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Introduction In warranty studies we model and analyze two types of risks, the risk of malfunctioning of a product quantified by its reliability, and the risk of misunderstanding the features and qualities of a product while deciding whether to buy it. The later risk is caused by the information asymmetry, which is a natural property of any communication process, see Christozov, Chukova, Mateev (2009a), we call it risk of misinforming. Due to the strong impact of the information asymmetry on the markets and specifically on the completion in the marketplace, studies related to information asymmetry attracted the attention of several research teams. Mukhopadhyay, Yue and Zhu (2011) studied the impact of information asymmetry on market completion. Akan, Ata and Lariviere (2011) studied outsourcing and the associated contracting problem under information asymmetry. Knill, Minnick and Nejadmalayeri (2011) examined whether it is rational for analysts to post biased estimates and how information asymmetry and analyst experience factor into the decision aiming to minimize forecasting error. Information provided by the Seller informs different clients differently, because of the different information asymmetry. Some are informed correctly regarding all properties of the product, but the same information may mislead others. Market segmentation allows sellers to approach different categories of potential buyers differently, so that they can address them with advertising messages, or messages with the product description, in the most appropriate and beneficial way. In this paper we propose an approach for segmentation of the population of potential customers based on how they understand the seller's message and the level of needed additional adjusting information for reducing their risk of making a wrong purchase decision, i.e., the risk of misinforming. It is natural to use the quantitative measures of the risk of misinforming for market segmentation. The earlier studies addressed mainly the warranty aspect of proposed measures for quantifying the risk of misinforming (see Christozov, Chukova, Mateev 2009b). The current study, influenced also by the ideas behind maximizing the "information gain" measure (see for example Han, Kamber, Pei (2011), Chapter 8), is addresses improvement of communication process. The idea is that maximization of information gain minimize the risk of misinforming. In this study we define a distance between customers of a given product based on the information needed to minimize the risk of misinforming and the seller's losses caused by this misinforming. Nowadays, the market segmentation is based on the information needs of the targeted group, so ability to make a conscious and realistic purchase decision is critical. A wrong purchase decision has a negative effect on the customer, and can lead to a significant losses incurred by the seller. These losses could be of two types--based on a missed sale or due to creating an unsatisfied customer. If the product is covered by a warranty of misinforming, an unsatisfied customer could cause direct financial losses by making a legitimate warranty claim. This concept was introduced in Christozov, Chukova, Mateev (2006) where "positive" and "negative" wrong purchase decisions were identified, i.e., the "positive" wrong purchase decision will generate a legitimate warranty claim, whereas the "negative" wrong purchase decision will lead to a missed sale. Both risks are caused by the misunderstanding or misinterpretation of the message provided by the seller to the customers. A natural question aiming to address this misunderstanding is: what must be the content of the additional message provided by seller to the customer so that his/her understanding about the real properties of the product is appropriately adjusted. Also, what is the seller's cost for providing such a message. It is clear that a particular customer must be provided with specific additional information, which reflects his/her specific product needs [n. …

Market Segmentation based on Risk of Misinforming Reduction