Churn Dataset Research Articles

Application of GWO-attention-ConvLSTM model in customer churn prediction and satisfaction analysis in customer relationship management

Customer Relationship Management (CRM) is vital in modern business, aiding in the management and analysis of customer interactions. However, existing methods struggle to capture the dynamic and complex nature of customer relationships, as traditional approaches fail to leverage time series data effectively. To address this, we propose a novel GWO-attention-ConvLSTM model, which offers more effective prediction of customer churn and analysis of customer satisfaction. This model utilizes an attention mechanism to focus on key information and integrates a ConvLSTM layer to capture spatiotemporal features, effectively modeling complex temporal patterns in customer data. We validate our proposed model on multiple real-world datasets, including the BigML Telco Churn dataset, IBM Telco dataset, Cell2Cell dataset, and Orange Telecom dataset. Experimental results demonstrate significant performance improvements of our model compared to existing baseline models across these datasets. For instance, on the BigML Telco Churn dataset, our model achieves an accuracy of 95.17%, a recall of 93.66%, an F1 score of 92.89%, and an AUC of 95.00%. Similar results are validated on other datasets. In conclusion, our proposed GWO-attention-ConvLSTM model makes significant advancements in the CRM domain, providing powerful tools for predicting customer churn and analyzing customer satisfaction. By addressing the limitations of existing methods and leveraging the capabilities of deep learning, attention mechanisms, and optimization algorithms, our model paves the way for improving customer relationship management practices and driving business success.

Customer churn data analysis using data mining

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].

Bank Customer Churn Prediction Using SMOTE: A Comparative Analysis

In today's market, customers have a plethora of options available to them when deciding where to invest their money. Consequently, customer churn and engagement have emerged as prominent concerns. With an increasing number of service providers targeting the same customer base, it is imperative for providers to understand evolving customer behavior and heightened expectations to retain their clientele. Numerous studies have addressed the issue of customer churn, with data mining frequently employed to predict bank customer attrition. While many researchers have proposed various approaches for predicting customer churn, some machine learning (ML) algorithms have struggled to deliver the required performance in identifying customer churn accurately most especially when the dataset is imbalance data. Therefore, this paper presents an application of Synthetic Minority Over Sampling Technique (SMOTE) on bank churn dataset. The SMOTE algorithm was employed to address the problem of data imbalance and Genetic Algorithm (GA) was applied to select most informative features from the original dataset. The selective features were evaluate using four (4) different classification algorithms: Random Forest (RF), K-Nearnest Neighbor (KNN), Artificial Neural Network (ANN) and Adaboost algorithms. The KNN model demonstrated superior performance compared to other models in terms of accuracy (96%), precision (96%), and F-measure (96%) respectively. Furthermore, we compared our results with existing models that utilized the same dataset, and our proposed strategy outperformed them.

Performance Analysis of Oversampling and Undersampling on Telco Churn Data Using Naive Bayes, SVM And Random Forest Methods

The telecommunications industry is a rapidly growing economic sector in today’s digital era. However, intense competition causes challenges in retaining customers. One of the problems faced by companies is the increase in customer switching, known as customer churn, which is when customers stop using the company’s services. To solve this problem, telecommunication companies use data mining techniques to predict potential customer churn. In the churn prediction process, data mining identifies patterns of customer behavior that indicate potential churn in the future. The dataset used in this research comes from the Telco Churn dataset which can be accessed through the Kaggle platform. This research implements oversampling and undersampling techniques to improve prediction accuracy on minority data in data mining algorithm models such as Random Forest, Naive Bayes, and SVM. The results of this study show that the application of oversampling and undersampling techniques is effective in increasing the Specificity (True Negative Rate) value of the algorithm model used. This Specificity increase is observed in various algorithms such as Random Forest which experienced an increase in Specificity value up to 45.98%, Naive Bayes up to 14.97%, and SVM up to 65.41%.

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.

A Novel Telecom Customer Churn Analysis System Based on RFM Model and Feature Importance Ranking

Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets and reduce capital outflows. Future Research: The research is also applicable to other fields, such as education, banking, and so forth. We will make more new attempts based on this system.

A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.

Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifier

A Study of Artificial Neural Network and Its Implementation from Scratch

One of the major problems in the field of artificial intelligence (AI) is the use of machine learning model as a black box, even though it might be helpful in a few cases but understanding the internal structure and the operating mechanism will assist the user to tweak the variables in a more efficient and productive manner. In this paper we have introduced the working of an artificial neural network (ANN) by taking the example of a three layered neural network. The entire mathematics behind the working of neural network along with the different evaluation metrics required to assess the performance of the model are discussed in this paper. We have also created a custom neural network from scratch and compared it with the keras based model on three different datasets - susy dataset [8], cardiovascular dataset [6], churn dataset [7]. The results obtained demonstrates that the overall performance of both the models are almost identical which gives an idea that it is possible to train a neural network from scratch without the use of any framework.

A grid search optimized extreme learning machine approach for customer churn prediction

Customers' behaviors such as tendencies, loyalty status, satisfaction criteria show an alteration day by day due to the changing world. So, these behavior changes should be analyzed very well in every step of the decision-making process. Customer churn analysis is the determination of customers who tend to leave by analyzing the customer data with various methods before this situation occurs. This study aims to develop an Extreme Learning Machine based model for customer churn prediction problem and to determine the model parameters that provide the best performance. Grid search is used for hyperparameter tuning. Also modified accuracy calculation approach has been presented. The churn data set obtained from the UCI Machine Learning Repository has been used. Naive Bayes, k-Nearest Neighbor and Support Vector Machine methods are selected for performance comparison of the model. With a value of 93.1%, the best accuracy measure has been obtained with Extreme Learning Machine. Due to the low number of parameters to be determined and performance evaluation measures that compete with other models’ results, it can be said that the Extreme Learning Machine is highly effective and interesting in the solution of the problem.

Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees

The goal of the paper is to present the framework for combining clustering and classification for churn management in telecommunications. Considering the value of market segmentation, we propose a three-stage approach to explain and predict the churn in telecommunications separately for different market segments using cluster analysis and decision trees. In the first stage, a case study churn dataset is prepared for the analysis, consisting of demographics, usage of telecom services, contracts and billing, monetary value, and churn. In the second stage, k-means cluster analysis is used to identify market segments for which chi-square analysis is applied to detect the clusters with the highest churn ratio. In the third stage, the chi-squared automatic interaction detector (CHAID) decision tree algorithm is used to develop classification models to identify churn determinants at the clusters with the highest churn level. The contribution of this paper resides in the development of the structured approach to churn management using clustering and classification, which was tested on the churn dataset with a rich variable structure. The proposed approach is continuous since the results of market segmentation and rules for churn prediction can be fed back to the customer database to improve the efficacy of churn management.

Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

Optimized Deep Neural Network Based Predictive Model for Customer Attrition Analysis in the Banking Sector

Background: In recent time with the growth of the technology and the business model, customer attrition analysis is considered as a very important metric which decides the revenues and profitability of the organization. It is applicable for all the business domains irrespective of the size of the business even including the start-ups. Because about 65% revenue for the organization comes from the existing customer. The goal of the customer attrition analysis is to predict the customer who is likely to exit or churn from the current business organization. In this research work, the literature review is carried out to explore the related work which has been already carried out in the field of customer attrition analysis. The literature review also focuses on some of the patents which are issued in the area of customer attrition or churn analysis. The goal of the research paper is to predict accurately the customer attrition rate in the Banking Sector. Objective: The main objective of this paper is to predict accurately the attrition rate in the Banking sector using an optimized deep feed-forward neural network. Methods: In the proposed work the predictive machine learning model is implemented using the optimized deep feed-forward neural network having five hidden layers in it. The model is trained using Adam optimizer algorithm to obtain the optimal accuracy. The Banking Churn data set is passed as input to the Optimized Deep Feed Forward Neural Network Model. In order to perform the comparative analysis, the same data set is passed as input to the other machine learning algorithm such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, and Artificial Neural Network. Results: The test results indicate that the proposed optimized deep feedforward neural Network model performed better in accuracy compared to existing machine learning techniques. Conclusion: The proposed optimized deep neural network model is an accurate model for customer attrition analysis in the Banking sector compared to the existing machine learning techniques.

Improving Customer Churn Classification with Ensemble Stacking Method

Due to the high cost of acquiring new customers, accurate customer churn classification is critical in any company. The telecommunications industry has employed single classifiers to classify customer churn; however, the classification accuracy remains low. Nevertheless, combining several classifiers' decisions improves classification accuracy. This article attempts to enhance ensemble integration via stack generalisation. This paper proposed a stacking ensemble based on six different learning algorithms as the base-classifiers and tested on five different meta-model classifiers. We compared the performance of the proposed stacking ensemble model with single classifiers, bagging and boosting ensemble. The performances of the models were evaluated with accuracy, precision, recall and ROC criteria. The findings of the experiments demonstrated that the proposed stacking ensemble model resulted in the improvement of the customer churn classification. Based on the results of the experiments, it indicates that the prediction accuracy, precision, recall and ROC of the proposed stacking ensemble with MLP meta-model outperformed other single classifiers and ensemble methods for the customer churn dataset.

Applying Reinforcement Learning for Customer Churn Prediction

Customer churn prediction is one of the biggest challenges for companies nowadays. Since the loss of customers directly affects the organization’s reputation, financial and growth plans. Customer behaviours may change due to any uncontrollable factors or unexpected circumstances, resulting in changing patterns of data. This may aggravate the prediction performance of the classifiers generated from supervised learning technique considered as Passive learning. This paper has thus proposed applying the technique of reinforcement learning for customer churn prediction. The model of Deep Q Network (DQN) is implemented and adapted for learning on the selected customer churn dataset used for classification tasks. We simulate a different distribution dataset with shuffle sampling to embrace pattern changes. The performance of the selected classifiers, compared to DQN, has been evaluated with four measures: accuracy, precision, recall, and F1. The results showed that as Active learner, DQN outperformed the selected classifiers, XGBoost, Random forest, and kNN, when data have been enlarged and evolving with emerging new patterns.

Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry

Combining multiple classifiers to create hybrid learners (ensembles) has gained popularity in recent years. Ensembles are gaining more interest in the field of data mining as they have reportedly performed best predictions as compared to individual classifiers. This has resulted in experimentation with new ways of ensemble creation. This paper presents a study on creation of novel hybrid ways of combining multiple ensemble models using ‘over production and choose approach.’ In contrast to the original concept of ensembles that combine various learners, the proposed ensemble models comprise of combinations of other ensembles. In particular, we have combined learners as in composition of other learners, thus producing nested learners. Two such models named as Boosted-Stacked learners and Bagged-Stacked learners are proposed and are shown to outperform the traditional ensembles. Experiments are performed in churn prediction domain where a benchmark customer churn dataset (available on UCI repository) and a newly created dataset from a South Asian wireless telecom operator (named as SATO) are used. SATO dataset is created as balanced dataset (having equal number of churners and non-churners). The novel Boosted-Stacked learner and Bagged-Stacked learner achieved accuracies of 98.4% and 97.2%, respectively, on the UCI Churn dataset outperforming the existing state-of-the-art techniques. Furthermore, a high accuracy on the SATO dataset validates the effectiveness of the proposed models on balanced as well as imbalanced datasets.

K- local maximum margin feature extraction algorithm for churn prediction in telecom

Telecom customer churn data is not publicly available because involving users' personal privacy. In 2009, the French telecommunications company Orange for knowledge discovery and data mining (KDD) competition provides a telecom customer churn data set KDD Cup 09. In order to solve the high dimensional problem of KDD Cup 09, a new feature reduction method is used to explore the influence of different features on the prediction of classification model. In this paper, a new K- local maximum margin feature extraction algorithm (KLMM) is proposed. Through researching on the diversification subspace partition rules, the corresponding potential field structure is constructed. According to the data source in the dimension of scalability, the intrinsic link between data attributes and classification results is revealed. The extracted features can reduce the dimension of the churn prediction in telecom data. The KLMM method adapts auto selection sigma factor to reflect the anisotropy of features. The potential function is used to assess the weights of attributes and find the potential important weight. Experiments and analysis show that the extracted features by KLMM are more likely to find a classification hyperplane which can separate data points of the different classes.

Semi-Supervised Churn Clustering for Fault and Constraints Prediction in Telecom Industry

Objective: Churn prediction process on telecom industry is performed using background knowledge such as user information and application oriented constraints with the aim of clustering churn items of telecom communication users. Methods: This paper proposes Semi-supervised Constraint based Churn Clustering (SCCC) method. Semi-supervised learning method identifies different types of churns for labelled data items in telecom industry. PCK Mean based Clusteringwith Expectation Maximization (EM) algorithm finds cluster labels and distance metric for easy grouping of faults set. Similar types of issues are mapped with fast traversal procedure. Constraint based Cluster Membership achieves effective churns clustering by considering relative pairing of users. Findings: The proposed Semi-supervised Constraint based Churn Clustering (SCCC) method is implemented by using JAVA language. This JAVA language uses the code to effectively identify the churns in telecom industry. The SCCC method uses a churn dataset for conducting the experimental work.Experiment is conducted on the factors such as execution time, accuracy, clustering efficiency and support rate on predicting the churns in telecom industry. Experimental results show that the proposed Semi-supervised Constraint based Churn Clustering (SCCC) method out performs than the existing methods. Improvement: Proposed method is able to minimize the execution time, improve accuracy and clustering efficiency and also increase the support rate on predicting churns in telecom industry.Keywords: Cluster Membership, Churn Prediction, Expectation Maximization, Semi-Supervised Clustering

A Study on Efficiency of Decision Tree and Multi Layer Perceptron to Predict the Customer Churn in Telecommunication using WEKA

Data mining is the technique to discover the knowledge which is hidden in the large data sets. It involves with different methods and algorithms to perform efficient analysis over the data sets. The classification is the technique, which is used to mine the data and helps to make the prediction about the future. Different data mining algorithms are available for classification, like C4.5, Simple Cart, NavieBayesen, Logistic Regression and Multi Layer Perceptron based on Artificial Neural Network. The main objective of this paper is to analyse the efficiency of various classification algorithms in terms of performance, accuracy and time complexity. Telecommunication churn dataset is used for the analysis. The obtained results revealed that MLP algorithm outperformed in terms of accuracy and C4.5 algorithm provides better performance in terms of time complexity. Keywordsmining, Classification algorithm, Decision tree,

A novel cost-sensitive framework for customer churn predictive modeling

Customer churn predictive modeling deals with predicting the probability of a customer defecting using historical, behavioral and socio-economical information. This tool is of great benefit to subscription based companies allowing them to maximize the results of retention campaigns. The problem of churn predictive modeling has been widely studied by the data mining and machine learning communities. It is usually tackled by using classification algorithms in order to learn the different patterns of both the churners and non-churners. Nevertheless, current state-of-the-art classification algorithms are not well aligned with commercial goals, in the sense that, the models miss to include the real financial costs and benefits during the training and evaluation phases. In the case of churn, evaluating a model based on a traditional measure such as accuracy or predictive power, does not yield to the best results when measured by the actual financial cost, ie. investment per subscriber on a loyalty campaign and the financial impact of failing to detect a real churner versus wrongly predicting a non-churner as a churner. In this paper, we present a new cost-sensitive framework for customer churn predictive modeling. First we propose a new financial based measure for evaluating the effectiveness of a churn campaign taking into account the available portfolio of offers, their individual financial cost and probability of offer acceptance depending on the customer profile. Then, using a real-world churn dataset we compare different cost-insensitive and cost-sensitive classification algorithms and measure their effectiveness based on their predictive power and also the cost optimization. The results show that using a cost-sensitive approach yields to an increase in cost savings of up to 26.4 %.