479 research outputs found

    Customer Churn Prediction in Telecom Sector: A Survey and way a head

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    © 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe

    Bagging and boosting classification trees to predict churn.

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    In this paper, bagging and boosting techniques are proposed as performing tools for churn prediction. These methods consist of sequentially applying a classification algorithm to resampled or reweigthed versions of the data set. We apply these algorithms on a customer database of an anonymous U.S. wireless telecom company. Bagging is easy to put in practice and, as well as boosting, leads to a significant increase of the classification performance when applied to the customer database. Furthermore, we compare bagged and boosted classifiers computed, respectively, from a balanced versus a proportional sample to predict a rare event (here, churn), and propose a simple correction method for classifiers constructed from balanced training samples.Algorithms; Bagging; Boosting; Churn; Classification; Classifiers; Companies; Data; Gini coefficient; Methods; Performance; Rare events; Sampling; Top decile; Training;

    Research trends in customer churn prediction: A data mining approach

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    This study aims to present a very recent literature review on customer churn prediction based on 40 relevant articles published between 2010 and June 2020. For searching the literature, the 40 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to six main dimensions: Reference; Areas of Research; Main Goal; Dataset; Techniques; outcomes. The research has proven that the most widely used data mining techniques are decision tree (DT), support vector machines (SVM) and Logistic Regression (LR). The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. Therefore, the telecom company can effectively predict the churn of customers, and then avoid customer churn by taking measures such as reducing monthly fixed fees. The present literature review offers recent insights on customer churn prediction scientific literature, revealing research gaps, providing evidences on current trends and helping to understand how to develop accurate and efficient Marketing strategies. The most important finding is that artificial intelligence techniques are are obviously becoming more used in recent years for telecom customer churn prediction. Especially, artificial NN are outstandingly recognized as a competent prediction method. This is a relevant topic for journals related to other social sciences, such as Banking, and also telecom data make up an outstanding source for developing novel prediction modeling techniques. Thus, this study can lead to recommendations for future customer churn prediction improvement, in addition to providing an overview of current research trends.info:eu-repo/semantics/acceptedVersio

    Customer Churn Prediction

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    Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the customer churn. The overall accuracy of the model, Receiver Operating Characteristic curve and Area Under the Receiver Operating Characteristic Curve is used as the evaluation metrics for this research to identify the best classifier. A comparative study on the most popular supervised machine learning methods – Logistic Regression, Random Forest, Support Vector Machine (SVM) and Neural Network were applied to customer churning prediction in a CU context. In the first phase of our experiments, the various feature selection techniques were studied. In the second phase of our study, all models were applied on the imbalance dataset and results were evaluated. SMOTE technique is used to balance the data and then the same models were applied on the balanced dataset and results were evaluated and compared. The best over-all classifier was Random Forest with accuracy almost 97%, precision 91% and recall as 98%

    Computational Efficiency Analysis of Customer Churn Prediction Using Spark and Caret Random Forest Classifier

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    Today’s businesses are buying into technological advancement for productivity, profit maximization and better service delivery. Meanwhile technology as also brought about data coming in at an alarming rate in which businesses need to re-strategize how these data are being handled for them to retain ability to turn them to value. Traditional data mining techniques has proofed beyond doubt that data can be harnessed and turn into value for business growth. But the era of large scale data is posing a challenge of computational efficiency to this traditional approach. This paper therefore address this issue by under-studying a big data analytics tool-Spark with a data mining technique Caret. A churn Telecom dataset was used to analyse both the computational and performance metrics of the two approaches using their Random Forest (RF) classifier. The Classifier was trained with same the train set partitioning and tuning parameters. The result shows that Spark-RF is computational efficient with execution time of 50.25 secs compared to Caret-RF of 847.20 secs. Customer churning rate could be minimized if proper management attention and policy is paid to tenure (ShortTenure), Contract, InternetService and PaymentMethod as the variable importance plot and churn rate count mechanism confirm that. The Classifier accuracy was approximately 80% for both implementation. Keywords: Spark, Caret, Random Forest, Churn, accurac

    Using analytical CRM system to reduce churn in the telecom sector: A macine learning approach

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    Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2019Customers are considered to be the most valuable assets of any business, and thus their loyalty is key to profitability as they indulge in repeat purchases and attract their colleagues through word-of-mouth. In competitive markets such as telecommunications, customers have a lot of flexibility due to the variety of service providers available and the introduction of mobile number portability (MNP) thus they can easily switch services and service providers. Customer churn is, therefore, a major problem among telecommunication companies hence their quest to reduce customer churn rate and retain an existing customer. Customer relationship management systems have been used over the years to track patterns within the customer data, but this could be improved notably with the technological advances hitting the universe on a daily basis. We have moved past the age of innovations around steam engines, electricity, computers, mobile, internet to the current technology trends in artificial intelligence and big data. We are at the cusp of a new wave where enterprises have embraced the application of machine learning in streamlining different business processes. Telecom companies have the advantage of mining large customer datasets that can be leveraged on for predictive analysis using data science. This project explores the use of analytical CRM system in reducing customer churn in the telecom industry using machine learning algorithms to predict customer behavior in order to retain them. Its goal is to analyze all relevant customer data and develop focused customer retention programs. This is on the focus that if you could somehow predict in advance which customers are at risk of leaving, you could develop focused customer retention programs to reduce customer churn.Ashesi Universit

    Classification of customer call details records using Support Vector Machine (SVMs) and Decision Tree (DTs)

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    On a daily basis, telecom businesses create a massive amount of data. Decision-makers underlined that acquiring new customers is more difficult than maintaining current ones. Further, existing churn customers' data may be used to identify churn consumers as well as their behavior patterns. This study provides a churn prediction model for the telecom industry that employs SVMs and DTs to detect churn customers. The suggested model uses classification techniques to churn customers' data, with the Support Vector Machine (SVMs) method performing well 98.36 % properly categorized instances) and the Decision Tree (DTs) approach performing poorly 33.04 % and the decision tree algorithm deliver outstanding results

    Intelligent data analysis approaches to churn as a business problem: a survey

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    Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.Peer ReviewedPostprint (author's final draft
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