SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
A data-driven approach with explainable artificial intelligence for customer churn prediction in the telecommunications industry
14 references·3 citing·46 second hop
A data-driven approach with explainable artificial intelligence for customer churn prediction in the telecommunications industry (2025)A data-driven approach with explainable a…Investigating customer churn in banking: a machine learning approach and visualization app for data science and management (2023)Investigating customer churn …Global fintech trends and their impact on international business: a review (2023)Global fintech trends and the…Customer churning analysis using machine learning algorithms (2023)Customer churning analysis us…A survey on machine learning methods for churn prediction (2022)A survey on machine learning …Customer churn prediction system: a machine learning approach (2021)Customer churn prediction sys…Churn Prediction in Telecommunication using Logistic Regression and Logit Boost (2020)Churn Prediction in Telecommu…A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector (2019)A Churn Prediction Model Usin…Social network analytics for churn prediction in telco: Model building, evaluation and network architecture (2017)Social network analytics for …Churn prediction on huge telecom data using hybrid firefly based classification (2017)Churn prediction on huge tele…Big data preprocessing: methods and prospects (2016)Big data preprocessing: metho…A Survey on Customer Churn Prediction using Machine Learning Techniques (2016)A Survey on Customer Churn Pr…Improved churn prediction in telecommunication industry using data mining techniques (2014)Improved churn prediction in …Applying data mining to telecom churn management (2005)Applying data mining to telec…Turning telecommunications call details to churn prediction: a data mining approach (2002)Turning telecommunications ca…Explainable AI-driven customer churn prediction: a multi-model ensemble approach with SHAP-based feature analysis (2026)Explainable AI-driven custome…A Fuzzy-XAI Framework for Customer Segmentation and Risk Detection: Integrating RFM, 2-Tuple Modeling, and Strategic Scoring (2025)A Fuzzy-XAI Framework for Cus…A comprehensive survey on customer churn analysis studies (2025)A comprehensive survey on cus…Experiments in induction (1966)Experiments in Induction (1967)A Data Mining-Based Framework for Supply Chain Risk Management (2018)Machine-Learning Techniques for Customer Retention: A Comparative Study (2018)Predicting Employee Attrition Using Machine Learning Techniques (2020)Building Data Mining Applications for CRM (1999)Knowledge management and data mining for marketing (2001)New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2011)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis (1999)A survey of results on mobile phone datasets analysis (2015)2013 IEEE International Conference on Big Data (2013)Data mining with big data (2013)Big Data Quality: A Survey (2018)Big data monetization throughout Big Data Value Chain: a comprehensive review (2020)Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering (2022)Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior (2000)Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers (2000)Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods (2018)Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction (2023)ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry (2024)Zero defections: quality comes to services. (1990)Marketing management: analysis. planning. implementation and control (2000)A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods (2020)Integrated Churn Prediction and Customer Segmentation Framework for Telco Business (2021)A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners (2024)A novel evolutionary data mining algorithm with applications to churn prediction (2003)Customer churn prediction in the telecommunication sector using a rough set approach (2016)B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM (2022)Customer Churn Prediction in Telecom Sector Using Machine Learning Techniques (2022)Deep Churn Prediction Method for Telecommunication Industry (2023)Handling class imbalance in customer churn prediction (2008)Business Intelligence and Analytics: From Big Data to Big Impact (2012)Customer churn prediction in telecom using machine learning in big data platform (2019)A comparison of machine learning techniques for customer churn prediction (2015)AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn (2024)Customer Churn Prediction using Machine Learning Models (2024)Improving Churn Detection in the Banking Sector: A Machine Learning Approach with Probability Calibration Techniques (2024)Signaling in Equity Crowdfunding (2015)The Evolution of Fintech: A New Post-Crisis Paradigm? (2015)Digital Finance and FinTech: current research and future research directions (2017)Does service quality matter in FinTech payment services? An integrated SERVQUAL and TAM approach (2024)Financial innovation and gender dynamics: a comparative study of male and female FinTech adoption in emerging economies (2024)FinTech Adoption and Corporate Greenwashing: A Technology Affordance Perspective (2024)
this work references cited by second hop

References

  • 2016Big data preprocessing: methods and prospects714 cited
  • 2005Applying data mining to telecom churn management514 cited
  • 2002Turning telecommunications call details to churn prediction: a 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
  • 2014Improved churn prediction in telecommunication industry using data mining techniques176 cited
  • 2020Churn Prediction in Telecommunication using Logistic Regression and Logit Boost160 cited
  • 2023Global fintech trends and their impact on international business: a review119 cited
  • 2017Social network analytics for churn prediction in telco: Model building, evaluation and network architecture109 cited
  • 2023Customer churning analysis using machine learning algorithms95 cited
  • 2017Churn prediction on huge telecom data using hybrid firefly based classification87 cited
  • 2023Investigating customer churn in banking: a machine learning approach and visualization app for data science and management84 cited
  • 2022A survey on machine learning methods for churn prediction76 cited
  • 2016A Survey on Customer Churn Prediction using Machine Learning Techniques

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

71 cited

Cited by

  • 2025A comprehensive survey on customer churn analysis studies15 cited
  • 2025A Fuzzy-XAI Framework for Customer Segmentation and Risk Detection: Integrating RFM, 2-Tuple Modeling, and Strategic Scoring15 cited
  • 2026Explainable AI-driven customer churn prediction: a multi-model ensemble approach with SHAP-based feature analysis14 cited