Customer Churn Prediction Model Research Articles

Customer Churn Prediction Using Machine Learning Techniques: the case of Lion Insurance

The growth of an insurance company is measured by the number of policies purchased by customers. To keep the company growing and having more customers, the customer churn prediction model is crucial to maintain its competitiveness. Even if the company has good service delivery, it is important to identify the customer’s behavior and be able to predict the future churners. The main contribution to our work is the development of a predictive model that can proactively predict the customer who will leave the insurance company. The model developed in this study uses machine learning techniques on lion insurance data. Another main contribution of this study is the labeling of the data using an unsupervised algorithm on 12007 rows with 9 features from which 2 clusters were generated using the K-means++ algorithm. As the cluster results found are imbalanced, the synthetic minority oversampling technique was applied to the training dataset. The Deep Neural Network algorithm turns out to be a very effective model for predicting customer churn, reaching an accuracy of 98.81%. The two years of customer data were obtained from lion insurance and used to train test, and evaluate the model. The Randomized optimization technique was selected for each algorithm. However, the best results were obtained by a deep neural network with a structure of (9-55-55-55-55-55-1). This algorithm was selected for classification in this churn prediction study

Research on Telecom Customer Churn Prediction Based on GA-XGBoost and SHAP

To address the prominent problems faced by customer churn in telecom enterprise management, a telecom customer churn prediction model integrating GA-XGBoost and SHAP is proposed. By using the ADASYN algorithm for data processing on the unbalanced sample set; based on the GA-XGBoost model, the XGBoost algorithm is used to construct the telecom customer churn prediction model, and the hyperparameters of the model are optimized by using the genetic algorithm. The experimental results show that compared with traditional machine learning methods such as GBDT, decision tree, KNN and single XGBoost model, the improved XGBoost model has better performance in recall, F1 value and AUC value; the GA-XGBoost model is integrated with SHAP framework to analyze and explain the important features affecting telecom customer churn, which is more in line with the telecom industry to predict customer the actual situation of churn.

Customer Churn Prediction Model for Telecommunication Industry

Customer Churn Prediction Model for Telecommunication Industry

Research on Improved Xgboost Algorithm for Big Data Analysis of e-Commerce Customer Churn

With the increasing cost of acquiring new users for e-commerce enterprises, it has become an important task for e-commerce enterprises to actively carry out customer churn management. Therefore, based on the distributed gradient enhancement library algorithm (XGBoost), this research proposes a big data analysis study on e-commerce customer churn. First, it conducts an evaluation analysis on e-commerce customer segmentation and combines the random forest algorithm (RF) to build an RF XGBoost prediction model based on customer churn. Finally, it verifies the performance of the prediction model. The results show that the area under receiver operating characteristic curve (AUC) value, prediction accuracy, recall rate, and F1 value of the RF-XGBoost model are significantly better than those of the RF, XGBoost, and ID3 decision trees to build an e-commerce customer churn prediction model; The average output error of RF-XGBoost model is 0.42, and the average output error is relatively good, indicating that the model proposed in this study has a smaller error and higher accuracy. It can make a general assessment of the customer churn of e-commerce enterprises, and then provide data support for the customer maintenance work of e-commerce enterprises. It is helpful to analyze the relevant factors affecting customer churn, to Equationte targeted customer service programs, thus improving the economic benefits of e-commerce enterprises.

Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry

Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry

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

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

How training on multiple time slices improves performance in churn prediction

Customer churn prediction models using machine learning classification have been developed predominantly by training and testing on one time slice of data. We train models on multiple time slices of data and refer to this approach as multi-slicing. Our results show that given the same time frame of data, multi-slicing significantly improves churn prediction performance compared to training on the entire data set as one time slice. We demonstrate that besides an increased training set size, the improvement is driven by training on samples from different time slices. For data from a convenience wholesaler, we show that multi-slicing addresses the rarity of churn samples and the risk of overfitting to the distinctive situation in a single training time slice. Multi-slicing makes a model more generalizable, which is particularly relevant whenever conditions change or fluctuate over time. We also discuss how to choose the number of time slices.

Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

Customer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

An early warning model for customer churn prediction in telecommunication sector based on improved bat algorithm to optimize ELM

The competition in the telecommunications industry is mainly reflected in the competition for the number of key customers. Therefore, how to effectively prevent the loss of large customers is a problem that many telecommunications companies are very concerned about. This paper establishes a customer churn prediction model based on the improved bat algorithm (IBA) optimized extreme learning machine (ELM), with the purpose of discovering potential churn customers in time and taking measures to retain them in advance. This paper uses the IBA to optimize the initial random weights of the ELM, so as to improve the prediction accuracy of the ELM. To overcome the shortcomings of the bat algorithm (BA) that the convergence speed is too slow in the early stage and difficult to converge in the later stage, this paper introduces the inertia weight into the speed update formula. To improve the population diversity and local search ability of the BA, this paper adds a chaotic local search method to balance the global search ability of the BA in the early stage. To control the search range of the BAs in the later stage, the lane flight formula is introduced into the position update formula to speed up the convergence speed of the algorithm. Then according to the result of function optimization, it can be seen that IBA has a significant improvement in convergence speed and accuracy. Finally, IBA was used to optimize ELM, and a customer churn prediction model was established. The empirical results show that the predictive model proposed in this paper can effectively identify lost customers and lay a foundation for improving the competitiveness of the telecommunications industry in the future.

Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling

An important business domain that relies heavily on advanced statistical- and machine learning algorithms to support operational decision-making is customer retention management. Customer churn prediction is a crucial tool to support customer retention. It allows an early identification of customers who are at risk to abandon the company and provides the ability to gain insights into why customers are at risk. Hence, customer churn prediction models should complement predictive performance with model insights. Inspired by their ability to reconcile strong predictive performance and interpretability, this study introduces rule ensembles and their extension, spline-rule ensembles, as a promising family of classification algorithms to the customer churn prediction domain. Spline-rule ensembles combine the flexibility of a tree-based ensemble classifier with the simplicity of regression analysis. They do, however, neglect the relatedness between potentially conflicting model components which can introduce unnecessary complexity in the models and compromises model interpretability. To tackle this issue, a novel algorithmic extension, spline-rule ensembles with sparse group lasso regularization (SRE-SGL) is proposed to enhance interpretability through structured regularization. Experiments on fourteen real-world customer churn data sets in different industries (i) demonstrate the superior predictive performance of spline-rule ensembles with sparse group lasso over a set well yet powerful benchmark methods in terms of AUC and top decile lift; (ii) show that spline-rule ensembles with sparse group lasso regularization significantly outperform conventional rule ensembles whilst performing at least as well as conventional spline-rule ensembles; and (iii) illustrate the interpretable nature of a spline-rule ensemble model and the advantage of structured regularization in SRE-SGL by means of a case study on customer churn prediction for a telecommunications company.

Customer churn prediction in telecommunication industry through machine learning based Fine-tuned XGBoost algorithm

Telecom companies require the accurate prediction of probable churn customers to improvise the customer relationship management; this is addressed through customer churn prediction model. Customer churn is one of the major issue faced by telecommunication companies due to various competitors and this further cause’s loss of revenue. Moreover, the customer churn prediction model helps in predicting the potential churners; hence plenty of research has been carried out in past and few of them succeeded through machine learning approach. However data imbalance remains the major issue, hence in this research work we design and develop Fine-tuned XGBoost model which addresses the issue of imbalance dataset through introducing the feature function; further, it also tackles with data sparsity and overfitting problem. Finetuned XGBoost is evaluated through comparing the machine-learning algorithm in terms of performance metrics like precision, recall and accuracy and fine-tuned XGBoost outperforms the existing model.

生存分析在客户流失预测模型中的应用

生存分析在客户流失预测模型中的应用

Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry

Customer churn prediction (CCP) is a crucial problem in telecom industry which helps to improve the revenue of the company and prevent the loss of customers. Customer churn is an important issue in service sector with highly competitive services. At the same time, the prediction of users who are probably leaving the company can be identified at an earlier stage to prevent loss of revenue. Several works have used machine learning (ML) techniques for predicting the existence of customer churn in different industries. With this motivation, this paper presents an optimal long, short-term memory with stacked autoencoder (OLSTM-SAE) technique for CCP in telecom industry. The OLSTM-SAE technique encompasses three subprocesses namely preprocessing, classification, and parameter optimization. The OLSTM-SAE technique classifies the preprocessed data into churn and non-churn customers. In addition, the grey wolf optimization (GWO) technique is used to adjust the variables involved in the LSTM-SAE model. For examining the enhanced performance of the OLSTM-SAE technique, an extensive simulation analysis takes place, and the outcomes are inspected with respect to various measures. The experimental results highlighted the betterment of the OLSTM-SAE technique in terms of different evaluation parameters.

Leveraging unstructured call log data for customer churn prediction

Customer retention is important in the financial services industry. Machine learning has been incorporated into customer data analytics to predict client churn risks. Despite its success, existing approaches primarily use only structured data, e.g., demographics and account history. Data mining with unstructured data, e.g., customer interaction, can reveal more insights, which has not been adequately leveraged. In this research, we propose a customer churn prediction model utilizing the unstructured data, which is the spoken contents in phone communication. We collected a large-scale call center dataset with two million calls from more than two hundred thousand customers and conducted extensive experiments. The results show that our model can accurately predict the client churn risks and generate meaningful insights using interpretable machine learning with personality traits and customer segments. We discuss how these insights can help managers develop retention strategies customized for different customer segments.

Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes

The customer churn prediction is an important business strategy for the company. The ease of switching operators is one of the serious challenges that must be faced by the telecommunications industry. To get new customers requires a much higher cost than maintaining existing customers. Customer churn refers to the periodic loss of customers in an organization. To retain existing customers, organizations must improve customer service, improve product quality, and must be able to know in advance which customers have the possibility of leaving the organization. By predicting customer churn, companies can immediately take action to retain customers. Prediction can be done by analysing customer data using data mining techniques. This study proposes to implement feature selection to select relevant features and can provide improved performance in customer churn prediction models. Some proposed feature selection techniques are Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Forward Floating Selection (SBFS), Sequential Backward Floating Selection (SBFS). The classification algorithm used to classify is Naive Bayes. The model that provides the best performance value is the model that implements Sequential Backward Selection (SBS) and Sequential Backward Floating Selection (SBFS) feature selection technique with feature number 19.

Projection pursuit random forest using discriminant feature analysis model for churners prediction in telecom industry

A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model.

An Effective Customer Churn Prediction Model using Adaptive Gain with Back Propagation Neural Network in Cloud Computing Environment

An Effective Customer Churn Prediction Model using Adaptive Gain with Back Propagation Neural Network in Cloud Computing Environment

Misclassified Reduced Instance and Stochastic Gradient Descent with Logistic Regression Model for Customer Churn Prediction

Customer Churn Prediction (CCP) is a difficult problem found to be helpful to make decisions due to the rapid growth in the number of telecom providers. At present, deep learning models are familiar because of the significant improvement in different areas. In this paper, a deep learning based CCP is introduced by the use of Stochastic Gradient Boosting (SGD) with Logistic regression (LR) classifier model. By the integration of SGD and LR, effective classification can be accomplished. To further improve the classifier efficiency, misclassified instances are removed from the dataset. Then, the processed data is again provided as input to the classification model. The presented SGD-LR model is validated on a benchmark dataset and the results are examiner with respect to different measures. The experimental outcome pointed out the projected model is superior to available CCP models on the identical dataset.

Identifying Potential Churners Through Predictive Analysis: Evaluation using Pro-Active-Attrition Management Logistic Regression

Telecom industries are sensitive to customer churn prediction management system, which is popularly known as predictive analytics. Digital network and linked IT have severs that need customer churn prediction models. It is a great challenge for them to deal with a massive amount of data generated across fields. This research is a comprehensive study to develop a 'customer churn prediction model' by identifying potential churners using predictive analytics, concerning critical evaluation through pro-active-attrition management logistic regression. Such model and research work can help to identify customers, that are more likely to churn, save from fraudulent activities, inactive, or non-payment subscribers. The dataset is a scaled-down version of the full database generously donated by an anonymous wireless telephone company. There are 71,047 customers in the database and 75 potential predictors (total 78 variables) that combine the calibration and validation. Results show that the reason behind the customer churn is customer attrition.

Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content

Customer churn is a problem for most companies because it affects the revenues of the company when a customer switch from a service provider company to another in the telecom sector. For solving this problem we put two main approaches: the first one is identifying the main factors that affect customers churn, the second one is detecting the customers that have a high probability to churn through analyzing social media. For the first approach we build a dataset through practical questionnaires and analyzing them by using machine learning algorithms like Deep Learning, Logistic Regression, and Naive Bayes algorithms. The second approach is customer churn prediction model through analyzing their opinions through their user-generated content (UGC) like comments, posts, messages, and products or services' reviews. For analyzing the UGC we used Sentiment analysis for finding the text polarity (negative/positive). The results show that the used algorithms had the same accuracy but differ in arrangement of attributes according to their weights in the decision.