Customer Churn Prediction Model Research Articles

Application of machine learning techniques for churn prediction in the telecom business

The telecom business generates a significant amount of data on a daily basis due to its massive client base. Acquiring a fresh client base is more expensive than retaining existing customers, whereas churn refers to customers transitioning from one company to another within a specified timeframe. Telecom managers and analysts investigate why customers cancel their subscriptions and analyze the behavior patterns of customers who have stopped using the services. In this work employs categorization methodologies to determine the instances of leave subscriptions and gathers the rationales behind client leave subscriptions in the telecommunications sector. The primary objective of this work is to examine various machine learning algorithms necessary for creating customer churn prediction (CP) models and identifying the reasons for churn. This work aims to provide retention strategies and plans to address churn. This work utilizes machine learning (ML) technique such as random forests (RF) to collect and classify client data for leave subscriptions. These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. This allows business management to take timely action to prevent churn and minimize profit loss. In this work obtains an accuracy of 98.1 % by utilizing the random forest classifier for churn prediction. The classifier matrix has obtained a precision of 92.8 % and a recall factor of 92.7 %, resulting in an overall accuracy of 95.6 %. Similarly, our research endeavors enhance churn prediction, encompass additional business domains, and furnish prediction models to retain their current consumers, improve customer service, and efficiently prevent churn.

Membangun Model Prediksi Churn Pelanggan yang Akurat

Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compared to previously implemented rule-based methods. The findings of this research allow TELCO Company to identify high-risk customers more effectively and implement targeted retention strategies. Results show that the resulting model can identify customers at risk of churn more effectively, enabling more targeted retention actions..

Ensemble-based deep learning techniques for customer churn prediction model

PurposeThe new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.Design/methodology/approachThe features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.FindingsThe accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.Originality/valueThe proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.

Churn Prediction in Telecommunication Industry: A Comparative Analysis of Boosting Algorithms

The issue of customer churn, which is a major problem in the telecommunications industry, poses several challenges, such as financial implications, client attrition, and increased marketing costs. The advancements achieved in the domain of machine learning and artificial intelligence have significantly expanded the possibilities for forecasting customer churn, presenting a promising resolution for effectively handling customer attrition and enhancing customer retention. This study presents a customer churn prediction model that uses machine learning approaches to assist telecom firms in enhancing customer retention and mitigating churn rates. The study employs machine learning techniques, such as Adaboost, Gradient Boosting, and Extreme Gradient Boosting (XGBoost), in order to evaluate extensive datasets and provide predictions on customer churn via a comparative evaluation. The methodology involves extracting data from the Kaggle data pool, doing further data preparation, and identifying relevant features. The Synthetic Minority Oversampling Technique (SMOTE) is used as a strategy to mitigate the challenges posed by imbalanced data. The dataset is partitioned into training and testing sets at a ratio of 75% to 25%. The XGBoost model demonstrated superior accuracy and recall, positioning itself as the top-performing model among the studied models. The attained accuracy rate was 89.51%. The XGBoost method has a recall rate of 92.48%, which is the highest of the three algorithms evaluated. Gradient boosting follows with a recall rate of 87.69%, while Adaboost achieves a recall rate of 85.13%. These findings underscore the potential of machine learning techniques for addressing the challenges posed by customer churn in the telecommunications industry.

AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn

Understanding consumer churn is pivotal for companies in the USA to develop efficient strategies for consumer retention and reduce its negative effects on revenue and profitability. To start with, understanding client churn entails pinpointing the factors that contribute to it. This research paper delved into the application of machine learning algorithms such as Random Forests and Decision Trees for designing churn prediction models and exploring key factors that churn probabilities. The dataset used in this study was sourced from the prominent UCI repository of machine learning databases, preserved at the University of California, Irvine. This dataset provided extensive information on a total of 3333 clients, facilitating in-depth analysis and insights. Models performance evaluation comprised examining the model's efficiency using a confusion matrix. Random Forest seemed to be a relatively better performing model than Decision Tree for this specific classification task. In particular, Random Forest attained higher accuracy (96.25%), precision (91.49), Recall (83.49%), F-measure (0.87), and Phi coefficient (0.85). By deploying Random Forest and Decision Tree models, government companies can get an in-depth comprehension of the factors that lead to consumer churn. As a result, this information may enable them to tailor targeted retention strategies and interventions. By effectively retaining consumers, government organizations can maintain a stable customer base, leading to sustained revenue and economic growth.

Hybrid black-box classification for customer churn prediction with segmented interpretability analysis

Customer retention management relies on advanced analytics for decision making. Decision makers in this area require methods that are capable of accurately predicting which customers are likely to churn and that allow to discover drivers of customer churn. As a result, customer churn prediction models are frequently evaluated based on both their predictive performance and their capacity to extract meaningful insights from the models. In this paper, we extend hybrid segmented models for customer churn prediction by incorporating powerful models that can capture non-linearities. To ensure the interpretability of such segmented hybrid models, we introduce a novel model-agnostic approach that extends SHAP. We extensively benchmark the proposed methods on 14 customer churn datasets on their predictive performance. The interpretability aspect of the new model-agnostic approach for interpreting hybrid segmented models is illustrated using a case study. Our contributions to decision making literature are threefold. First, we introduce new hybrid segmented models as powerful tools for decision makers to boost predictive performance. Second, we provide insights in the relative predictive performance by an extensive benchmarking study that compares the new hybrid segmented methods with their base models and existing hybrid models. Third, we propose a model-agnostic tool for segmented hybrid models that provide decision makers with a tool to gain insights for any hybrid segmented model and illustrate it on a case study. Although we focus on customer retention management in this study, this paper is also relevant for decision makers that rely on predictive modeling for other tasks.

Predicting churn with filter-based techniques and deep learning

Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.

Customer churn prediction model in enterprises using machine learning

Customer churn prediction model in enterprises using machine learning

Telecom customer churn prediction model : Analysis of machine learning techniques for churn prediction and factor identification in telecom sector

This research project leverages exploratory data analysis (EDA) on a telecom company’s customer data to predict user churn. Utilizing Python and its libraries, including Pandas, NumPy, Matplotlib, and Scikit-Learn, it identifies key parameters crucial for accurate predictions. The results and predictions are visualized using the Flask framework and Power BI analytics tool. In the highly competitive telecommunications sector, customer churn poses a significant challenge, with an annual churn rate of 15-25%. This study addresses the escalating phenomenon of consumers freely switching between providers, leading to financial losses for businesses. By employing machine learning models, it aims to forecast potential churners, enabling companies to focus targeted retention efforts and mitigate losses. The tool’s efficacy lies in its ability to analyze patterns among churned users, offering a solution to a pressing issue in the telecom industry. Beyond its immediate application, this tool can be extended to other industries, providing valuable insights for customer retention strategies.

Empirical analysis of tree-based classification models for customer churn prediction

Customer churn is a vital and reoccurring problem facing most business industries, particularly the telecommunications industry. Considering the fierce competition among telecommunications firms and the high expenses of attracting and gaining new subscribers, keeping existing loyal subscribers becomes crucial. Early prediction of disgruntled subscribers can assist telecommunications firms in identifying the reasons for churn and in deploying applicable innovative policies to boost productivity, maintain market competitiveness, and reduce monetary damages. Controlling customer churn through the development of efficient and dependable customer churn prediction (CCP) solutions is imperative to attaining this goal. According to the outcomes of current CCP research, several strategies, including rule-based and machine-learning (ML) processes, have been proposed to handle the CCP phenomenon. However, the lack of flexibility and robustness of rule based CCP solutions is a fundamental shortcoming, and the lopsided distribution of churn datasets is deleterious to the efficacy of most traditional ML techniques in CCP. Regardless, ML-based CCP solutions have been reported to be more effective than other forms of CCP solutions. Unlike linear-based, instance-based, and function-based ML classifiers, tree-based ML classifiers are known to generate predictive models with high accuracy, high stability, and ease of interpretation. However, the deployment of tree-based classifiers for CCP is limited in most cases to the decision tree (DT) and random forest (RF). Hence, this research investigated the effectiveness of tree-based classifiers with diverse computational properties in CCP. Specifically, the CCP performances of diverse tree-based classifiers such as the single, ensemble, enhanced, and hybrid tree-based classifiers are investigated. Also, the effects of data quality problems such as the class imbalance problem (CIP) on the predictive performances of tree-based classifiers and their homogeneous ensemble variants on CCP were assessed. From the experimental results, it was observed that the investigated tree-based classifiers outperformed other forms of classifiers such as linear-based (Support Vector Machine (SVM)), instance-based (K-Nearest Neighbour (KNN)), Bayesian-based (Naïve Bayes (NB)) and function-based (MultiLayer Perceptron (MLP)) classifiers in most cases with or without the CIP. Also, it was observed that the CIP has a significant effect on the CCP performances of investigated tree-based classifiers, but the combination of a data sampling technique and a homogeneous ensemble method can be an effective solution to CIP and also generate efficient CCP models.

E-Commerce Customer Churn Prediction for the Marketplace in Malaysia

Customer churn, or the loss of customers, is a significant challenge for e-commerce businesses, as it leads to revenue loss and increased marketing costs. Most online marketplaces have insufficient awareness of the e-commerce customer churn rate that can be analysed before conducting customer retention activities or management. It is crucial for an online marketplace to handle customer churn as it is more high-value to manage existing customers than to have new customers on the platform. This study uses machine learning algorithms to predict e-commerce customer churn. This project has three (3) goals that must be met, which include identifying the attributes with high association to e-commerce customer churn for the online marketplace in Malaysia, constructing e-commerce customer churn prediction model with the important attributes by using the machine learning techniques, and evaluating and comparing the performance of the e-commerce customer churn prediction models using evaluation metrics. The machine learning techniques chosen based on the literature review are Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistics Regression (LR), K-Nearest Neighbours (KNN), and eXtreme Gradient Boosting (XGB). The dataset involved in the project was compiled from a leading e-commerce platform in Malaysia – online marketplace ABC, and it contains customer demographic information, purchase history etc. The dataset and model will use Python as the analytics tool and extended CRISP-DM with TDSP as the methodology. There will be four (4) evaluation metrics to compare and evaluate the performance of the prediction models, including accuracy, precision, recall, and F1-score. The desired outcome will show that machine learning can predict Malaysian e-commerce customer churn. This study can help Malaysian e-commerce companies identify at-risk customers and retain them. Additionally, this study can inform e-commerce customer churn prediction research.

Customer churn prediction in telecom sector using machine learning techniques

In the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from one firm to another in a given stipulated time. Telecom management and analysts are finding the explanations behind customers leaving subscriptions and behavior activities of the holding churn customers’ data. This system uses classification techniques to find out the leave subscriptions and collects the reasons behind the leave subscription of customers in the telecom industry. The major goal of this system is to analyze the diversified machine learning algorithms which are required to develop customer churn prediction models and identify churn reasons in order to give them with retention strategies and plans. In this system, leave subscriptions collects customers' data by applying classification algorithms such as Random Forest (RF), machine learning techniques such as KNN and decision tree Classifier. It offers an efficient business model that analyzes customer churn data and gives accurate predictions of churn customers so that business management may take action within the churn period to stop churn as well as loss in profit. System achieves an accuracy of 99 % using the random forest classifier for churn predicts, the classifier matrix has achieved a precision of 99 % with a recall factor of 99 % alongwith received overall accuracy of 99.09 %. Likewise, our research work improves churn prediction, scope other business fields, and provide prediction models to hold their existing customers customer service, and avoid churn effectively.

Retracted: A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

Retracted: A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

Classification of customer churn prediction model for telecommunication industry using analysis of variance

Customer predictive analytics has shown great potential for effective churn models. Thriving in today's telecommunications industry, discerning between consumers who are likely to migrate to a competitor is enormous. Having reliable predictive client behavior in the future is required. Machine learning algorithms are essential to predict customer turnovers, and researchers have proposed various techniques. Churn prediction is a problem due to the unequal dispersal of classes. Most traditional machine learning algorithms are ineffective in classifying data. Client cluster with a higher risk has been discovered. A support vector machine is employed as the foundational learner, and a churn prediction model is constructed based on each analysis of variance. The separation of churn data revealed by experimental assessment is recommended for churn prediction analysis. Customer attrition is high, but an instantaneous support can ensure that customer needs are addressed and assess an employee's capacity to achieve customer satisfaction. This study uses an analysis of variance (ANOVA) with a support vector machine (SVM), classification in analyzing risks in telecom systems It may be determined that SVM provides the most accurate forecast of customer turnover (95%). The projected outcomes will allow other organizations to assess possible client turnover and collect customer feedback.

Customer churn prediction model in cloud environment using DFE-WUNB: ANN deep feature extraction with Weight Updated Tuned Naïve Bayes classification with Block-Jacobi SVD dimensionality reduction

With progressing competitive market, different organizations were desperate to hold this churn rate as minimum value, hence to achieve this, building an effective (CCP) customer churn prediction model is essential. In order to address those issues in CCP, the study deliberated the churn prediction model using DFE-WUNB (Deep Feature Extraction with Weight Updated Tuned Naïve Bayes classifier) in a cloud-computing environment. Due to the huge non-linear features of the Telco customer churn dataset, the pre-processed features are deeply learned by the subsequent two models of ANN. In ANN, the input feature gets multiplied by the weight value, and the resultant output feature passes to the next dense layer in ANN. The deep feature extraction in ANN models facilitates precise accuracy in determining relevant churn features. However, the higher matrix dimensions of features exhibit complexity in prediction, hence this Block Jacobi SVD algorithm is applied to decrease the dimensions of features, such as to create a sparse dataset projected to get fit to the training model for efficient classification. The dimension-reduced features pass through enhanced weighing Naïve Bayes algorithm gets updated with ANN weights and tunes the parameters having greater and best weights to NB classifier, improvising the classification accuracy performance. The comparative assessment of the proposed DFE-WUNB churn prediction model delineated the efficiency of highly accurate churn prediction, outperforming other conventional churn prediction models.

Decoding the DNA of Customer Relationships: the Role of Marketing Diagnostics in the Digital Age

As businesses increasingly recognize the importance of effective customer relationship management, building a rich database and managing it sustainably becomes a crucial factor for success. Digital transformation has altered consumer behavior, necessitating the transformation of strategic frameworks for managing customer relationships and precise management of customer data. To implement effective marketing approaches, it is necessary to conduct a CRM system diagnostics that identifies critical customer interaction areas through adequate metrics. This paper examines the main aspects of marketing diagnostics and their contribution to customer relationship management. The research focuses on the nature and development of three main diagnostic tools as fundamental to the diagnostic process in customer relationship management: the RFM-method, the customer lifetime value (CLTV) and the Customer churn prediction model. The possibilities of using machine learning and artificial intelligence in marketing diagnostics and their impact on customer relationship management are discussed. The purpose of the present study is to contribute to a better understanding of the importance of marketing diagnostics for customer relationship management and to highlight the importance of the effective application of diagnostic tools in organizations.

Hybrid model for profit-driven churn prediction based on cost minimization and return maximization

Customer churn prediction is widely used to detect potential churners, which stimulates customer retention, and decrease churn loss. Most customer churn prediction models evaluate classifiers with a profit maximization indicator, which ignores the complex relationship between the cost and return of customer churn prediction. To fill this gap, a hybrid profit-driven churn prediction model is proposed that considers both return and cost. To evaluate category and continuous variables in the sample, synthetic minority over-sampling technique-nominal continuous is used to predict churners and non-churners. The feature selection based on a modified multi-objective atomic orbital search and extreme learning machine is used to obtain suitable variables for churn prediction with maximum return and minimum cost. Moreover, the modified multi-objective atomic orbital search optimizes the initial weights and thresholds of extreme learning machine to maximize profit from churn prediction. Based on two real-life datasets from bank and telecommunication service providers, experimental results show that the proposed hybrid model can achieve high-quality forecasting performance with higher profit, which can provide reliable references for operators and decision-makers.

Smart evaluation for deep learning model: churn prediction as a product case study

Customer churn prediction recently is one of the vital issues that confronts diverse business industries to sustain the customers base and profits. On the other hand, data scientists employ gigantic customer data to automate the data modelling process to offer these models as a generally portable service. This research has two main contributions: deep learning customer churn prediction model and smart evaluation prediction model service. So, this service harnesses any customer data to automate building, evaluation, and deployment of the churn prediction model. The research consists of three main parts. Firstly, it illustrates the dataset labelling which annotates customers data into churn or non-churn. Secondly, the deep learning churn prediction framework using convolutional neural network (CNN) algorithm. Finally, a case study is presented to show how churn prediction service is automatically trained and generated based on real customer data, where CNN parameters are adapted to achieve the most reliable performance in line with customers' behavior. The applied case study achieves accuracy 0.77, area under the curve (AUC) 0.84 and f1 score 0.83.

Exploiting time-varying RFM measures for customer churn prediction with deep neural networks

AbstractDeep neural network (DNN) architectures such as recurrent neural networks and transformers display outstanding performance in modeling sequential unstructured data. However, little is known about their merit to model customer churn with time-varying data. The paper provides a comprehensive evaluation of the ability of recurrent neural networks and transformers for customer churn prediction (CCP) using time-varying behavioral features in the form of recency, frequency, and monetary value (RFM). RFM variables are the backbone of CCP and, more generally, customer behavior forecasting. We examine alternative strategies for integrating time-varying and non-variant customer features in one network architecture. In this scope, we also assess hybrid approaches that incorporate the outputs of DNNs in conventional CCP models. Using a comprehensive panel data set from a large financial services company, we find recurrent neural networks to outperform transformer architectures when focusing on time-varying RFM features. This finding is confirmed when time-invariant customer features are included, independent of the specific form of feature integration. Finally, we find no statistical evidence that hybrid approaches (based on regularized logistic regression and extreme gradient boosting) improve predictive performance—highlighting that DNNs and especially recurrent neural networks are suitable standalone classifiers for CCP using time-varying RFM measures.

Deep Churn Prediction Method for Telecommunication Industry

Being able to predict the churn rate is the key to success for the telecommunication industry. It is also important for the telecommunication industry to obtain a high profit. Thus, the challenge is to predict the churn percentage of customers with higher accuracy without comprising the profit. In this study, various types of learning strategies are investigated to address this challenge and build a churn predication model. Ensemble learning techniques (Adaboost, random forest (RF), extreme randomized tree (ERT), xgboost (XGB), gradient boosting (GBM), and bagging and stacking), traditional classification techniques (logistic regression (LR), decision tree (DT), and k-nearest neighbor (kNN), and artificial neural network (ANN)), and the deep learning convolutional neural network (CNN) technique have been tested to select the best model for building a customer churn prediction model. The evaluation of the proposed models was conducted using two pubic datasets: Southeast Asian telecom industry, and American telecom market. On both of the datasets, CNN and ANN returned better results than the other techniques. The accuracy obtained on the first dataset using CNN was 99% and using ANN was 98%, and on the second dataset it was 98% and 99%, respectively.