Explainability of Leverage Points Exploration for Customer Churn Prediction

Danny Y. C. Wang · Lars Arne Jordanger · Jerry Chun‐Wei Lin

7 citations0/0 files storedIEEE International Conference on Big Data (BigData)

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Customer churn and segmentation Customer Service Quality and Loyalty Explainable Artificial Intelligence (XAI)

Abstract

Customer churn is a most crucial challenge faced by various industries, especially for the telecommunications sector since it causes over 5 - 6 times cost to keep customers. Thus, customer churn prediction has experienced substantial expansion. While a lot of prediction models are becoming increasingly accurate, the capability to interpret these models and perform causal analysis has become a new challenge. Identifying the key reasons or patterns that truly influence customer churn is vital for enabling industries to make significant improvements. This study then introduced a comprehensive framework. That integrates causal graphs and explainable models such as SHAP and Shapley flow, and concepts from system dynamics to explore customer churn patterns and leverage points. Experimental results indicates that the designed model is more explainable and interpretable to explore the customer behaviors for customer churn prediction.

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References

2019Customer churn prediction in telecom using machine learning in big data platform 2005Applying data mining to telecom churn management 2021Integrated Churn Prediction and Customer Segmentation Framework for Telco Business 2023An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes 2017A Method for Simplification of Complex Group Causal Loop Diagrams Based on Endogenisation, Encapsulation and Order-Oriented Reduction 2018Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning 2017A Novel Approach for Churn Prediction Using Deep Learning 2019Locating Potential Leverage Points In A Systems Thinking Causal Loop Diagram Toward Policy Intervention

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