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Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking
13 references·0 citing·50 second hop
Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking (2018)Deep Ensemble Classifiers and Peer Effect…Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors (2017)Deep Learning in Customer Chu…Using Deep Learning To Predict Customer Churn In A Mobile Telecomunication Network (2016)Using Deep Learning To Predic…Predicting time-to-churn of prepaid mobile telephone customers using social network analysis (2016)Predicting time-to-churn of p…Mining Telecommunication Networks to Enhance Customer Lifetime Predictions (2014)Mining Telecommunication Netw…Social network analysis for customer churn prediction (2013)Social network analysis for c…Modeling partial customer churn: On the value of first product-category purchase sequences (2012)Modeling partial customer chu…Improving customer retention in financial services using kinship network information (2012)Improving customer retention …Segmentation of telecom customers based on customer value by decision tree model (2011)Segmentation of telecom custo…A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business (2010)A two-stage clustering method…Application of data mining techniques in customer relationship management: A literature review and classification (2008)Application of data mining te…Predicting customer retention and profitability by using random forests and regression forests techniques (2005)Predicting customer retention…Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry (2000)Predicting subscriber dissati…Retail relationships and store loyalty: A multi-level perspective (1997)Retail relationships and stor…Structural equation modeling in practice: A review and recommended two-step approach. 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References

  • 2008Application of data mining techniques in customer relationship management: A literature review and classification1,267 cited
  • 1997Retail relationships and store loyalty: A multi-level perspective807 cited
  • 2000Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry347 cited
  • 2005Predicting customer retention and profitability by using random forests and regression forests techniques308 cited
  • 2013Social network analysis for customer churn prediction164 cited
  • 2012Modeling partial customer churn: On the value of first product-category purchase sequences95 cited
  • 2010A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business82 cited
  • 2011Segmentation of telecom customers based on customer value by decision tree model71 cited
  • 2012Improving customer retention in financial services using kinship network information64 cited
  • 2016Predicting time-to-churn of prepaid mobile telephone customers using social network analysis46 cited
  • 2017Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors36 cited
  • 2016Using Deep Learning To Predict Customer Churn In A Mobile Telecomunication Network20 cited
  • 2014Mining Telecommunication Networks to Enhance Customer Lifetime Predictions12 cited

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