In the recent world of diverse products, it is important to identify the consumer segments that should be targeted for each product. In order to identify the consumer segments to be targeted from among all consumers, it is common practice to use target attributes such as “male office workers in their 30s.” If data including attributes, hobbies and preferences of all consumers are available, target attributes can be identified using an analytical process. However, it is not realistic for each company to collect such data from all consumers for their products because of the huge cost involved. It is therefore, common to ask a consulting company that have the web browsing and purchasing histories of various sample consumers to analyze the sample data to identify the target. In particular, clustering behavioral data such as web browsing histories of sample consumers and discovering appropriate attributes that characterize target clusters (i.e. cluster attributes) is one of the most commonly used approaches. In real situations, cluster attributes are often assigned by the analyst based on the attribute statistics of the sample consumers belonging to the cluster, and multiple cluster attributes are often assumed for each cluster. When such qualitative analysis and judgment are involved, the selection of cluster attributes strongly depends on the experience and skills of the analyst. In this study, we formulate a model for clustering sample consumers based on their web browsing history, including various interests and preferences, and assigning effective cluster attributes as targets to each cluster. In addition, we propose a method to search for the best target attributes for a given objective function. By demonstrating an analysis of an actual data set, the effectiveness of the proposed method using real data is clarified.

The purpose of this research is to clarify the relationship between word-of-mouth content and hotel rankings in a Japanese travel information website. We conducted a latent semantic analysis of word-of-mouth data based on the Latent Dirichlet Allocation (LDA) model and created variables for word-of-mouth content (topic indexes). A hierarchical multiple regression analysis was performed on the ranking order of the objective variable, with the control variable (customer and hotel characteristics) as the explanatory variables in the first stage and the topic index as the explanatory variable in the second stage.

We used 3,769 word-of-mouth data written for 79 hotels between October 1, 2019 and September 30, 2020 for the analysis. As a result, five topics (bath and hot springs, cleanness, virus protection, staff, and meals with family) were extracted from the word-of-mouth data and five topic indexes were created. We found a statistically significant positive impact of the "virus protection," “staff” and “meals with family” topic indexes on hotel rankings. It is important for hotel operators to provide personalized and attentive services to each guest, to improve its ranking and for guests to enjoy special moments with their family and friends on a vacation under a secure environment.

An analytical model for clarifying the relationship between word-of-mouth content (unstructured data) and ranking order (structured data) is presented, and can be practically applied to show hotel operators how to improve their hotel services to increase their ranking on travel information websites.

As of Feb. 2021, COVID-19 is still spreading. To overcome this infectious disease, COVID-19 vaccines are being developed around the world. In Japan, inoculation of COVID-19 vaccines to people will be starting soon (as of Feb. 2021). However, desirable vaccination strategies are not clear. Therefore, we investigate the relationship between vaccination strategies and COVID-19 spreading using positive-case data in Japan. The method is SIRVD (Susceptible Infected Recovered Vaccination Death) model, which can represent vaccinated persons. Moreover, we discuss desirable vaccination strategies based on the results.