In recent years, more and more consumers who cancelled their newspaper subscription have signed up for online-flyer portal sites. However, their site access logs are not necessarily linked with their purchase records stored in ID-POS data, which is a marketing problem from the sellers' point of view. As a solution to this problem, we propose a practical framework of a hidden Markov model which allows for the assessment of the flyer advertisement. We also present a hierarchical Bayesian model which sheds light on the information search and shopping behavior of consumers, given individual heterogeneity. Our results demonstrate that the subscribers of online flyers are more likely to visit stores as they have more flyer accesses, email advertisements from portal sites and the announcements of time-limited sales.

The authors clarify the behavioral traits of PLP (Partnership Loyalty Program) members before their trial purchase at the focal company. Specifically, they apply a zero-inflated negative binomial regression model using historical data of PLP members: purchase, marketing reaction, and reward program usage. There are two contributions to their research. First, they clarify the behavioral traits of PLP members at other coalitional companies before the trial purchase at the focal company, which cannot be captured by the loyalty program of a single company. Then, the behavioral traits of PLP members are captured from the three perspectives: purchasing behavior, marketing reaction, and reward acquisition/redemption behavior. These results induce discussions on the effectiveness of PLP from the perspective of sharing and utilizing behavioral information of PLP members among coalitional companies.

Although past studies have demonstrated the significant impact of emotional expression on the helpfulness of online reviews, the explicit effects of emotion require further exploration. This study fills a gap in the existing literature by uncovering, in detail, how emotions affect review helpfulness. We considered whether the influence of emotion is dependent on varying review content and customer uncertainties about a product. We used Latent Dirichlet Allocation, one of natural language processing (NLP) techniques, to extract review content and we used product pricing plans to represent product uncertainties. We applied a regression method to analyze 268,293 (English only) online reviews of 2,832 products to explore further the relationship between emotions and review helpfulness. Our findings suggest that 1) the impact of emotions on perceptions of helpful reviews depends strongly on the review content that emotions interact with, and 2) similar emotional dimensions have different impacts when uncertainty about a product differs. We present the effects of each emotion in detail.

Social survey questionnaires tend to be large numbers of items with diverse content. Hence, irrespective of the quantification procedure used, the quantitative dimensions obtained may be quite large. In the usual applications of multiple correspondence analysis (MCA), however, three-dimensional solutions are the most complex interpretations typically employed. Orthonormal principal component analysis (OPCA) for categorical variables (Murakami, 2020) was devised to interpret large-dimensional quantities of information in categorical variables. In this study OPCA is applied to survey data obtained from spectators at a Japanese professional baseball stadium. Six interpretable components are derived, and mean differences of component scores among four demographic groups are found. From the simple structure attained by rotation of the matrix of weights, it became possible to draw scatter plots between specified components. A few plots between uncorrelated but nonlinearly related components suggested that so-called horseshoe phenomena are not necessarily mathematical artifacts but may reflect empirical properties.

Signal detection theory is a framework developed in mathematical psychology to explain judgement with perceptual uncertainty, and is used in a variety of fields of psychonomics. It quantifies signal detectability, which is the ability to detect a perceived signal, and response bias, which is the tendency to overestimate or underestimate the existence of a signal. It is essential for applied researchers to understand its mathematical background and theoretical development to date in order to apply the theory properly and productively. However, few comprehensive documents are available that comprehensively describe the theory in Japanese. Therefore, this paper addresses the basic concepts and methods of the theory with related mathematical backgrounds and reviews its recent theoretical developments.

Drug stores have recently expanded in size and widened available product categories. Efficient store management requires accurate and rapid data analysis and in-depth understanding of customers' purchasing characteristics. In this study, we apply the block clustering method to point-of-sale (POS) data with identifications (IDs) in drug stores in Japan's Gifu region to examine the purchasing characteristics of product categories. Since this method can simultaneously evaluate customers and product categories, it is possible to investigate data more easily and quickly than other analytical methods used for customers and product categories. We construct blocks combining clusters of customer and product categories and identify the customers that stores should prioritize and specific product categories that should bolster marketing measures. This study proposes a method that retailers can employ when considering decisions on marketing activities such as product lineups, sales floor layouts, or pricing strategies.