A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn

Abstract

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.

Impact of Hyperparameters on Deep Learning Model for Customer Churn Prediction in Telecommunication Sector

“…e same finding as mentioned before has also been published and confirmed in some other relevant papers that had also used the same deep learning methods to predict customer's churn in the business of the telecommunication field, and it can be seen in [33,34]. us, we can gladly inscribe that the approaches which are used to configure the hyperparameters in a neural network where NNs are used for modelling the churn rate seem to be still lacking on a larger scale in the telecommunication sector, and this work has widely been explored in this article, respectively.…”

A Hybrid Prediction Model for E-Commerce Customer Churn Based on Logistic Regression and Extreme Gradient Boosting Algorithm

“…Huang and Wang [9] improved the Iterative Dichotomiser 3 (ID3) algorithm with weighted entropy, constructed a decision tree model based on the improved algorithm, and proved the effectiveness and accuracy of the improved algorithm through empirical analysis. Ahmed et al [10] relied on the genetic algorithm (GA) to identify eight indices of customer churn from basic and transaction data, combined the SVM and neural network (NN) into a hybrid prediction model, and confirmed that the hybrid model is more accurate and efficient than the SVM and NN along.…”

Deep Learning for Distribution Channels' Management

“…using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. [6] E-commerce has provided new opportunities for both businesses and consumers to easily share information, find and buy a product, increasing the ease of movement from one company to another as well as to increase the risk of churn. Studies develop churn prediction model [7] by testing the forecasting capability of the support vector machine (SVM).…”