Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning

Agrawal, Sanket; Das, Avishek; Gaikwad, Amit; Dhage, Sudhir

doi:10.1109/icscee.2018.8538420

Cited by 33 publications

(22 citation statements)

References 8 publications

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“…Stacking algorithms would be expected to significantly improve model performance because they combine multiple classifiers, such as logistics regression and random forest combines for generating fewer errors (Abbasimehr et al, 2014). On the other hand, DL uses multiple layers to progressively extract higher-level features from the raw input by which the machine can learn customer behavioural patterns more thoroughly and effectively (Agrawal et al, 2018). However, this approach would cost much more in terms of computational time which must also be considered for any future studies.…”

Section: Discussionmentioning

confidence: 99%

Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company

Dhini¹,

Fauzan²

2021

IJTech

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Technology advancement has developed a shift perception towards better service from internet providers, and the power to move easily to another provider to secure improved quality results in customer churn. Internet service providers must detect the risk of churn at the earliest opportunity if they want to retain their customers. This study aimed to predict churn using recent developments in machine learning approaches, and customer data from one of the biggest fixed broadband companies in Indonesia was selected as a case study. Ensemble learning is the collaboration of meta-algorithms to improve model performance, and two such approaches were performed in this study, namely random forest and extreme gradient boosting (XGBoost). The results show that the ensemble learning models outperform classical technique and XGBoost is the best algorithm for predicting customer churn. Customers are thereby clustered as being at high, medium, or low risk of churn, and the company can specify particular retention strategies towards each customer cluster.

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Section: Discussionmentioning

confidence: 99%

Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company

Dhini¹,

Fauzan²

2021

IJTech

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“…It is more important to find out the reasons behind customer churn [29]. And the models are supposed to provide the churn factors for marketers to better understand the reason behind churn [30]. The Bayesian Analysis is implemented to conduct the factor analysis in this research.…”

Section: Research Motivation and Contributionsmentioning

confidence: 99%

Integrated Churn Prediction and Customer Segmentation Framework for Telco Business

et al. 2021

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In the telco industry, attracting new customers is no longer a good strategy since the cost of retaining existing customers is much lower. Churn management becomes instrumental in the telco industry. As there is limited study combining churn prediction and customer segmentation, this paper aims to propose an integrated customer analytics framework for churn management. There are six components in the framework, including data pre-processing, exploratory data analysis (EDA), churn prediction, factor analysis, customer segmentation, and customer behaviour analytics. This framework integrates churn prediction and customer segmentation process to provide telco operators with a complete churn analysis to better manage customer churn. Three datasets are used in the experiments with six machine learning classifiers. First, the churn status of the customers is predicted using multiple machine learning classifiers. Synthetic Minority Oversampling Technique (SMOTE) is applied to the training set to deal with the problems with imbalanced datasets. The 10-fold cross-validation is used to assess the models. Accuracy and F1-score are used for model evaluation. F1-score is considered to be an important metric to measure the models for imbalanced datasets since the premise of churn management is to be able to identify customers who will churn. Experimental analysis indicates that AdaBoost performed the best in Dataset 1, with accuracy of 77.19% and F1-score of 63.11%. Random Forest performed the best in Dataset 2, with accuracy of 93.6% and F1-score of 77.20%. Random Forest performed the best in Dataset 3 in terms of accuracy, at 63.09%, while Multi-layer Perceptron performed the best in terms of F1-score, at 42.84%. After implementing churn prediction, Bayesian Logistic Regression is used to conduct the factor analysis and to figure out some important features for churn customer segmentation. Churn customer segmentation is then carried out using K-means clustering. Customers are segmented into different groups, which allows marketers and decision makers to adopt retention strategies more precisely.

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“…Agrawal et al [28] utilized Deep Learning Approach for churn on a Telco dataset prediction. A non-linear classification model is constructed by means of a Multilayered Neural Network.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry

Praseeda

Shivakumar

2021

SN Appl. Sci.

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Customer churn has been considered as one of the key issues in the operations of the corporate business sector, as it influences the turnover directly. In particular, the telecom industries are seeking to develop new approaches to predict potential customer to churn. So, it needs the appropriate algorithms to overcome the increasing problem of churn. This work proposed a churn prediction model that employs both strategies of classification and clustering, that helps in recognizing the churn consumers and giving the reasons after the churning of subscribers in the industry of telecom. The process of information gain and fuzzy particle swarm optimization (FPSO) has been executed by the method of feature selection, besides the divergence kernel-based support vector machine (DKSVM) classifier is employed in categorizing churn customers in the proposed approach. In this way, the compelling guidelines on retention have generated since the process plays a vital role in customer relationship management (CRM) to suppress the churners. After the classification process, the churn customers are divided into clusters through the process of fragmenting the data of churning customer. The cluster-based retention offers have provided by the clustering algorithm of hybrid kernel distance-based possibilistic fuzzy local information C-means (HKD-PFLICM), whereas the measurement of distance have accomplished through the kernel functions such as the hyperbolic tangent kernel and Gaussian kernel. The results reveal that proposed churn prediction model (FPSO- DKSVM) produced better churn classification results compared to other existing algorithms such as K-means, flexible K-Medoids, fuzzy local information C-means (FLICM), possibilistic FLICM (PFLICM) and entropy weighting FLICM (EWFLICM). Article highlights Customer churn is a major concern in most of the companies as it influences the turnover directly. The performance of churn prediction has been improved by applying artificial intelligence and machine learning techniques. Churn prediction plays a crucial role in telecom industry, as they are in the position to maintain their precious customers and organize their Customer Relationship Management.

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Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning

Cited by 33 publications

References 8 publications

Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company

Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company

Integrated Churn Prediction and Customer Segmentation Framework for Telco Business

Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry

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