关键词: 目标客户选择, 代价敏感, 半监督学习, RSS集成方法, 半监督集成

Abstract: With the advent of the era of big data, more and more customer data are grasped by the enterprises and their marketing concept has changed from "product-centric" to "customer-centric". The enterprises pay more attention to customer relationship management (CRM) than before. In order to avoid the disadvantage of conventional marketing means, such as low efficiency, high cost and so on, many enterprises have started to use database marketing to improve the effectiveness and pertinence of their marketing activities. As one of the most important issues in database marketing, the customer targeting modeling is used to identify target customers from potential customers who are the most likely respond to the marketing means, thus helping the enterprise work out marketing strategies. It takes advantage of various customer information, including identity information, consumer preference, historical purchase records and so on to build the customer targeting model, and then predicts which customers are more likely to respond to marketing means.Actually, the customer targeting modeling is a classification problem.In a real customer targeting modeling, a small number of labeled samples and a large number of unlabeled ones can always be obtained. Most of the existing studies have used the paradigm of supervised learning, which merely built model with the labeled samples, and it's difficult to achieve satisfactory results. In order to solve this problem, semi-supervised learning (SSL) technology is introdueed, and it is combined with cost sensitive learning (CSL) and random subspace (RSS) which is one of the multiple classifiers ensemble methods, and the cost-sensitive semi-supervised ensemble model (CSSE) is proposed. This model uses the cost-sensitive SVM to handle the imbalanced class distribution in customer targeting modeling. Meanwhile, it can build a model with both labeled and unlabeled samples. Further, RSS is adopted to train a series of base classifiers and the final classification results are obtained by integration. The experiment is carried out in a customer targeting database of a car insurance company from CoIL2000 prediction competition, and the results show that CSSE model has better customer targeting performance compared with two supervised ensemble models, two single semi-supervised models, and two semi-supervised ensemble models.Apart from the AUC value which is frequently used, hit rate, Lorenz curve and lift chart are also used to evaluate the customer targeting performance more intuitively. It provides a good idea to further research, that is, more targeted and more reasonable evaluate indicators shouold be used to improve the practicality of the model in the research field. In CRM, there are many other classification problems that are similar to customer targeting modeling, such as customer churn prediction, customer credit scoring. Therefore, the proposed model can also be applied to these fields, and can achieve satisfactory classification performance.

Key words: customer targeting, cost-sensitive, semi-supervised learning, RSS ensemble method, semi-supervised ensemble