A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector

Abstract

In the telecom sector, a huge volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining the existing ones. Business analysts and customer relationship management (CRM) analyzers need to know the reasons for churn customers, as well as, behavior patterns from the existing churn customers' data. This paper proposes a churn prediction model that uses classification, as well as, clustering techniques to identify the churn customers and provides the factors behind the churning of customers in the telecom sector. Feature selection is performed by using information gain and correlation attribute ranking filter. The proposed model first classifies churn customers data using classification algorithms, in which the Random Forest (RF) algorithm performed well with 88.63% correctly classified instances. Creating effective retention policies is an essential task of the CRM to prevent churners. After classification, the proposed model segments the churning customer's data by categorizing the churn customers in groups using cosine similarity to provide group-based retention offers. This paper also identified churn factors that are essential in determining the root causes of churn. By knowing the significant churn factors from customers' data, CRM can improve productivity, recommend relevant promotions to the group of likely churn customers based on similar behavior patterns, and excessively improve marketing campaigns of the company. The proposed churn prediction model is evaluated using metrics, such as accuracy, precision, recall, f-measure, and receiving operating characteristics (ROC) area. The results reveal that our proposed churn prediction model produced better churn classification using the RF algorithm and customer profiling using k-means clustering. Furthermore, it also provides factors behind the churning of churn customers through the rules generated by using the attribute-selected classifier algorithm.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

“…Ładyżyński et al [76], for instance, employed Random Forest (RF) and DNN methods and customers' historical transactional data to propose a hybrid model that can predict the customers' willingness to purchase credit products from the banks. Ullah et al [77] used the RF algorithm to predict churn customers and to formulate strategies for customer relationship management to prevent churners. Ullah et al [77] explained that a combination of churn classification, utilizing the RF algorithm and customer profiling using k-means clustering, increased their model's performance.…”

Data Science in Economics

“…Ullah et al [71] use the RF algorithm to prediction churn customers and use their result to formulate strategies for customer relationship management to prevent churners. Ullah et al [71] explain that combination of churn classification utilizing the RF algorithm and customer profiling using k-means clustering increased their model performance. Agarwal [72] integrated RNN and CNN to develop a model for sentiment analysis.…”

Churn rate prediction in Telecommunication systems

“…Tan Yi Fei et al 2017 [31] proposed two examinations with the execution of data handling methods utilizing K-Means and Equal-Width Discretization (EWD) combined with Naïve Bayes are performed alone to advance a alternation of techniques to admit acceptable activity exercises. Irfan Ullah et al, 2019 [32] proposed model initially groups beat clients data utilizing classification algorithms in which the Random Forest (RF) adding performed able-bodied with 88.63% accurately abiding instances. Making able aliment arrange is a capital assignment of the CRM to apprehend churners.…”