Yamada Hiroki

This study aims to reveal the satisfaction structure of department stores located in specific urban regeneration urgent development areas and the degree of influence of the attractiveness of the areas. Specifically, we analyze the effects of store image factors and area attractiveness ranking on department store satisfaction ranking, and the effect of area brand asset factors on area attractiveness ranking simultaneously. We use a hierarchical Bayesian binomial logit model framework, and express the structure of department store satisfaction ranking and area attractiveness ranking for each consumer in the within-individual model. As a result of model estimation, we clarified store image factors that influence store satisfaction ranking, and area brand asset factors that influence area attractiveness ranking. Furthermore, we found that area attractiveness ranking also had a significant impact on store satisfaction ranking, and simultaneous analysis revealed area brand asset factors that can influence store satisfaction ranking. Based on these results, we were able to discuss perspectives on marketing strategies and public relations activities that department storeʼs management should take into account.

This study aimed to elucidate customer satisfaction with specific drugstores and reveal successful marketing measures. We used a Bayesian hierarchical logit model that accounts for the heterogeneity of consumers. The proposed model incorporated the variable selection model proposed in the brand selection model of Gilbride, Allenby, and Brazell (2006). The variable selection model expresses structural heterogeneity of individual brand selection models. By incorporating the variable selection model into the logit model, we could express the structural heterogeneity of the satisfaction model for each customer in addition to the difference in reaction parameter values. Therefore, we were able to capture the factors that clearly affect satisfaction. We used questionnaire data about Vdrug and Genky, which are competing in Gifu. The questionnaire included items about convenience, product lineup, product quality, price, store atmosphere, clerk service, and satisfaction ranking. When a Bayesian hierarchical model is used in the marketing field, panel data are often used for estimating the model stably (Yamada and Sato, 2019). In this study, however, we applied cross-sectional data to the proposed model. Therefore, the results derived from the model estimation carry implications for retail practice.

In this paper, POS data with IDs of drugstores is applied to the analysis method proposed by Govaert & Nadif (2010). By using the Poisson latent block model with snacks purchase history data at drugstores located in the Gifu region, we cluster both the customers and the snacks brands simultaneously, and grasp the snack brand group that the customer group with a high purchase frequency choose. Furthermore, based on the model estimation, we discuss effective marketing measures in drugstores. Although we analyze only snack category, it can be extended to other product categories and is an effective way to summarize Big Data.

We attempt to clarify the mechanism of the amount of money of purchase by each customer in a department store in Nagoya city using the Bayesian regression model. Specifically, we use two types of explanatory variables, “Direct mail”, and ”Events”, to research a relationship between the purchase amount and department store’s strategy. Direct mail means the number of mails that the department store uses to inform their customers about new items, privileges of card holders, and storewide events. We also add “frequency coming to the store” as explanatory variables. Additionally, we adopt type-1 Tobit model to presume the optimal value for the dependent variable, if the dependent variable in the regression model is zero. Furthermore, we examine the relation between department strategy and the customer’s attributes by incorporating customer’s attributes into the hierarchical model.

The purpose of this paper is to reveal how the evaluation of store image between competitive supermarkets in the same retail area influences the evaluation as the most important store. We model with the framework of a hierarchical bayesian logit model，and use the data that evaluate the elements that consist of store image attributes by the Internet questionnaire survey. We include convenience of shopping, quality of the goods, assortment of the goods, price of the goods, crew's attitude for store image attributes. From their analysis, we discuss about the marketing measure that the evaluation of existing customers is developed.

The purpose of this paper is to reveal how the evaluation about the library located in Gifu City influences its satisfaction. Most of the user in the library are elderly persons and mothers with children, however there aren’t many youngsters. I model with the framework of a hierarchical bayes model, and evaluate the elements with questionnaire data by the students at Gifu Shotoku Gakuen University. From their analysis, I discuss about the library’s service quality that the satisfaction of young people is developed.

The purpose of this research is to reveal how the marketing mix strategy in the department store influences the customer's shopping trip behavior to the department store. The model represents the mechanism of frequency of coming to the stores with framework of an hierarchical Bayes Poisson regression model. The model includes three explanatory variables: “Store loyalty”, “Direct mail”, and “Events”. These variables are also modeled by functional form with parameters. As a result, we could confirm that direct mail is one of the marketing mix variables that have the most effect on frequency of coming to the store.

2014年10月.審査合格 百貨店店舗における顧客の購買行動を消費者の異質性を考慮しうる形式でモデル化し，それらのメカニズム解明を行うとともに，顧客維持を図るための効率的な個別対応型マーケティングに関しての知見獲得を狙う．解明する購買行動は，①百貨店顧客の店舗選択行動（顧客満足），②百貨店顧客の来店行動，③百貨店顧客の客単価形成，の3点である．これらメカニズムを解明することによって，態度面及び行動面の両面で顧客維持を図るための知見を獲得する．

百貨店におけるFSPによるID付POSデータを用いて，1.顧客の同時購買傾向による商品カテゴリーの類型化（サブ・カテゴリーの形成），2.サブ・カテゴリーの購買嗜好から顧客の分類（顧客のセグメンテーション）を行う．分析手法には，因子分析及びクラスター分析を用いる．これらによって買い回りしやすい売場編集，顧客の購買パターンを考慮したDMや販売体制の方向性を考察する．

百貨店に対する消費者の満足構造を2項ロジットモデルを構築して明らかにする．モデルは消費者の満足度順位（擬似的な店舗選択とみなす）を小売ミックスに対するパフォーマンス評価と期待の差分で説明する（1）SERVQUAL型モデル，差分の gain（差分が+）と差分の loss（差分が-）で説明する．（2）プロスペクト理論型モデル，パフォーマンス評価のみで説明する．（3） SERVPERF型モデルを比較検討する．データには，銀座地区の競合する百貨店に対するアンケートデータを用いており，最尤法でモデル推定している．