A SEGMENTATION ANALYSIS OF U.S. GROCERY STORE SHOPPERS

Cluster analysis was used to conduct a segmentation analysis of U.S. supermarket shoppers. This study is based on the responses of a sample of 1,000 shoppers concerning the importance of 21 store characteristics in selecting their primary grocery store for the Food Marketing Institute's 2000 consumer trends survey. Stores must satisfy the attributes important to all consumers in order to be successful. In order of importance, the four top characteristics are a clean/neat store, high quality produce, high quality meats and courteous, friendly employees. The three key supermarket shopper segments identified are time-pressed convenience seekers, sophisticates, and middle Americans. In order to cater to a particular consumer niche, a store must better fulfill the store preferences of that segment. Time-pressed convenience seekers, 36.70 percent of the sample, put a premium on features such as childcare, gas pumps and online shopping. They are likely to be younger, urban with lower or moderate incomes and have the greatest number of children six years old or younger. Quality and services are important to the sophisticates, 28.40 percent of the sample. This group is middle-aged, better educated with higher incomes than average. Middle Americans, 34.90 percent, are attracted by pricing/value factors such as frequent shopper programs, sales and private label brands. They want stores that are active in the community. Demographically they are in the middle with the highest proportion of high school graduates.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Marketing,

DATA MINING: A SEGMENTATION ANALYSIS OF U.S. GROCERY SHOPPERS

Consumers make choices about where to shop based on their preferences for a shopping environment and experience as well as the selection of products at a particular store. This study illustrates how retail firms and marketing analysts can utilize data mining techniques to better understand customer profiles and behavior. Among the key areas where data mining can produce new knowledge is the segmentation of customer data bases according to demographics, buying patterns, geographics, attitudes, and other variables. This paper builds profiles of grocery shoppers based on their preferences for 33 retail grocery store characteristics. The data are from a representative, nationwide sample of 900 supermarket shoppers collected in 1999. Six customer profiles are found to exist, including (1) "Time Pressed Meat Eaters", (2) "Back to Nature Shoppers", (3) "Discriminating Leisure Shoppers", (4) "No Nonsense Shoppers", (5) "The One Stop Socialites", and (6) "Middle of the Road Shoppers". Each of the customer profiles is described with respect to the underlying demographics and income. Consumer shopping segments cut across most demographic groups but are somewhat correlated with income. Hierarchical lists of preferences reveal that low price is not among the top five most important store characteristics. Experience and preferences for internet shopping shows that of the 44% who have access to the internet, only 3% had used it to order food.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

Mining Consumer Knowledge from Shopping Experience: A case study on Indian E_Commerce Industry

E_Commerce becomes far much popular in recent years. E Commerce nowadays is almost everywhere. People go through online ; meanwhile, they are more and more accustomed to buy goods via E_Commerce channel. - The E-Commerce web sites are facing lots of problems today. Customers prefer traditional way to purchase the products and not from E-Commerce web sites. If we see the history of E-Commerce, then we get that E-Commerce is the purpose of Internet and the web to conduct business Even in recession, it is thriving and has become one of the most important consumption modes. This study uses cluster analysis to identify the profiles of E_Commerce consumers. The rules between E_Commerce spokespersons and commodities from consumers are recognized by using association analysis. Depicting the marketing knowledge map of spokespersons, the best endorsement portfolio is found out to make recommendations. By the analysis of spokespersons, period, customer profiles and products, four business modes of E_Commerce are proposed for consumers: new product, knowledge, low price and luxury product; the related recommendations are also provided for the industry reference

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

EXPERIENTIAL VALUE: A HIERARCHICAL MODEL, THE IMPACT ON E-LOYALTY AND A CUSTOMER TYPOLOGY

The main objective of this study is to empirically test a fourth-order hierarchical model of experiential value in an online book and CD setting. In addition, we provide empirical evidence for the role of hedonic and utilitarian value components in creating attitudinal and behavioral loyalty. Finally, we develop an online customer typology, based on the underlying value sources. Based on a sample of 190 visitors of online book and CD retailers, we used PLS to test a third and fourth order hierarchical model of experiential value, emphasizing a hedonic (intrinsic) and utilitarian (extrinsic) value component and the existence of the holistic concept of experiential value. Our results demonstrate that experiential value consists of the third order components hedonic (intrinsic) and utilitarian (extrinsic) value. Both value aspects impact attitudinal loyalty ultimately leading to behavioral loyalty which is also directly affected by utilitarian value. Finally, a nonhierarchical (k-means) cluster analysis identified four segments of online visitors: hedonists, utilitarians, active negativists, and reactive positivists.marketing ;