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A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners
15 references·12 citing·43 second hop
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References

  • 2004Balancing Acquisition and Retention Resources to Maximize Customer Profitability751 cited
  • 2015Customer loyalty: a review and future directions with a special focus on the hospitality industry620 cited
  • 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees529 cited
  • 2019Customer churn prediction in telecom using machine learning in big data platform486 cited
  • 2015A comparison of machine learning techniques for customer churn prediction470 cited
  • 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach460 cited
  • 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector394 cited
  • 2021Customer churn prediction system: a machine learning approach328 cited
  • 2018Customer churn prediction in telecommunication industry using data certainty251 cited
  • 2016A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry214 cited
  • 2019Implementing big data strategies: A managerial perspective182 cited
  • 2012A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models177 cited
  • 2016Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry

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161 cited
  • 2017An empirical comparison of techniques for the class imbalance problem in churn prediction161 cited
  • 2019Incorporating textual information in customer churn prediction models based on a convolutional neural network149 cited
  • Cited by

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    • 2024Digital technologies and social sustainability in the digital transformation age: a systematic analysis and research agenda47 cited
    • 2025Ensemble-based customer churn prediction in banking: a voting classifier approach for improved client retention using demographic and behavioral data44 cited
    • 2025Mitigating class imbalance in churn prediction with ensemble methods and SMOTE42 cited
    • 2025Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction32 cited
    • 2024Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns14 cited
    • 2025Machine Learning-Based Analysis of Technology Acceptance in FinTech: A Behavioral Study Using Digital Wallet Data13 cited
    • 2025Bridging Predictive Insights and Retention Strategies: The Role of Account Balance in Banking Churn Prediction13 cited
    • 2025Deep Learning Models for Predicting Customer Churn in Omni-Channel Retail Environments10 cited
    • 2025An Enhanced Deep Learning Approach to Potential Purchaser Prediction: AutoGluon Ensembles for Cross-Industry Profit Maximization10 cited
    • 2025Data-Driven Decision Support in SaaS Cloud-Based Service Models10 cited
    • 2025Predicting customer loyalty in omnichannel retailing using purchase behavior, socio-cultural factors, and learning techniques10 cited