Keyword advertising has been used as a promotion tool rather than the advertising itself to online retailers. This is because the online retailer expects the direct sales increase when they deploy the keyword sponsorship. In practice, many online sellers rely on keyword advertising to promote their sales in short term with limited budget. Most of the previous researches use direct revenue factors as dependent variables such as CTR (click through rate) and CVI (conversion per impression) in their researches on the keyword advertising [14,16,22,25,31,32]. Previous studies were, however, conducted in the context of aggregate-level due to the limitations on the data availability. These researches cannot evaluate the performance of keyword advertising in the individual level.To overcome these limitations, our research focuses on conversion of keyword advertising in individual-level. Also, we consider manageable factors as independent variables in terms of online retailers (the costs of keyword by implementation methods and meanings of keyword).In our study we developed the keyword advertising conversion model in the individual-level. With our model, we can make some theoretical findings and managerial implications.Practically, in the case of a fixed cost plan, an increase of the number of clicks is revealed as an effective way. However, higher average CPC is not significantly effective in increasing probability of purchase conversion. When this type (fixed cost plan) of implementation could not generate a lot of clicks, it cannot significantly increase the probability of purchase choice.Theoretically, we consider the promotional attributes which influence consumer purchase behavior and conduct individuals-level research based on the actual data. Limitations and future direction of the study are discussed.

In the e-commerce, the conversion into the multi-media is the important issue. According to the research by Nielsen Korea, the 83% of customers who purchase the products in the e-commerce utilize multi-channel to buy the products such as mobile and online [3]. Thus, to effectively implement online advertising, marketers should understand the customers' path [15] in the multi-channel. The study of the multi-site activities plays an important role to predict customers' purchase [28].To explain the e-commerce site visit activities of customers, we have developed research model in terms of the online advertising. This research model is based on the study of Moe and Fader [23]. There are two types of composition in the research model. First, general site visit as an exploratory search have net effect on the shopping site visit because customers could acquire or develop information on the e-commerce site via online advertising. Secondly, the e-commerce site visit as a goal-directed search cause threshold of the e-commerce site visit because customers could achieve their goal. When the threshold is increased, the probability of a shopping site visit is decreased and vice versa. Thus, we have investigated the impact of customers' previous visit activities (general site visit and shopping site visit) on the next e-commerce site visit in terms of dynamic view.Research data was provided by Cheil World Wide. This panel data include mobile and online log data of panelists from Jan. 2013 to March 2013.As the results, the customers' e-commerce site visit on the online media would decrease the probability of e-commerce site visit because these visit activities increase the threshold of e-commerce site visit. This result is similar with the previous study [23]. Otherwise, since e-commerce site visit on the mobile media decrease the threshold, the customers' probability of e-commerce site visit would increase In summary, the site visit activities on the mobile could improve the probability of e-commerce site visits.

■ ■The recent keyword advertising does not reflect the individual customer searching pattern because it is focused on each keyword at the aggregate level. The purpose of this research is to observe processes of customer searching patterns. To be specific, individual deal-proneness is mainly concerned. This study incorporates location as a control variable. This paper examines the relationship between customers' searching patterns and probability of purchase.A customer searching session, which is the collection of sequence of keyword queries, is utilized as the unit of analysis. The degree of deal-proneness is measured using customer behavior which is revealed by customer searching keywords in the session. Deal-proneness measuring function calculates the discount of deal prone keyword leverage in accordance with customer searching order. Location searching specificity function is also calculated by the same logic. The analyzed data is narrowed down to the customer query session which has more than two keyword queries. The number of the data is 218,305 by session, which is derived from Internet advertising agency's (COMAS) advertisement managing data and the travel business advertisement revenue data from advertiser's.As a research result, there are three types of the deal-prone customer. At first, there is an unconditional active deal-proneness customer. It is the customer who has lower deal-proneness which means that he/she utilizes deal-prone keywords in the last phase. He/she starts searching a keyword like general ones and then finally http://dx.doi.org/10.7737/JKORMS.2014.39.