Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

Not just the best years of my life: personal growth in higher education

Our conception of product affirmation depicts a product as “sculptor” of the consumer’s ideal self, similar to how a relationship partner can help us achieve our aspirations and goals. We performed two studies to look at the role of higher education as a product in affirming a consumer’s ideal self. We found that product affirmation for undergraduate students and alumni (with the university as the product that affirms the ideal self of the student/alumnus) leads to increases in the experience of various positive emotions, the acquisition of various positive traits, and positive evaluations of the university. Additionally, we found that product affirmation effects were more pronounced and robust in one’s personal ideal-self domain than in one’s professional ideal-self domain. Practical implications, study limitations, and future directions are discussed, as well as preliminary findings from a follow-up experiment using a sample of graduate students

A bi-level decision model for customer churn analysis

This paper develops a bi-level decision model and a solution approach to optimizing service features for a company to reduce its customer churn rate. First, a bi-level decision model, together with its modeling approach, are developed to describe the gaming relationship between decision makers in a company (service provider) and its customers. Then, a practical solution approach to reaching solutions for the bi-level-modeled customer churn problem is developed. Finally, experiments and case studies are conducted to illustrate the bi-level decision model and the solution approach. © 2013 Wiley Periodicals, Inc

Churn prediction using customers' implicit behavioral patterns and deep learning

The processes of market globalization are rapidly changing the competitive conditions of the business and financial sectors. With the emergence of new competitors and increasing investments in the banking services, an environment of closer customer relationships is the demand of today’s economics. In such a scenario, the concept of customer’s willingness to change the service provider – i.e. churn, has become a competitive domain for organizations to work on. In the banking sector, the task to retain the valuable customers has forced management to preemptively work on customers data and devise strategies to engage the customers and thereby reducing the churn rate. Valuable information can be extracted and implicit behavior patterns can be derived from the customers’ transaction and demographic data. Our prediction model, which is jointly using the time and location based sequence features has shown significant improvement in the customer churn prediction. Various supervised models had been developed in the past to predict churning customers; our model is using the features which are derived jointly from location and time stamped data. These sequenced based feature vectors are then used in the neural network for the churn prediction. In this study, we have found that time sequenced data used in a recurrent neural network based Long Short Term Memory (LSTM) model can predict with better precision and recall values when compared with baseline model. The feature vector output of our LSTM model combined with other demographic and computed behavioral features of customers gave better prediction results. We have also iv proposed and developed a model to find out whether connection between the customers can assist in the churn prediction using Graph convolutional networks (GCN); which incorporate customer network connections defined over three dimension

Who Renews? Who Leaves? Identifying Customer Churn in a Telecom Company Using Big Data Techniques

Within the context of the telecom industry, this teaching case is an active learning analytics exercise to help students build hands-on expertise on how to utilize Big Data to solve a business problem. Particularly, the case utilizes an analytics method to help develop a customer retention strategy to mitigate against an increasing customer churn problem in a telecom company. Traditionally, the forecast of customer churn uses various demographic and cell phone usage data. Big Data techniques permit a much finer granularity in the prediction of churn by analyzing specific activities a customer undertakes before churning. The authors help students to understand how data from customer interactions with the company through multiple channels can be combined to create a “session.” Subsequently, the authors demonstrate the use of effective visualization to identify the most relevant paths to customer churn. The Teradata Aster Big Data platform is used in developing this case study

The effect of friends’ churn on consumer behavior in mobile networks

We study how consumers decide which tariff plan to choose and whether to churn when their friends churn in the mobile industry. We develop a theoretical model showing conditions under which users remain with their carrier and conditions under which they churn when their friends do. We then use a large and rich anonymized longitudinal panel of call detailed records to characterize the consumers’ path to death with unprecedented level of detail. We explore the structure of the network inferred from these data to derive instruments for friends’ churn, which is typically endogenous in network settings. This allows us to econometrically identify the effect of peer influence in our setting. On average, we find that each additional friend that churns increases the monthly churn rate by 0.06 percent. The observed monthly churn rate across our dataset is 2.15 percent. We also find that firms introducing the pre-paid tariff plans that charge the same price to call users inside and outside the carrier help retain consumers that would otherwise churn. In our setting, without this tariff plan the monthly churn rate could have been as high as 8.09 percent. We perform a number of robustness checks, in particular to how we define friends in the social graph, and show that our results remain unchanged. Our paper shows that the traditional definition of customer lifetime value underestimates the value of consumers and, in particular, that of consumers with more friends due to the effect of contagious churn and, therefore, managers should actively take into account the structure of the social network when prioritizing whom to target during retention campaigns.info:eu-repo/semantics/acceptedVersio