Customer Retention Analysis for Telecom Industry

Abstract

In today’s highly competitive telecom industry, retaining customers is vital for sustaining business growth and profitability. Customer churn, the phenomenon where customers switch from one service provider to another, poses a significant challenge for telecom companies. Predicting churn can help these companies take proactive measures to retain valuable customers. This study explores the application of machine learning algorithms for predicting customer churn in the telecom industry. Additionally, the research contributes to the existing body of knowledge in the field of customer churn prediction, showcasing the potential of machine learning algorithms in addressing complex business challenges.

Introduction

I. INTRODUCTION

In today’s dynamic and fiercely competitive telecom industry, retaining customers is paramount for sustaining business growth and ensuring profitability. With a plethora of options available to consumers, telecom companies face the significant challenge of minimizing customer churn, the phenomenon where existing customers switch to competitors’ services. Customer churn not only leads to revenue loss but also incurs substantial acquisition costs as companies must invest in attracting new customers to offset the losses. To address this challenge, predictive analytics, and machine learning algorithms have emerged as powerful tools that enable telecom companies to foresee customer churn and take proactive measures to retain valuable subscribers. By leveraging historical customer data and employing advanced analytical techniques, telecom companies can gain valuable insights into customer behavior, preferences, and patterns, allowing them to predict churn and implement targeted retention strategies. The study also investigates the crucial factors contributing to customer churn, ranging from customer demographics to usage patterns, customer service interactions, and pricing plans. By discerning the key drivers of churn, telecom companies can tailor their retention efforts to address these specific issues. Moreover, the interpretability of machine learning models will be explored to provide actionable insights into why customers churn, empowering telecom companies to make informed business decisions. The study’s findings hold immense practical significance for the telecom industry, offering actionable strategies to reduce churn rates, enhance customer satisfaction, and optimize business operations. By harnessing the power of machine learning, telecom companies can proactively engage with their customers, foster loyalty, and ensure long-term sustainability in the ever-evolving telecommunications landscape. Advances in cancer research have led to significant improvements in diagnosis, treatment, and survival rates for many types of cancer.

II. LITERATURESURVEY

1. SA Representation-Based Query Strategy to Derive Qualitative Features for Improved Churn Prediction

The effectiveness of any Machine Learning process depends on the accuracy of annotated data that is used to train a learner. However, manual annotation is expensive. Hence, researchers adopt a semi-supervised approach called active learning that aims to achieve state-of-the-art performance using a minimal number of samples. Although it boosts classifier performance, the underlying query strategies are unable to eliminate redundancy in selected samples. Redundant samples lead to increased cost and sub-optimal performance of learners. Inspired by this challenge, the study proposes a new representation-based query strategy that selects highly informative and representative subsets of samples for manual annotation.[1] 

2. Evaluative study of cluster based customer churn prediction against conventional RFM-based churn model

Churn prediction in retail industry is a very critical measure as retention strategies play a crucial role in any successful business model. In the era of e-commerce and customized customer solutions, it is essential for businesses to be on their toes to predict the changes in customer behavior as early as possible in their life cycle. RFM analysis is one of the age-old techniques that have proven to be an accurate measure of customer behavior and although it aids in customized targeting, it also requires time to amass enough data. It sometimes means that by the time the business collects substantial data about a customer to make strategic decisions, they are already halfway through their change in interests or behavior. Therefore, it is essential to study the possibilities of predicting the future state of a customer well in advance. This study focuses on evaluating the accuracy of Machine Learning Models in predicting the churn of retail customers based on their k-means clusters against their actual  RFM features.[2]

3. Churn Prediction in the Telecommunication Industry

The telecom industry has become one of the leading companies in the entire world. To grow further in this competitive world, it is important for companies to lessen the probability of churn. Churn is the shifting of customers across various service providers. One of the most important tasks that the Telcom industry has to deal with is predicting churn. Customers churn due to several factors including the exciting offers provided by competitors.

The churn rate has a significant impact on the lifetime of customers. As churn has a direct impact on the revenue of the industry, companies are looking forward to model with the best accuracy. In the proposed system model is developed using Machine Learning. Companies will be able to identify the consumers who are most likely to leave and the causes influencing them with the aid of machine learning models. This data can be used by the service providers to improve their services and thereby reduce churn.[3]

4. A Machine Learning Approach to Predict Customer Churn of a Delivery Platform.

The use of delivery platforms has become widespread due to the impact of the Covid- 19 and the O2O industry. However, the ELEME delivery platform, a subsidiary of Alibaba Group, which represents China, has recently been losing market share. This means that companies need to constantly look at strategies to attract new customers and maintain existing ones. In general, it costs at least five times more to attract new customers than it does to manage existing customers. This paper attempts to predict customer churn using the ELEME customer dataset to develop strategies to identify and prevent churn in advance. The results of the analysis using the machine learning ap- approach found that the most influential feature that can predict churn is the number of clicks made by the user.

This paper presents the process and explanation of applying various algorithms for predicting customer churn on a distribution platform. It also proposes strategies for dealing with customer churn.[4]

5. On the Effectiveness of Features for Predicting User Churn in Reddit Communities

Predicting which users are likely to leave a given online community has been studied for several types of platforms. The problem of predicting whether a given user is going to leave a given community is referred to as the user churn prediction problem. Existing studies have typically used features obtained from the user’s activity records in the community.

In contrast, we use both social-network and inter-community features, as well as the basic features used in existing studies. In this paper, we focus on Reddit as an example of a popular online community platform. We extract several features from records of comments in Reddit communities, and then use them to construct models for user churn prediction and to evaluate their prediction accuracy.[5]

III. SYSTEM ARCHITECTURE

Preprocessing is the initial step in data preparation,It involves cleaning,transforming,and organizing raw data,preprocessing ensures that data is it a suitable format for model training.Feature extraction techniques can include dimensionality reduction methods like PCA or feature engineering to create new features.and focusing on important information.

Effective features extraction can improve model performance classification is used in various applications, such as spam email detection , image recognition, and disease diagnosis. Common classification algorithms includes decision trees,support vector machines,logistic regression and neutral networks. The output can be used to tasks like making recommendations,identifying anomalies or making decisions based on the classification results.

IV. ALGORITHM

SVM

SVMs can model non-linear decision boundaries effectively using techniques likethe kernel trick, which transforms data into a higher-dimensional space where non-linearrelationshipsbecomelinear.

SVMs are less prone to overfitting, especially when compared to some other machine learning algorithms.

Step1:Loadtheimportantlibraries.

Step2:ImportdatasetandextracttheXvariablesandYseparately.

Step3:Dividethe datasetinto trainand test.

Step4:InitializingtheSVMclassifiermodel.

Step5:FittingtheSVMclassifiermodel.

Step 6: Coming up with predictions.Step7:Evaluatingmodel’sperformance.

Advantages:

1. Service Quality Assessment.
2. It provides clarity regarding its customer retention which reflects the quality and usefulness of its service.
3. Identification of areas requiring improvement.
4. Cost Savings and Revenue Growth.

Conclusion

Customer churn prediction in the telecom industry using machine learning algorithms is a vital and feasible initiative that offers significant benefits to telecom companies. By leveraging advanced analytical techniques, telecom providers can proactively identify customers at risk of churning, allowing for targeted retention strategies and improved customer satisfaction. By investing in this technology, telecom providers can not only reduce churn rates but also foster stronger customer relationships, ensuring their long-term success in a highly competitive industry. The future machine learning and artificial intelligence continue to advance, telecom companies will leverage more sophisticated algorithms and predictive models to identify potential churners. This will improve the accuracy of predictions and reduce false positives.Big Data Analytics: With the increasing volume of data generated by telecom customers, big data analytics will play a significant role in customer churn prediction. Analyzing vast datasetscan uncover hidden patterns and insights that were previously challenging to identify. Real-time Predictions: The ability to predict churn in real-time is becoming increasinglyimportant. Telecom companies can take immediate actions to retain customers by identifyingissuesoffering personalized incentives when a customer shows signs of leaving.Personalization:Tailoring retention strategies to individual customers is a growing trend.Telecom providers will use predictive analytics to understand each customer\'s preferences andneeds, allowing for personalized offers and services.

References

[1] W. M. Van der Aalst, “Process modeling and analysis,” in Process Mining: Data Science in Action, 2nd ed. Berlin, Germany: Springer, 2016, ch. 3, pp. 55–88. [2] S. Sakr, Z. Maamar, A. Awad, B. Benatallah, and W. M. P. van der Aalst, “Business process analytics and big data systems: A roadmap to bridge the gap,” IEEE Access, vol. 6, pp. 77308–77320, 2018. [3] Z. Tangyuan and S. Moro, “Research trends in customer churn prediction: A data mining approach,” in Proc. World Conf. Inf. Syst. Technol., 2021, pp. 227–237. [4] A. D. Caigny, K. Coussement, and K. W. D. Bock, “A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees,” Eur. J. Oper. Res., vol. 269, pp. 760–772, Sep. 2018. [5] N. Alboukaey, A. Joukhadar, and N. Ghneim, “Dynamic behavior based churn prediction in mobile telecom,” Expert Syst. Appl., vol. 162, Dec. 2020, Art. no. 113779 [6] 6 K. Viol, H. Scho¨ller, A. Kaiser, C. Fartacek, W. Aichhorn, and G. Schiepek, “Detecting pattern transitions in psychological time series—A validation study on the pattern transition detection algorithm (PTDA),” PLoS ONE, vol. 17, no. 3, Mar. 2022,Art.no.e

Copyright

Copyright © 2023 Prof. Soniya Dhotre, Sanskruti Galange, Shweta Awasarmol, Mayuri Patil, Raviraj Malule. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.