Customer Churn Prediction Model Research Articles

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.

Investigating factors affecting customer churn in electronic banking and developing solutions for retention

Due to the competitive environment in electronic banking services, banks need to understand situations that lead to customer churn. Hence identification of factors affecting customer churn and developing programs to retain customer is important for banks. In this study, binomial logistic regression technique is used to identify factors affecting customer churn in electronic banking. Then affecting factors are employed in decision tree and artificial neural network methods to predict customer churn. Bagging technique is used to solve class imbalance problem and improves accuracy. The results show that the length of customer association, customer's age, customer's gender and the number of mobile banking transactions influence customer churn. Comparison of the prediction methods shows that performance of decision tree classifier is better than artificial neural network. Finally, based on the findings, some solutions are recommended to prevent customer from churning. In addition, some methods are suggested to build customer churn prediction model.

Why you should stop predicting customer churn and start using uplift models

Uplift modeling has received increasing interest in both the business analytics research community and the industry as an improved paradigm for predictive analytics for data-driven operational decision-making. The literature, however, does not provide conclusive empirical evidence that uplift modeling outperforms predictive modeling. Case studies that directly compare both approaches are lacking, and the performance of predictive models and uplift models as reported in various experimental studies cannot be compared indirectly since different evaluation measures are used to assess their performance.Therefore, in this paper, we introduce a novel evaluation metric called the maximum profit uplift (MPU) measure that allows assessing the performance in terms of the maximum potential profit that can be achieved by adopting an uplift model. This measure, developed for evaluating customer churn uplift models, extends the maximum profit measure for evaluating customer churn prediction models. While introducing the MPU measure, we describe the generally applicable liftup curve and liftup measure for evaluating uplift models as counterparts of the lift curve and lift measure that are broadly used to evaluate predictive models. These measures are subsequently applied to assess and compare the performance of customer churn prediction and uplift models in a case study that applies uplift modeling to customer retention in the financial industry. We observe that uplift models outperform predictive models and lead to improved profitability of retention campaigns.

A Hybrid Prediction Model for E-Commerce Customer Churn Based on Logistic Regression and Extreme Gradient Boosting Algorithm

A Hybrid Prediction Model for E-Commerce Customer Churn Based on Logistic Regression and Extreme Gradient Boosting Algorithm

Incorporating textual information in customer churn prediction models based on a convolutional neural network

This study investigates the value added by incorporating textual data into customer churn prediction (CCP) models. It extends the previous literature by benchmarking convolutional neural networks (CNNs) against current best practices for analyzing textual data in CCP, and, using real life data from a European financial services provider, validates a framework that explains how textual data can be incorporated in a predictive model. First, the results confirm previous research showing that the inclusion of textual data in a CCP model improves its predictive performance. Second, CNNs outperform current best practices for text mining in CCP. Third, textual data are an important source of data for CCP, but unstructured textual data alone cannot create churn prediction models that are competitive with models that use traditional structured data. A calculation of the additional profit obtained from a customer retention campaign through the inclusion of textual information can be used by practitioners directly to help them make more informed decisions on whether to invest in text mining.

Customer Churn Prediction Model Based on LSTM and CNN in Music Streaming

It is critical for companies to accurately predict customer churn if they want to achieve sustained development. Previous studies have used many machine learning methods to predict customer churn. The common models cannot make full use of time series features. To overcome this shortcoming, we propose a model based on LSTM and CNN which has cross-layer connections between the LSTM layers and the convolution layers. This model can simultaneously learn latent sequential information and capture important local features from time series features. Moreover, we introduce a method of constructing new features by training an XGBoost model on the existing features. Experimental results on the real-life dataset show that the model we proposed performs better than other comparison models.

Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context

<p><span>With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors’ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-Recency-Frequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macro-averaging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers’ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.</span></p>

A REVIEW ON CUSTOMER CHURN PREDICTION DATA MINING MODELING TECHNIQUES

Objectives: To find one of the best data mining techniques in telecommunication especially in customer churn prediction. Methods/Statistical Analysis: This paper presents a review of customer’s churn prediction in the telecommunication. The study shows a large number of attributes that are used to put into practice to develop customer churn prediction model by the large number of reviewer. These attributes are segmentation, account info, billing info, call dialup types, line-info, and payment info, and complain info, service provider info, and services info. In this study appropriate modeling techniques such as LR, NNM, DT, FL, CMC, SVM and DME are discussed for the churning purpose. Findings: The Review shows that to find customer churn prediction depends on the objectives of decision maker’s e.g. DT and SVM with a low ratio used, if interested in the true churn rate and false churn rate. The Logistic Regressions might be used if looking for the churn probability. DMEL modeling technique is impractical and ineffective for churn prediction on a large dataset with high dimension. Application/Improvements: The Technique proposed in this paper will overcome discussed issues and it will be applied on those customers who want to leave in near future and predict them based on some parameters. Keywords: Customer Prediction, Churn Prediction, Data Mining, Data Modeling

Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values

The goal of customer retention campaigns, by design, is to add value and enhance the operational efficiency of businesses. For organizations that strive to retain their customers in saturated, and sometimes fast moving, markets such as the telecommunication and banking industries, implementing customer churn prediction models that perform well and in accordance with the business goals is vital. The expected maximum profit (EMP) measure is tailored toward this problem by taking into account the costs and benefits of a retention campaign and estimating its worth for the organization. Unfortunately, the measure assumes fixed and equal customer lifetime value (CLV) for all customers, which has been shown to not correspond well with reality. In this article, we extend the EMP measure to take into account the variability in the lifetime values of customers, thereby basing it on individual characteristics. We demonstrate how to incorporate the heterogeneity of CLVs when CLVs are known, when their prior distribution is known, and when neither is known. By taking into account individual CLVs, our proposed approach of measuring model performance gives novel insights when deciding on a customer retention campaign. The method is dependent on the characteristics of the customer base as is compliant with modern business analytics and accommodates the data-driven culture that has manifested itself within organizations.

Just-in-time customer churn prediction in the telecommunication sector

Due to the exponential growth in technologies and a greater number of competitors in the telecom sector, the companies are facing a rigorous problem of customer churns. The customer churn is a phenomenon that highlights the customer’s intention who may switch from a certain service or even the service provider company. Many customer churn prediction (CCP) techniques are developed by academics and practitioners to handle the customer churn in order to resolve the problems pertaining to customer retention. However, CCP is not widely studied in the scenario where the company is not having enough historical data due to either been a newly established company or due to the recent start of a new technology or even because of the loss of the historical data. The just-in-time (JIT) approach can be a more practical alternative to address this issue as compared to state-of-the-art CCP techniques. Unfortunately, similar to traditional churn prediction models, JIT also requires enough historical data. To address this gap in the traditional CCP models, this study uses the cross-company data, i.e., data from another company, in the context of JIT for addressing CCP problems in the telecom sector. We empirically evaluated the performance of the proposed model using publicly available datasets of two telecom companies. It is found from the empirical evaluation that in the JIT-CCP context: (i) it is possible to evaluate the performance of the predictive model using cross-company dataset for training purposes and (ii) it is evident that heterogeneous ensemble-based JIT-CCP model is more suitable approach to use as compared to individual classifier or homogeneous ensemble-based technique.

Prediction model for telecom postpaid customer churn using Six-Sigma methodology

This paper elaborates the utilisation of Six-Sigma methodology in telecommunication sector. Every telecom industry is in the race of making the quantity of the customers before concentrating on the various issues related with the existing customer's results, the existing customers churn from the service provider. It is like a container having hole at the bottom. To identify the churn, a prediction model is developed using design for Six-Sigma which tells about the customers those are showing the symptoms to go out the network. For prediction, the model binary logistic regression analysis is used. The model is validated, and the accuracy of the model is analysed.

Prediction model for telecom postpaid customer churn using Six-Sigma methodology

Prediction model for telecom postpaid customer churn using Six-Sigma methodology

Developing a prediction model for customer churn from electronic banking services using data mining

Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. Banks are no exception to this rule. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Being based on existing information technologies which allow one to collect data from organizations’ databases, data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data. In this research, the decision tree technique was applied to build a model incorporating this knowledge. The results represent the characteristics of churned customers. Bank managers can identify churners in future using the results of decision tree. They should be provide some strategies for customers whose features are getting more likely to churner’s features.

Churn prediction via support vector classification: An empirical comparison

An empirical framework for customer churn prediction modeling is presented in this work. This task represents a very interesting business analytics challenge, given its highly class imbalanced nature, and the presence of noisy variables that adversely affect the prediction capabilities of classification models. In this work, two SVM-based techniques are compared: Support Vector Data Description (SVDD), and standard two-class SVMs. The proposed methodology involves the comparison of these two methods under different conditions of class imbalance and using different subsets of variables. Feature ranking is performed via the Fisher Score Criterion, while the class imbalance problem is dealt with through resampling techniques, namely random undersampling and SMOTE oversampling. Experiments on four customer churn prediction datasets show the advantages of SVDD: it outperforms standard SVM in terms of predictive performance, demonstrating the importance of techniques that take the class imbalance problem into account.

A Novel Approach for Providing the Customer Churn Prediction Model using Enhanced Boosted Trees Technique in Cloud Computing

Organizations earns huge amount of money by providing the different services to their customers. In today‟s world of competition, organizations need to focus on customer relationship management. Retaining the existing customers is as much important as attracting the new customers for an organization. For this purpose, organizations use data mining techniques for segmenting the churn customers and loyal customers so that special offers can be provided to churn customers to retain them as customers are the most valuable asset for organizations. The aim of this paper is to provide a customer churn prediction model using a standard CRISP-DM methodology based on RFM and Boosted Trees Technique. To enhance the performance of the technique, hybrid approach for building classifiers is used. There is also a comparison between the performances of both techniques. Results show that enhanced boosted trees technique performs better than existing boosted tree technique. Proposed approach is then implemented on the cloud environment to provide the cloud facilities for mining the data.

A Customer Churn Prediction Model in Telecom Industry Using Boosting

With the rapid growth of digital systems and associated information technologies, there is an emerging trend in the global economy to build digital customer relationship management (CRM) systems. This trend is more obvious in the telecommunications industry, where companies become increasingly digitalized. Customer churn prediction is a main feature of in modern telecomcommunication CRM systems. This research conducts a real-world study on customer churn prediction and proposes the use of boosting to enhance a customer churn prediction model. Unlike most research that uses boosting as a method to boost the accuracy of a given basis learner, this paper tries to separate customers into two clusters based on the weight assigned by the boosting algorithm. As a result, a higher risk customer cluster has been identified. Logistic regression is used in this research as a basis learner, and a churn prediction model is built on each cluster, respectively. The result is compared with a single logistic regression model. Experimental evaluation reveals that boosting also provides a good separation of churn data; thus, boosting is suggested for churn prediction analysis.

Social network analysis for customer churn prediction

This study examines the use of social network information for customer churn prediction. An alternative modeling approach using relational learning algorithms is developed to incorporate social network effects within a customer churn prediction setting, in order to handle large scale networks, a time dependent class label, and a skewed class distribution. An innovative approach to incorporate non-Markovian network effects within relational classifiers and a novel parallel modeling setup to combine a relational and non-relational classification model are introduced. The results of two real life case studies on large scale telco data sets are presented, containing both networked (call detail records) and non-networked (customer related) information about millions of subscribers. A significant impact of social network effects, including non-Markovian effects, on the performance of a customer churn prediction model is found, and the parallel model setup is shown to boost the profits generated by a retention campaign.

A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models

The interest for data mining techniques has increased tremendously during the past decades, and numerous classification techniques have been applied in a wide range of business applications. Hence, the need for adequate performance measures has become more important than ever. In this paper, a cost-benefit analysis framework is formalized in order to define performance measures which are aligned with the main objectives of the end users, i.e., profit maximization. A new performance measure is defined, the expected maximum profit criterion. This general framework is then applied to the customer churn problem with its particular cost-benefit structure. The advantage of this approach is that it assists companies with selecting the classifier which maximizes the profit. Moreover, it aids with the practical implementation in the sense that it provides guidance about the fraction of the customer base to be included in the retention campaign.