients in today's dynamic market climate. Churn prediction is essential in order to accomplish that. It is not a straight forward task to predict churning consumers. In the fast-food industry, it is even more difficult since, when a consumer stops having the industry's products, there can be different reasons. The study is proposed to resolve this situation. The customer data structure is sequentially established with the data related to the customers. With the sequential data, a long short term memory model is designed to estimate the customers' churn stages and compared with the conventional methods of classification. The proposed model offers promising results and stands out among related studies with its predictions.

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References

2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2009Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn 2019Comparing to Techniques Used in Customer Churn Analysis 2012Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences 2019Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis

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2021Personalized Customer Churn Analysis with Long Short-Term Memory

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