Comparative Analysis of Machine Learning Techniques to Identify Churn for Telecom Data

Malleswari, M.; Manira, R.J; Kumar, Praveen; Murugan,

doi:10.14419/ijet.v7i3.34.19210

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…In [9], the authors compared the performance of different machine learning models for predicting customer churn, which were balanced random forest, random forest, and logistic regression. They utilized principal component analysis (PCA) for feature selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Customer Retention through Deep Learning and Imbalanced Data Techniques

Loukili,

Messaoudi,

El Ghazi

2024

Iraqi Journal of Science

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Accurately predicting customer churn is considered crucial by businesses in order to take proactive measures to retain their customers and avoid financial losses. In this paper, a customer churn prediction model is proposed that incorporates deep neural networks and imbalanced data techniques. The approach involves applying oversampling and undersampling methods to address class imbalances in the dataset. The model's performance is evaluated using various evaluation metrics and compared to other methods. The results demonstrate that superior performance in predicting customer churn is achieved by the proposed model compared to traditional statistical methods and deep neural networks without imbalanced data techniques. Important implications for businesses seeking to reduce customer churn and improve customer retention are provided by our findings. By using the suggested model, customers can be retained, and financial losses can be avoided proactively.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Customer Retention through Deep Learning and Imbalanced Data Techniques

Loukili,

Messaoudi,

El Ghazi

2024

Iraqi Journal of Science

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“…Like the studies above, Malleswari et al [22], Suguna et al [23], Isabella Amali [24], Raja and Pandian [25] and Jaisakthi [26] study on customer churn prediction problem and use some machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

BİLİŞİK,

SARP

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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In today's conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the company’s loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Artificial Neural Networks (ANN) are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

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“…Consumers can easily compare products or services and switch vendors [2] One of the challenges that E-Commerce companies face is the effort to reduce the number of customers who stop using the company's services and move to another company. The behavior of customers from a service that does not continue to subscribe to the company or the ratio between the number of customers who stop using the company's products/services in a certain period, with the average total number of customers in the same period and that can cut the company's profits is called the churn rate [3]. The customer segmentation process aims to classify customers based on their consumption behavior, preferences, and demands and provide targeted products and services according to the existing customer groups.…”

Section: Introductionmentioning

confidence: 99%

Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall

Huda,

Achmad Suhendra,

Arif Bijaksana

2023

JOIV : Int. J. Inform. Visualization

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The classification of churn is driven by the potential risks e-commerce companies face, such as losing customers who discontinue their service usage or churn. Marketing specialists have shifted their efforts from acquiring new customers to retaining existing ones in order to mitigate customer churn. Predictive models are created using data mining techniques to identify customer churn patterns. This study proposes a data mining model aimed at predicting customer behavior, with the processed results utilized as suggestions for improvements and company strategies in customer retention through segmentation and classification. Segmentation and classification involve several variables: Session, Interaction with Application, Actions taken during the interaction, purchasing, claim, and discount. This study employs a clustering technique based on the Recency, Frequency, and Monetary (RFM) model, which considers factors such as the time since the last visit, the number of visits, and the total amount spent by the customer. The classification algorithm model was evaluated by comparing three classification algorithms: decision tree and Support Vector Machine (SVM). The decision tree algorithm had the highest accuracy, achieving an impressive 87% accuracy rate in customer classification. Factors influencing customer churn include purchasing behavior, session activity, claim feature utilization, adding products to cart, and discounts. Improving stock management is crucial to prevent stock shortages, likely to cause churn. Additional measures like sending emails/notifications and offering vouchers/loyalty points can be implemented for customers who added products to their carts but didn't complete the purchase, with a focus on popular products.

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Comparative Analysis of Machine Learning Techniques to Identify Churn for Telecom Data

Cited by 6 publications

References 2 publications

Enhancing Customer Retention through Deep Learning and Imbalanced Data Techniques

Enhancing Customer Retention through Deep Learning and Imbalanced Data Techniques

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall

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