As the popularity of gaming has increased, the importance and effectiveness of in-game advertisements have become more relevant to marketers. However, despite this development, both marketing academics and practitioners do not fully comprehend how consumers respond to in-game advertisements. This study focuses on the dynamics associated with in-game advertisements, considering the privacy concerns and ad relevance expectations of consumers/gamers.

The channel transformation to omni-channel is currently in progress in the retail industry. For the progress to occur, facilitating meaningful experiences of customers in their customer journeys, capturing such experiences through various channels and touch points, and then analyzing the information acquired as big data are required (Lemon and Verhoef, 2016). With the increase in the number of customer experiences being observed through the internet and mobile communication, the focus is now on engagement. However, there have not yet been many studies conducted to deliberate comprehensively on how the engagements of behavioral aspects captured through various channels and the evaluation indicators of customers, as represented by the RFM or LTV, are related in a broader sense. The purpose of this research is to clarify the relational structure from a comprehensive perspective that are not constrained by monetary amounts. This paper showed results using data from the retailer. This research is divided broadly into two stages. The first stage identifies the engagements of behavioral aspects and the relationship between the respective behaviors, as well as the typification of behavioral patterns. The second stage involves clarifying the relationship between the customer’s evaluation indicators and engagement behaviors. The engagement behaviors are basically correlated with RFM, however authors found that there is no relationship between specific engagement behavior and RFM in the group of low rank customers. On the other hand, using different types of services or shops from the core business strengthens the customer relationship. Finally, the relationship between the types of engagement behaviors and the respective customer evaluation indicators is presented in the conclusion.

This exploratory research focuses on variety-seeking behavior in the e-commerce (EC) apparel market. The author introduces the causes of switching behavior through exploring different attributes such as product category, price range, and brand. The author discusses the definition of variety-seeker within relatively high price, high involvement, and less frequency category. Next, the author proposes a practical methodology to find different types of variety-seekers from transaction data and customer databases. Finally, the author identifies the characteristics of variety-seekers, including mobile device behavior and psychographics of customers. Having reviewed previous research, in this study, the author focuses on: (1) research of variety-seeking that leans toward the low price, low-involvement, and high-frequency category, (2) define and distinguish variables of variety-seeking, especially in fashion and EC websites, and (3) use of mobile channels in variety-seeking. Researchers and practitioners have studied variety-seeking behavior since the latter half of the 20th century. One of the earliest studies of variety-seeking behavior is by Tucker (1964), who proposed the exploratory behavior concept. Since then, variety-seeking behavior has been extensively researched, and it is considered the antithesis of brand loyalty. As introduced by Assael (1987), in his matrix on buying behavior types, variety-seeking occurs as a low-involvement behavior and in a relatively low-priced category (e.g., Inman, 2001). Therefore, the research on variety-seeking in a higher priced category, like fashion, is a relatively novel approach, when compared to other categories of consumer goods. Meanwhile, there are many variables in general transaction data and customer databases, such as product category and group level (large, medium, and small), brand, product attribute (color, size, gender), price, time, and store. However, much of the previous research considered brand-switching as a clue for distinguishing variety-seeking. Thus, there is little research that has considered every database variable, and defined variety-seeking in the fashion category. Recently, new research focusing on the apparel industry and EC websites has been published. For example, Ko, Kim, and Lee (2009) discussed mobile shopping for fashion products, where they proposed the concept of information-seeking. However, their research was based on questionnaires. For this review, discussing and developing a methodology to analyze variety-seeking in the EC fashion industry was necessary. Distinguishing variety-seekers in this area might be useful for retailers or manufacturers for category management or line expansion (e.g., Inman 2001). This study uses transaction data from fashion EC sites obtained from the 2016 Data Analytics Competition, which was sponsored by the Joint Association Study Group of Management Science. There were over 550,000 purchasing transactions, and approximately one million records of units purchased from April 1, 2015 to March 31, 2016. The number of customers was approximately 100,000, out of which around 3,000 answered the psychographic questionnaire. Every product was classified into 24 large groups and 226 small groups. The data covered approximately 6,500 brands across 900 shops. In this study, the author conducts three analyses. First, the author introduces the types of variety-seeking behavior into the data, which produces a distribution that resembles the shape of the Pareto distribution in terms of sales or frequency. Second, the author discusses how to distinguish variety-seekers. Brand-switching is the most important criterion of variety-seeking behavior; however, the author includes concepts such as price-seeking, category expansion, and purchase interval. Finally, the author introduces characteristics of variety-seekers with demographic and psychographic variables in order to discuss the factors that determine variety-seekers. For example, using large group switching and sales, the author distinguishes the variety-seekers (over 3,000 customers, that is, 3%). From analyzing demographic and psychographic variables, the author, then, attempts to specify the reasons for variety-seeking in the fashion category. Finally, the author confirms the differences between mobile device and PC channels. In the age of customer experience management, use of mobile device has an important role. The author demonstrates the relationship between mobile device use and variety-seeking behavior. However, this research has certain limitations. First, this exploratory research does not adopt a rigorous hypothesis testing approach. The second limitation pertains to data—if the author had web access log data and real channel purchase data, other indicators could have been calculated. However, despite these limitations, this research makes theoretical and practical contributions. First, using fashion EC website data, variety-seeking behavior could be observed in relatively high price and high-involvement categories. Second, the author proposes a simple method to distinguish variety-seekers. EC sites, in general, may have similar databases; therefore, this research has application possibilities. Third, the author explains how psychographic characteristics and mobile channel usage of variety seekers could be beneficial for further research on variety-seeking behavior.

Pricing, especially the area of discounting, poses many practical problems and continues to generate academic interest. In this exploratory study, we proposed an analysis format based on multi-channel purchasing and a method to calculate the amount of discount. Our principal contribution is using single-source panel data to calculate the discounts for several stores. First, we presented our definition of discounts. Next, as few consumers (so-called cherry-pickers) accounted for a large portion of the discounts, we confirmed that discounts follow the Pareto principle. Further, we showed how consumers used different chains; we identified the discount-sensitive consumers. We find that consumers who tend to buy high-priced brands are of key importance for sales and revenues of some chains. In the context of shopping behavior, shopping trip type is one of the main concerns. In a relatively early study, Kahn and Schmittlein (1989) showed quick trips and regular trips; however, they did not focus on discounts. Since the 1990s, researchers have been focusing on discounts and multi-channel shopping trips. Walters and Jamil (2003) explored shopping trip type and discount; however, their data are restrictive regarding the purchase period and chain. Fox and Hoch (2005) also showed how the difference in prices across two grocery store chains on the same day was distributed and how some consumers could exhibit cherry-picking behavior. Nevertheless, their research data are not comprehensive and are rather limited to two popular grocery stores. The same limitation is applicable to other studies in the literature (e.g., Bell, Ho and Tang 1998). One of the contributions of this study is our data. We used single-source panel data, acquired through the service of Macromill, Inc. The monitors of this service can use portable code scanners to read JICFS(JAN Item Code File Service)codes anytime and anywhere. Since we focus on multi-channel shopping, we used only data on food purchases. We included 6 million purchase transactions that covered all food categories in 2012 for 6,422 individuals who live in the metropolitan area around Tokyo. There was, essentially, no gender and age bias in the data. However, the data do not provide locational information of the store and consumer. Location is a very important factor for determining a shopping trip (e.g., Arentze, Oppewal and Timmermans 2005). This is one of the limitations of our research. The first step in our analysis is defining the discounts on individual items. To mitigate the influence of extremely high unit prices, we calculated the discount as the difference between the third-quartile price and the purchase price. The next step, we identified cherry-pickers from cross table of sales decile and discount decile. We examined demographic feartures, women relatively exhibit higher cherry-picking behavior than men, and there is a distinct relationship between household income and cherry-picking. As the income level rises, the ratio of cherry-pickers decreases. As for the relationship with age, we find that the ratio of cherry-pickers is the highest among consumers who are in their 30s. However, this ratio decreases with age. Examining the ratio of cherry-pickers by chain, we find that the ratio is higher in EDLP type than in Hi-Lo type chains or High-quality type chains. To simplify the purpose of our study, we deal with the milk category. The reasons for selecting the milk category are that it is one of the most popular food categories in Japan and that the Japanese milk category is assumed as a loss leader. We present some results from a k-means cluster analysis and show how the customer segments utilize each channel in the milk category. This allows us to observe each segment is more discount-conscious in each channel. Among Japanese milk brands, there are some popular and high-priced (above 200 yen / 1 liter pack) brands that rarely offers bargain sales. And the customer who buy that high-priced brands frequently is known as a loyal one. We showed the frequency of purchases for high-priced brands and the frequency (in days) of chain visits. We compared a well-known EDLP type and low-priced supermarket chain and perceived as average or slightly expensive chain. Consumers who habitually purchase the high-priced brand are likely to be loyal customers of the supermarket chain, but this does not hold for the low-priced chain. We will present the results from further analysis and details at the conference.