Customer Churn Research Articles

Identifying Key Learning Algorithm Parameter of Forward Feature Selection to Integrate with Ensemble Learning for Customer Churn Prediction

Identifying Key Learning Algorithm Parameter of Forward Feature Selection to Integrate with Ensemble Learning for Customer Churn Prediction

Evaluating Machine Learning Techniques for Predicting Customer Churn in E-Commerce: A Comparative Analysis

Evaluating Machine Learning Techniques for Predicting Customer Churn in E-Commerce: A Comparative Analysis

Enhancing customer retention in telecom industry with machine learning driven churn prediction

Customer churn remains a critical concern for businesses, highlighting the significance of retaining existing customers over acquiring new ones. Effective prediction of potential churners aids in devising robust retention policies and efficient customer management strategies. This study dives into the realm of machine learning algorithms for predictive analysis in churn prediction, addressing the inherent challenge posed by diverse and imbalanced customer churn data distributions. This paper introduces a novel approach—the Ratio-based data balancing technique, which addresses data skewness as a pre-processing step, ensuring improved accuracy in predictive modelling. This study fills gaps in existing literature by highlighting the effectiveness of ensemble algorithms and the critical role of data balancing techniques in optimizing churn prediction models. While our research contributes a novel approach, there remain avenues for further exploration. This work evaluates several machine learning algorithms—Perceptron, Multi-Layer Perceptron, Naive Bayes, Logistic Regression, K-Nearest Neighbour, Decision Tree, alongside Ensemble techniques such as Gradient Boosting and Extreme Gradient Boosting (XGBoost)—on balanced datasets achieved through our proposed Ratio-based data balancing technique and the commonly used Data Resampling. Results reveal that our proposed Ratio-based data balancing technique notably outperforms traditional Over-Sampling and Under-Sampling methods in churn prediction accuracy. Additionally, using combined algorithms like Gradient Boosting and XGBoost showed better results than using single methods. Our study looked at different aspects like Accuracy, Precision, Recall, and F-Score, finding that these combined methods are better for predicting customer churn. Specifically, when we used a 75:25 ratio with the XGBoost method, we got the most promising results for our analysis which are presented in this work.

Data-Driven Decision-Making: Accurate Customer Churn Prediction with Cat-Boost

In the cutthroat telecommunications industry, customer churn poses a significant threat to profitability and brand reputation. To combat this, we propose a data-driven approach to predict churn, leveraging advanced Machine Learning (ML) algorithms on a comprehensive dataset of 7043 customer records with 21 features sourced from the IEEE website. By applying one-hot encoding to transform categorical data, we optimize ML algorithm performance. Our study pits three ML algorithms against each other: Cat Boost, Stochastic Gradient Boost, and Extreme Gradient Boost. The results are striking, with Cat Boost incipient as the top performer, boasting an impressive 94% accuracy, 90% precision, 97% recall, 94% F1-score, and an AUC of 0.94. These discoveries emphasize Cat Boost's extraordinary ability to predict churn, empowering telecom companies to proactively prevent attrition, target high-risk customers, and make data-driven decisions to make the most of profits. In today's speedily developing technical landscape, correctly predicting churn is critical to meeting the ever-changing potential of customers. Our research marks a noteworthy innovation in addressing churn trials, flooring the way for long-term accomplishment in the telecom industry.

Customer churn modeling in telecommunication using a novel multi-objective evolutionary clustering-based ensemble learning

Customer churn modeling in telecommunication using a novel multi-objective evolutionary clustering-based ensemble learning

Predictive Analytics Applications for Enhanced Customer Retention and Increased Profitability in the Telecommunications Industry

Predictive analytics applications have a lot of potential to help the telecommunications business keep customers and make more money. However, more studies are needed to use industry data to build and test solid predictive models for important customer relationship management tasks. The study tries to create models that can predict customer churn, lifetime value, and segmentation by using a dataset from a prominent telecom provider that includes demographic, usage, transactional, and survey response data. Descriptive statistics will be used to describe the group and find the most critical customer traits that affect retention. The research will use logistic models, decision trees, and neural networks to see how well they can predict churn. Using regression methods, different ways of keeping a customer will be used to figure out how much they are worth over their career. Customers will be put into groups by clustering algorithms based on how likely they are to stay as customers. When the results come in, they will show how well different types of predictive modeling keep people. We will look at the best models to find out more about how the things about a customer affect their likely to stick with a business. For each segmented group, a customer profile will be made, and specific ways to keep customers will be offered. People will talk about the data in terms of past studies and methods. We will also talk about what happens when you use predictive analytics to make data-driven plans to keep customers and make the most money throughout the customer journey. The main point of this study is to make predictive analytics work better in the telecoms business to keep customers. By building and testing predictive models on a real-world industry dataset, we can learn more about how to use customer data and analytics carefully to make relationships better, decide where to help users, and make more money from them over time.

Bank Customer Churn Prediction Using Machine Learning Framework

Abstract Using real customer data from a large community bank in the South of the US, this paper analyzes the customer churn prediction problem by constructing and comparing ten machine learning classification models with five sample techniques. Our results show that Random Forest, XG Boost, AdaBoost, and Bagging Meta classifiers dominate others in terms of overall accuracy, F-score, and AUC curve for the test observations. For the four classifiers, the overall accuracy ranges from 87% to 96% across five different sampling methods explored, while the AUC values range between 0.9 to 0.93. Considering overall accuracy and F-Score, AdaBoost with original and MTDF sampling technique dominates others; however, considering the AUC measure, XG Boost and Random Forest perform similarly to AdaBoost, which slightly dominate Bagging Meta across all sampling techniques; although the performance measures for these four classifiers are comparable across all sampling techniques. The paper further presents important features of customer churn behavior as predicted by the model. The diagnostic analysis also provides an insightful comparison between churned and non-churned customers. JEL classification numbers: C0, C5, C8, G21. Keywords: Machine learning, Big data, Sampling techniques, Customer churn, Customer retention, Financial services, Community bank.

Customer Churn Prevention For E-commerce Platforms using Machine Learning-based Business Intelligence

Aims & Background: Businesses in the E-commerce sector, especially those in the business- to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. Objective: The main objective of this research is to detect probable churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Methodology: Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. Results: The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. Conclusion: To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle.

An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based

Metaheuristics is an optimization method that improves and completes a task in a short period of time based on its objective function. The goal of metaheuristics is to search the search space for the best solution. Machine learning detects patterns in large amounts of data. Machine learning encourages enterprise automation in a variety of areas in order to improve predictive ability without requiring explicit programming to make decisions. The percentage of customers who leave the company or stop using the service is referred to as churn. The purpose of this research is to forecast customer churn in the market business. Particle swam optimization (PSO) was used in this study as a metaheuristic method to provide a strategy to guide the search process for new customers and obtain parameters for processing by support vector regression (SVR). SVR predicts the value of a continuous variable by determining the best decision line to find the best value. The number of transactions, the number of periods, and the conversion value are the parameters that are visible. Efficiency models are added to improve prediction results through two optimizations: prediction flexibility and risk minimization. The findings demonstrate the effectiveness of prediction in reducing customer churn.

Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods

Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods

Assessing the effectiveness of OTT services, branded apps, and gamified loyalty giveaways on mobile customer churn in the telecom industry: A machine-learning approach

Telecom operators allocate a significant amount of resources to retain their customers as the organic growth in the number of customers is slowing down. Gamified loyalty programs, branded apps, and over-the-top (OTT) services emerged as ways to develop customer acquisition and retention strategies. Despite these strategies, some mobile customers still churn; therefore, churn prediction plays an essential role in the sustainable future of telecom businesses. Churn prediction is used both to detect customers with a high propensity to churn and to identify the reasons behind their churn behavior. This study examines several features affecting the churn behavior of mobile customers, including branded apps, gamified loyalty programs, and OTT services. In this study, the secondary data is provided by a telecom operator and contains the attributes of both churner and non-churner mobile customers. Logistic regression and random forest classifiers are compared in terms of their predictive power, and we used the latter as the machine learning algorithm in the churn prediction model. To understand the variable importance, mean decrease in impurity and permutation importance are performed. The key findings of this research reveal that while gamified loyalty giveaways and branded app strategies are effective, OTT service strategies show lower importance in predicting mobile customer churn behavior.

Leveraging Various Feature Selection Methods for Churn Prediction Using Various Machine Learning Algorithms

This study aims to examine the effect of customer experience on customer retention at DQLab Telco, using machine learning techniques to predict customer churn. The study uses a dataset of 6590 customers of DQLab Telco, which contains various features related to their service usage and satisfaction. The data includes various features such as gender, tenure, phone service, internet service, monthly charges, and total charges. These features represent the demographic and service usage information of the customers. The study applies several feature selection methods, such as ANOVA, Recursive Feature Elimination, Feature Importance, and Pearson Correlation, to select the most relevant features for churn prediction. The study also compares three machine learning algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, to build and evaluate the prediction models. This study finds that Logistic Regression without feature selection achieves the highest accuracy of 79.47%, while Random Forest with Feature Importance and Gradient Boosting with Recursive Feature Elimination achieve accuracy of 77.60% and 79.86%, respectively. The study also identifies the features influencing customer churn most, such as monthly charges, tenure, partner, senior citizen, internet service, paperless billing, and TV streaming. The study provides valuable insights for DQLab Telco in developing customer churn reduction strategies based on predictive models and influential features. The study also suggests that feature selection and machine learning algorithms play a vital role in improving the accuracy of churn prediction and should be customized according to the data context.

Smart Customer Churn Management System Using Machine Learning

While the speed of digitalization is rapidly evolving, the importance of the competition and customer experience is simultaneously gaining prominence. It is crucial for banks to understand the needs of customers and offer special products or opportunities accordingly, through developing automated systems that monitor and analyse reasons for customer churn and predict customers who are likely to leave in the future. The system aims to improve customer satisfaction by reducing customer churn, which is a term used to describe the loss of clients or subscribers for whatever reason from an organisation. This research seeks to leverage on the core processes of machine learning involved in building such a system, in order to address the business question ‘How to prevent customer attrition or churn?’ The adopted methodology involves four different phases, namely, data preparation and processing, feature extraction, dataset and testing, performance evaluation. Six classification algorithms are applied and compared against each other, and the best performing model is selected for the system implementation. In comparing the models, random forest classifier is selected with the accuracy of 87% and precision of 85%.

A Review on Classification Algorithm for Customer Churn Classification

Any sector faces a huge obstacle when it comes to retaining existing customers. The percentage of consumers who have quit using a product or service is referred to as customer churn, and it is a vital indication that offers reliable information about this percentage. When it comes to achieving long-term success in a market or industry, one of the most significant challenges that any company must face is the ability to keep their precious clients and to fulfill their needs. A review of the most significant studies on Customer Churn Prediction is presented in this paper so as to furnish the reader with an overview of frequently employed data mining methodologies and their respective performances. We provide the available statistics in addition to customer information in order to approximate customer attrition. The time period encompassing the survey extends from 2003 to 2023. During the process of Customer Churn Prediction, we identified the issues and difficulties that were linked with it and offered guidance and potential remedies.

Implementation of a client–centric model of customer service (based on the example of customer service in the field of telecommunications and communications)

Growing competitive pressure and problems of customer churn in the telecommunications industry determine the need for an intensive transition to customer–centric service. The article studies the features of the implementation of a client–centric model of customer service using the example of customer service in the field of telecommunications and communications. The key stages and principles of transition to a customer–centric model are formulated, and the maturity levels of the customer relationship management system are highlighted. Based on the analysis of scientific literature on the research topic, the features and key approaches to managing customer service, development and strategic planning in the activities of telecommunications companies are determined. Objective differences are established in building relationships with B2C and B2B client segments. Based on the results of the study, recommendations are formulated for the implementation of a client–centric model of client service in the field of telecommunications and communications, suggesting a transition to an integrated construction of a client–centric business model for the telecommunications business. It is emphasized that despite the historical tendency towards short–term planning in the telecommunications industry, a customer–centric business model is becoming increasingly in demand in inextricable connection with the ecosystem approach and maximum customer retention.

Utilizing Data Science and AI for Customer Churn Prediction in Marketing

This study explores the application of data science and AI techniques in predicting customer churn within the telecommunications industry, a sector characterized by intense competition and high customer turnover rates. By analyzing historical customer data, including usage patterns and service preferences, the study aims to identify factors contributing to churn and propose targeted retention strategies to mitigate losses. Traditional classification algorithms and ensemble techniques are evaluated using the Telecom-Customer-Churn dataset, with emphasis on the underutilized Stacking ensemble method. The results demonstrate that ensemble learning algorithms, particularly the Stacking model, outperform single algorithms, with CatBoost exhibiting the highest accuracy at 0.8119, followed closely by RandomForest at 0.7902 and XGBoost at 0.7820. These findings underscore CatBoost's superior generalization capabilities, likely attributed to its adept handling of categorical features and missing values, and its ability to model complex data relationships. The study contributes to advancing understanding of ensemble models and offers valuable insights for predicting telecom customer churn, thereby aiding in the development of effective retention strategies and enhancing customer satisfaction and loyalty.

A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry

A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry

Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models

Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use of ensemble learning models to analyze and forecast customer churn in the telecommunications business. Accurate churn forecasting is essential for successful client retention initiatives to combat regular customer churn. We used innovative and improved machine learning methods, including Decision Trees, Boosted Trees, and Random Forests, to enhance model interpretability and prediction accuracy. The models were trained and evaluated systematically by using a large dataset. The Random Forest model performed best, with 91.66% predictive accuracy, 82.2% precision, and 81.8% recall. Our results highlight how well the model can identify possible churners with the help of explainable AI (XAI) techniques, allowing for focused and timely intervention strategies. To improve the transparency of the decisions made by the classifier, this study also employs explainable artificial intelligence methods such as LIME and SHAP to illustrate the results of the customer churn prediction model. Our results demonstrate how crucial it is for customer relationship managers to implement strong analytical tools to reduce attrition and promote long-term economic viability in fiercely competitive marketplaces. This study indicates that ensemble learning models have strategic implications for improving consumer loyalty and organizational profitability in addition to confirming their performance.

Analysis of Customer Churn in Subscription Based Streaming Services – A Comparative Study of Spotify and Jiosaavn

Analysis of Customer Churn in Subscription Based Streaming Services – A Comparative Study of Spotify and Jiosaavn

Impact of Artificial Intelligence on Marketing strategies with reference to MNCs

Artificial intelligence (AI) is rapidly transforming the marketing landscape. This literature-based study explores the diverse applications of AI in marketing functions, drawing insights from academic journals, industry reports, and marketing publications. The study examines how AI personalizes marketing efforts through targeted advertising and content marketing, while also investigating its role in customer relationship management (CRM). It delves deeper into AI's influence on market research and marketing ROI (Return on Investment) optimization. By analyzing these applications, the study aims to provide a comprehensive understanding of how AI is shaping the future of marketing. Keywords: Artificial intelligence (AI), machine learning, customer segmentation, targeted advertising, content personalization, CRM, market research, marketing ROI, chatbots, recommendation engines, customer churn prediction.