Customer churn prediction for commercial banks using customer-value-weighted machine learning models

Wu, Zongxiao; Li, Zhiyong

doi:10.21314/jcr.2021.011

Cited by 3 publications

(2 citation statements)

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“…Recency, frequency, and monetary model is an important method of data mining that has significant practical value in CRM and marketing (Hu et al, 2022). Wu et al (2021) proposed framework with customer-value-weighted machine learning models, which give companies useful insights to more effective development of marketing strategies for various consumer's groups. In precision marketing, the allocation of limited resources to different target groups of customers is difficult.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer churn prediction model: a case of the telecommunication market

et al. 2022

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The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

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Section: Literature Reviewmentioning

confidence: 99%

Customer churn prediction model: a case of the telecommunication market

et al. 2022

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“…The practice of gathering helpful information and recurring patterns from massive amounts of data is known as data mining. By leveraging data mining approaches, banks can better understand customer behavior and develop targeted strategies to retain customers and reduce churn (Wu and Li 2021;Tao et al 2020) Authors of the study (Kaur and Kaur 2020) utilize many machine learning models to the bank dataset in an effort to forecast the likelihood of customer churn. These models include logistic regression (LR), decision trees (DT), K-nearest neighbor networks (KNN), random forests (RF), and others.…”

Section: Introductionmentioning

confidence: 99%

Analyzing customer churn in banking: A data mining framework

Saxena,

Singh,

2023

Multidiscip. Sci. J.

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Customer churn, the loss of customers to a business, is a significant challenge in the banking industry. Retaining existing customers is crucial for banks to maintain profitability and sustain growth. This paper focuses on analyzing customer churn in the banking sector. The study utilizes data mining and predictive analytics techniques to analyse customer behaviour, identify churn patterns, and develop predictive models. This research uses a data mining technique called Gaussian mixture model clustering-based adaptive support vector machine (GMM-ASVM) to forecast customer loss in the banking industry. By analyzing consumer competency and loyalty to the banking industry using GMM, this study predicts customer behaviour using a clustering approach. An accuracy of 98% was attained while classifying the clustering results using ASVM. This study gives bank administrators the ability to analyse the behaviour of their clients, which may trigger appropriate tactics based on engaging quality and increase appropriate actions of administrator capacities in interactions with customers.

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The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review

Muth,

Lingenfelder,

Nufer

2024

Manag Rev Q

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In a contemporary context characterised by shifts in macroeconomic conditions and global uncertainty, predicting the future behaviour of demanders is critical for management science disciplines such as marketing. Despite the recognised potential of Machine Learning, there is a lack of reviews of the literature on the application of Machine Learning in predicting demanders’ behaviour in a volatile environment. To fill this gap, the following systematic literature review provides an interdisciplinary overview of the research question: “How can Machine Learning be effectively applied to predict demand patterns under macroeconomic volatility?” Following a rigorous review protocol, a literature sample of studies (n = 64) is identified and analysed based on a hybrid methodological approach. The findings of this systematic literature review yield novel insights into the conceptual structure of the field, recent publication trends, geographic centres of scientific activity, as well as leading sources. The research also discusses whether and in which ways Machine Learning can be used for demand prediction under dynamic market conditions. The review outlines various implementation strategies, such as the integration of forward-looking data with economic indicators, demand modelling using the Coefficient of Variation, or the application of combined algorithms and specific Artificial Neural Networks for accurate demand predictions.

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Customer churn prediction for commercial banks using customer-value-weighted machine learning models

Cited by 3 publications

References 0 publications

Customer churn prediction model: a case of the telecommunication market

Customer churn prediction model: a case of the telecommunication market

Analyzing customer churn in banking: A data mining framework

The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review

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