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Customer Response Model in Direct Marketing: Solving the Problem of Unbalanced Dataset with a Balanced Support Vector Machine
14 references·0 citing·50 second hop
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References

  • 2005Can we predict customer lifetime value?248 cited
  • 2006Customer satisfaction and loyalty in a digital environment: an empirical test190 cited
  • 2021On the platform but will they buy? Predicting customers' purchase behavior using deep learning183 cited
  • 2005Response modeling with support vector machines92 cited
  • 2021A Comparison and Interpretation of Machine Learning Algorithm for the Prediction of Online Purchase Conversion68 cited
  • 2017Predicting customer response to bank direct telemarketing campaign61 cited
  • 2012Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique59 cited
  • 2017Predicting direct marketing response in banking: comparison of class imbalance methods49 cited
  • 2012A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models34 cited
  • 2012A Data Mining-Based Response Model for Target Selection in Direct Marketing26 cited
  • 2020Bank CRM Optimization Using Predictive Classification Based on the Support Vector Machine Method26 cited
  • 2021Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees25 cited
  • 2011Improved response modeling based on clustering, under-sampling, and ensemble24 cited
  • 2011Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing

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