Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms
提出用遗传算法引导的人工神经网络来识别最可能购买某产品或服务的家庭，相比主成分分析加逻辑回归，该方法在决策标准明确时更准确且更简洁。
One of the key problems in database marketing is the identification and profiling of households that are most likely to be interested in a particular product or service. Principal component analysis (PCA) of customer background information followed by logistic regression analysis of response behavior is commonly used by database marketers. In this paper, we propose a new approach that uses artificial neural networks (ANNs) guided by genetic algorithms (GAs) to target households. We show that the resulting selection rule is more accurate and more parsimonious than the PCA/logit rule when the manager has a clear decision criterion. Under vague decision criteria, the new procedure loses its advantage in interpretability, but is still more accurate than PCA/logit in targeting households.