Smart evaluation for deep learning model: churn prediction as a product case study

Esam Mohamed Elgohary · Mohamed Galal · Ahmed Mosa · Ghada Atef Elshabrawy

9 citations0/0 files storedBulletin of Electrical Engineering and Informatics

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

Abstract

Customer churn prediction recently is one of the vital issues that confronts diverse business industries to sustain the customers base and profits. On the other hand, data scientists employ gigantic customer data to automate the data modelling process to offer these models as a generally portable service. This research has two main contributions: deep learning customer churn prediction model and smart evaluation prediction model service. So, this service harnesses any customer data to automate building, evaluation, and deployment of the churn prediction model. The research consists of three main parts. Firstly, it illustrates the dataset labelling which annotates customers data into churn or non-churn. Secondly, the deep learning churn prediction framework using convolutional neural network (CNN) algorithm. Finally, a case study is presented to show how churn prediction service is automatically trained and generated based on real customer data, where CNN parameters are adapted to achieve the most reliable performance in line with customers' behavior. The applied case study achieves accuracy 0.77, area under the curve (AUC) 0.84 and f1 score 0.83.

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References

2016Intelligent data analysis approaches to churn as a business problem: a survey 2015A Multi-Layer Perceptron Approach for Customer Churn Prediction 2017A Novel Approach for Churn Prediction Using Deep Learning 2021Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning 2021Distributed model for customer churn prediction using convolutional neural network 2021Deep Learning as a Vector Embedding Model for Customer Churn 2016Churn analysis using deep convolutional neural networks and autoencoders 2019Predicting Customer Churn in the Telecommunication Industry by Analyzing Phone Call Transcripts with Convolutional Neural Networks

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