Makoto Mizuno

Optimal threshold analysis of segmentation methods for identifying target customers

In CRM (Customer Relationship Management), the importance of a segmentation method for identifying good customers has been increasing. For evaluation of different segmentation methods, Accuracy often plays a key role. This indicator, however, cannot distinguish two types of errors. The purpose of this paper is to overcome this pitfall by introducing two different indicators: Recall and Precision. Assuming that a promotion is addressed exclusively to the selected target customers, the financial effectiveness of the underlying segmentation method is expressed as a function of Recall and Precision. An optimization problem is then formulated so as to maximize the financial measure by finding the optimal threshold level in terms of the severeness for estimating the target set. By introducing a functional form which represents correctness and mistakes about the target set, the unique optimal solution is derived explicitly. The proposed approach is validated by using real customer purchase data.

Does advertising mislead consumers to buy low-quality products?

Abstract This paper investigates the interaction between consumer learning and advertising using a simulation model. Consumers use advertising to estimate the quality change, but learn advertisings trustworthiness through their consumption experience. Firms spend on not only advertising but also RD in this sense, advertising misleads consumers. When learning is fast or when consumers do not rely on advertising at all, advertising is unlikely to mislead them. Easy imitation of rival products also prevents advertising from being misleading. These results are consistent with existing empirical findings on advertising-quality relationships.

Emergence of the Leader-Follower Structure Among Consumers: What Type of Consumers Would Be the Most Influential in the Marketplace?

In the fashion or entertainment market, for example, consumers often prefer buying potentially popular products before other consumers do so. For this, they can rely more or less on information obtained from their own reference group in making a more correct prediction on whether or not a product will become a success. We formulate these behaviors as a process of dynamically reconstructing reference groups to improve predictive performance, resulting in the evolution of a social network between consumers. Our simulation reveals the following: (1) The leader-follower structure would emerge in the network. (2) In cases where consumers maintain broad reference groups and are permissive toward others’ fail-ures, the network would be denser, and the leaders proliferated. (3) By accepting others’ opinion more easily and not expecting them to be highly reliable, consumers are more likely to become leaders. Lastly, we discuss the implications and further development of this study.

Simulating Customer-to-Customer Interaction In a B2B Financial Service Business By Empirical Agent-Based Modeling

Service research has emphasized triad relationships between a firm, employees and customers. To coordinate these stakeholders effectively, it is highly important to understand what service activities are beneficial to all or some of these stakeholders. Yet, the recent increase in C2C interaction may make the problem more complex. This study proposes a methodology combining statistical techniques and agent-based modeling, which makes it possible to assess the joint impact of each service value and C2C interaction on the payoffs.

How Consumer-Generated Advertising Works: An Empirical Agent-Based Simulation

Affiliate advertising is a novel form of Internet advertising that enables bloggers to insert advertising for any product in their blog articles and to gain rewards based on consumers’ actual responses. To understand how this form of advertising works, we conducted Web-based questionnaire surveys among bloggers, including affiliates, and readers, including buyers. Moreover, we constructed an agent-based model that is empirically validated by the above data. The results of the simulation using this model showed that (1) link structures between affiliates and readers have a remarkable impact on the average revenues of all affiliates, and (2) the presence of random walkers in searching for a better ad mix may increase total revenues for all affiliates compared to the presence of local imitators. Based on these results, we discuss the managerial implications and suggest future avenues for research.