Modeling the Telemarketing Process using Genetic Algorithms and Extreme
  Boosting: Feature Selection and Cost-Sensitive Analytical Approach
Nazeeh Ghatasheh Ismail Altaharwa Khaled Aldebei
Currently, almost all direct marketing activities take place virtually rather
than in person, weakening interpersonal skills at an alarming pace.
Furthermore, businesses have been striving to sense and foster the tendency of
their clients to accept a marketing offer. The digital transformation and the
increased virtual presence forced firms to seek novel marketing research
approaches. This research aims at leveraging the power of telemarketing data in
modeling the willingness of clients to make a term deposit and finding the most
significant characteristics of the clients. Real-world data from a Portuguese
bank and national socio-economic metrics are used to model the telemarketing
decision-making process. This research makes two key contributions. First,
propose a novel genetic algorithm-based classifier to select the best
discriminating features and tune classifier parameters simultaneously. Second,
build an explainable prediction model. The best-generated classification models
were intensively validated using 50 times repeated 10-fold stratified
cross-validation and the selected features have been analyzed. The models
significantly outperform the related works in terms of class of interest
accuracy, they attained an average of 89.07\% and 0.059 in terms of geometric
mean and type I error respectively. The model is expected to maximize the
potential profit margin at the least possible cost and provide more insights to
support marketing decision-making.