Churn Analysis Research Articles

Customer Churn Analysis Using Machine Learning to Improve Customer Retention on Vissie Net

Customer Churn Analysis Using Machine Learning to Improve Customer Retention on Vissie Net

Customer churn analysis using feature optimization methods and tree-based classifiers

Customer churn analysis using feature optimization methods and tree-based classifiers

Customer data prediction and analysis in e-commerce using machine learning

Customer churn is a major challenge faced by e-commerce companies, as it leads to loss of revenue and decreased customer loyalty. In recent years, for predicting and reducing client churn machine learning techniques are powerful tools. This research aims to explore the use of machine learning algorithms for predicting customer churn, annual spending, and product on-time delivery in e-commerce. The study first conducted a comprehensive review of the literature on customer churn in machine learning. The literature showed that customer churn has been predicted successfully using a variety of machine learning algorithms, including support vector machine (SVM), random forest, and decision tree in various industries. To address this gap in the literature, the study conducted an empirical analysis of customer churn in e-commerce using machine learning algorithms. The data were then pre-processed and analyzed utilizing machine learning techniques for prediction. According to the study’s findings, machine learning algorithms are effective in predicting customer churn, and product on-time delivery in e-commerce. The best-performing algorithm SVM achieved an accuracy of 83.45% in predicting customer churn and 68.42% for product on-time delivery prediction.

Customer Churn Analysis in Telecom Industry using Kaplan–Meier and Cox Proportional Hazards Model

Customer churn poses a significant challenge for companies in the telecom industry, impacting revenue and profitability. In this paper, we present a comprehensive survey of customer churn analysis methodologies, focusing on the application of logistic regression, Kaplan-Meier survival analysis, and the Cox Proportional Hazards Model. Logistic regression serves as a fundamental technique for binary classification, enabling the prediction of churn based on customer attributes and behaviors. Kaplan-Meier survival analysis offers insights into customer survival probabilities over time, facilitating the identification of high-risk cohorts. Additionally, the Cox Proportional Hazards Model provides a flexible framework for analyzing the impact of predictor variables on churn timing, accounting for censoring and time-varying covariates. Through a review of existing literature, case studies, and practical applications, we explore the strengths and limitations of these methodologies in customer churn prediction. We also discuss challenges in data collection, feature engineering, and model evaluation, along with future research directions to enhance churn analysis effectiveness. By leveraging logistic regression with Kaplan-Meier and the Cox model, telecom companies can develop robust churn prediction models to improve customer retention strategies and mitigate revenue loss. Keywords: Customer churn analysis, Telecom industry, Logistic regression, Kaplan-Meier survival analysis, Cox Proportional Hazards Model, Retention strategies.

A Comparative Study on Customer Churn Analysis Using Machine Learning and Data Enrichment Techniques

With the increasing amount of online shopping, companies can collect more customer data. Companies use this data to get to know their customers better and provide customized services. Churn analysis is one of the most essential analyses derived from the vast amount of data collected, which provides information about when a customer will stop shopping with the company. In this study, we perform a churn analysis using machine learning (ML) algorithms to analyse the customer behavior data of a fashion retail company. To perform churn analysis, we performed a four-stage methodology. First, we carried out data preparation and visualization studies, and then we created models using various ML algorithms. After examining the baseline data, we added the RFM (Recency, Frequency, Monetary) score to the data with the data enrichment technique and performed the analysis again. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate the data irregularity and performed parameter optimization on the algorithms in SMOTE data. We compared the accuracy and F1 score values obtained after this four-stage process and examined the effect of the algorithms. In the last stage, we divided whole data into clusters using the k-means technique and applied ML algorithms to clustered data. Then, we compared all these results and examined the effect of segmentation on the results. The analysis shows that the extreme gradient boosting algorithm provides better accuracy and F1 score values. Using these results, the company can identify customers likely to churn and begin funding Customer Relationship Management (CRM) efforts. Additionally, experts can determine the company's development directions by organizing campaigns for these customers and analysing their reasons for churn in more detail.

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

Customer Churn Analysis in Telecom Organization

Customer Churn Analysis in Telecom Organization

Customer Churn Analysis Prediction Based on Cluster Analysis and Machine Learning Algorithms

Customer Churn Analysis Prediction Based on Cluster Analysis and Machine Learning Algorithms

Customer Churn Analysis in Telecom Industry using Logistics Regression in Machine Learning with Kaplan–Meier and Cox Proportional Hazards Model

Customer churn poses a significant challenge for companies in the telecom industry, impacting revenue and profitability. In this paper, we present a comprehensive survey of customer churn analysis methodologies, focusing on the application of logistic regression, Kaplan-Meier survival analysis, and the Cox Proportional Hazards Model. Logistic regression serves as a fundamental technique for binary classification, enabling the prediction of churn based on customer attributes and behaviors. Kaplan-Meier survival analysis offers insights into customer survival probabilities over time, facilitating the identification of high-risk cohorts. Additionally, the Cox Proportional Hazards Model provides a flexible framework for analyzing the impact of predictor variables on churn timing, accounting for censoring and time-varying covariates. Through a review of existing literature, case studies, and practical applications, we explore the strengths and limitations of these methodologies in customer churn prediction. We also discuss challenges in data collection, feature engineering, and model evaluation, along with future research directions to enhance churn analysis effectiveness. By leveraging logistic regression with Kaplan-Meier and the Cox model, telecom companies can develop robust churn prediction models to improve customer retention strategies and mitigate revenue loss. Keywords: Customer churn analysis, Telecom industry, Logistic regression, Kaplan-Meier survival analysis, Cox Proportional Hazards Model, Retention strategies.

Optimising customer retention: An AI-driven personalised pricing approach

Customer churn has become one of the most important challenges that telecom companies have to deal with. Churn cases not only cause revenue losses but also impose extra costs of finding new customers. To overcome this issue, they develop various strategies to retain their customers. In this regard, this study presents an integrated artificial intelligence-based model that can meet the expectations of these companies not only to profile the customer churn, but also to predict a service fee that is likely to be accepted by the customers. The model first identifies the customers who are likely to churn and then offers the customers a personalised service fee that is likely to be acceptable. In this study, the K-nearest neighbours, Decision Tree, Random Forest, and Support Vector Machine methods are adapted as classifiers for churn prediction, and regression models of the same methods are utilised to predict the most optimum “personalised” service fee for potential customer churns. Additionally, to reduce the cost of data collection for companies, the feature selection method is used to determine the most optimal feature subset in churn analysis and service fee prediction. The results show that the Random Forest method is superior to other methods in both churn and price predictions and has resulted in as much as a predictive accuracy of 94% and AUC of 98%. The outcome of this comprehensive analysis using four artificial intelligence methods over three diverse telecom datasets, suggests that the proposed “personalised” pricing model in the telecom sector could prevent the churn and increase the profitability by up to 36%. In addition, the model based on SVM suggests that it is possible to reduce the number of required data to be collected by as much as 20%. As the robustness and generalisation ability of the models has been demonstrated over three diverse data sets, it can be further adapted in different sectors.

Customer Churn Analysis Using Deep Reinforcement Learning Approach

Customer churn analysis is a critical component of business strategy in industries where retaining customers is essential for profitability and growth. This study aims to investigate the factors influencing customer churn in the telecommunications sector, utilizing a comprehensive dataset of customer interactions and demographics.

Customer Segmentation and Customer Churn Analysis System for Telecom Companies

The advent of 5G technology and the shifting customer preferences have created a lot of opportunities for telecommunication (telco) companies. Such immense business opportunities have also led to fierce competition in the telco market, accompanied by a high customer churn rate. It is pressing for telco operators to come up with effective marketing strategies based on extensive customer analytics to prevent customer turnover and bolster company revenue. Customer analytics in the telco industry consists of two key components, namely churn prediction and customer segmentation. As the telco market tends to be saturated, too much focus on attracting new subscribers is no longer applicable to the telco industry. The cost of attracting new customers by investing considerable resources is confirmed to be significantly higher than the cost of retaining existing customers. In this context, churn management becomes instrumental in the telco industry. Key Words Business, churn prediction, Bayesian analysis, customer segmentation.

GA optimization-based BRB AI reasoning algorithm for determining the factors affecting customer churn for operators

Customer churn directly leads to the weakening of the economic efficiency of an operator and even the loss of its competitive advantage in the market share. The identification and prediction of customer churn in the era of interconnection has become more complex, as it not only is based on the analysis of customer information and customer churn data but also must take into account the intricate relationships between the market economy and culture. In the era of big data, numerous predictive models are based on more redundant features, which increases the complexity of the algorithms and the difficulty of analyzing customer churn. Therefore, in this paper, a belief rule base (BRB) artificial intelligence inference algorithm based on GA optimization to determine the factors affecting customer churn for operators proposed. First, customer churn data from a website are analyzed, and features are extracted. Second, a BRB is established from the experience of operator experts, and a BRB inference prediction model is constructed for predicting and analyzing customer churn. Finally, the BRB model is optimized by GA optimization, the input characteristics with high feature weights are obtained, and the accuracy of the churn analysis is verified according to the obtained features. The results show that this method not only outperforms the comparative SVM and BP neural network models for predicting customer churn but also provides better judgment of the main inputs of customer churn, thus reducing the reliance on input information, optimizing the complexity of the algorithm, and enabling operators to obtain a more accurate understanding of the main factors that lead to churn.

Analysis and Classification of Customer Churn Using Machine Learning Models

Analysis studies of customer loss (customer churn) have been used for years to increase profitability and build customer relationships with companies. Customer analysis using exploratory data analysis (EDA) to visualize data and the use of machine learning to classify customer churn are often used by past analysts. This study uses several machine learning models that can be used for customer churn classification, namely Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Extreme Gradient Boosting (XGBoost). However, there is a class imbalance factor in the dataset, which is the biggest challenge that analysts usually face in achieving good results in the classification of machine learning models. The Synthetic Minority Oversampling Technique (SMOTE) method is a popular method applied to deal with class imbalances in datasets. The results of the analysis show that the classification of churn customers using the XGBoost algorithm has the best level of accuracy compared to other algorithms, with an accuracy value of 0.829424, and the oversampling method with SMOTE tends to reduce the accuracy value of each classification algorithm. The Permutation Feature Importance (PFI) technique of the XGBoost model gets the result that tenure, monthly contracts, and TV streaming are the features that affect customer churn the most.

Müşteri Kaybı Tahmini için Yeniden Örnekleme ve Topluluk Yöntemleri

Churn analysis is a customer relationship management analytics that companies implement to predict the customers who are likely to terminate doing business with them. The success of marketing efforts to retain the existing customers is possible only if probable churners are correctly specified beforehand. Therefore, having powerful models with high prediction capabilities that lead to a profit growth is crucial. The imbalanced nature of churn datasets negatively effects the classification performance of machine learning methods. This study examines resampling –over- and under-sampling- and ensemble learning –bagging, boosting, and stacking– strategies integrated with the cross-validation procedure on imbalanced churn prediction. The experimental results, which are compared to the results of Support Vector Machines taken as the benchmark, show that ensemble methods improve the prediction performances. Also, applying over-sampling achieves a noticeable performance in comparison with the under-sampling approach.

Customer Churn Analysis

Abstract: Customer churn analysis is a critical process for businesses to understand the reasons why customers stop using their products or services. It involves examining customer data and identifying patterns that can indicate potential churn. By analyzing this data, businesses can take proactive measures to retaincustomers, such as improving the quality of their offerings or targeting specific customer segments. Ultimately, effective customer churn analysis can help businesses reduce customer churn and increase profitability.

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

In today's conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the company’s loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Artificial Neural Networks (ANN) are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

Feature analysis and model comparison of logistic regression and decision tree for customer churn prediction

Customer churn has long been a concern for companies because it not only reduces the company's profit in the short term, but is also extremely detrimental to the company's growth in the long term. This paper focuses on the analysis of customer churn in banks by using two machine learning methods, namely logistic regression and decision tree, to predict the churn rate of customers and analyze the decision tree results based on the premise that decision trees are more accurate in prediction and do not have a large prediction bias for a certain group as logistic regression does. The results show that age, estimated salary and the number of products are important factors when predicting and customer groups with some specific characteristics will show a higher departure rate. To address this situation, this paper recommends that bankers continuously optimize their business systems and focus on user groups with high churn rates.

Predicting and visualization analysis of customer churn in telecommunications leveraging decision tree model

Nowadays, how to keep more customers is one of the most crucial problems to be solved facing communication companies in the increasingly competitive market. In these companies, one of their most important business indicators is customer churn for it is significantly less expensive to keep a current client than to find a new one. In order to carry out truly effective methods to minimize customer loss, build a prediction model is a reasonable way to analyze the characteristics and reasons of possible lost customers. In this research, after creating a Decision Tree Model and visualize it, it could be concluded that there was little difference between male and female, but the aged, unmarried and customers who are economic dependent have a great possibility to lose. Moreover, signing duration matters in the prediction. The longer the contract term is, the less likely the users to be lost. According to mentioned and even more analysis below, certain targeted suggestions are introduced to improve the situation. This research would like to provide research thought and analytical method for the prediction of customer churn.

Comparison of machine learning methods for estimating customer churn in the telecommunication industry

With the rising competition in business, particularly in the telecommunications industry, there has been a growing emphasis on churn prediction. This is attributed to the higher cost involved in attracting new customers than retaining the remaining ones. In telecom churn analysis, the primary goal is to accurately estimate churn behavior by identifying customers who are at risk of leaving. Another objective is to determine the primary reasons for customer churn. Manually predicting the churn in telecommunications is expensive, tedious, and time-consuming. To relieve the burden, machine learning algorithms are introduced to tackle this problem. This article examines several machine learning algorithms for predicting customer churn, including Random Forest, Decision Tree, and Naive Bayes. By constructing and comparing these classification models, the effectiveness of these algorithms is demonstrated. The aim of this work is to examine and compare the effectiveness of various models according to accuracy, which can be applied in real-life scenarios. The result shows that Naive Bayes outperforms the other models.