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.

翻译：当前，几乎所有直销活动均通过虚拟形式而非面对面方式开展，人际交往能力正以惊人速度弱化。与此同时，企业始终致力于感知并培养客户接受营销方案的趋势。数字化转型与虚拟化程度的提升迫使企业寻求新颖的营销研究方法。本研究旨在利用远程营销数据建模客户定期存款意愿，并挖掘客户最具显著性的特征。采用葡萄牙某银行的实际数据及国家社会经济指标构建远程营销决策过程模型。本研究提出两项关键贡献：其一，提出基于遗传算法的创新分类器，同步完成最佳判别性特征选择与分类器参数调优；其二，构建可解释性预测模型。通过50次重复10折分层交叉验证对最优分类模型进行严格验证，并对所选特征展开分析。该模型在目标类别准确率方面显著优于现有研究，几何均值与第一类错误率分别达到89.07%与0.059。预期该模型能以最低成本最大化潜在利润率，并为营销决策提供更多洞察支持。