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Determining the Intervening Effects of Exploratory Data Analysis and Feature Engineering in Telecoms Customer Churn Modelling
16 references·7 citing·41 second hop
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References

  • 2005The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention1,575 cited
  • 1998Data mining for direct marketing: problems and solutions658 cited
  • 2005Applying data mining to telecom churn management514 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
  • 2002Turning telecommunications call details to churn prediction: a data mining approach460 cited
  • 2007The effects of brand credibility on customer loyalty429 cited
  • 2008Classifying the segmentation of customer value via RFM model and RS theory426 cited
  • 2008Customer churn prediction using improved balanced random forests412 cited
  • 2006Bagging and Boosting Classification Trees to Predict Churn410 cited
  • 2010Building comprehensible customer churn prediction models with advanced rule induction techniques366 cited
  • 2011Customer churn prediction in telecommunications344 cited
  • 2006Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry325 cited
  • 2009Customer churn prediction by hybrid neural networks321 cited

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

  • 2016Customer churn prediction in the telecommunication sector using a rough set approach222 cited
  • 2010Employee churn prediction188 cited
  • Cited by

    • 2022Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data41 cited
    • 2022Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks27 cited
    • 2023Machine Learning and Deep Learning technique used in Customer Churn Prediction: - A Review17 cited
    • 2021Customer Decision Prediction Using Deep Neural Network on Telco Customer Churn Data15 cited
    • 2022Analysis of customer churn prediction using machine learning and deep learning algorithms15 cited
    • 2023Ensemble classification using balanced data to predict customer churn: a case study on the telecom industry11 cited
    • 2023Analysing Theoretical Models for Predicting Employee Attrition: A Comparative Study in the FMCG Sector3 cited