Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing

Clickstream data are defined as the electronic record of Internet usage collected by Web servers or third-party services. The authors discuss the nature of clickstream data, noting key strengths and limitations of these data for research in marketing. The paper reviews major developments from the analysis of these data, covering advances in understanding (1) browsing and site usage behavior on the Internet, (2) the Internet's role and efficacy as a new medium for advertising and persuasion, and (3) shopping behavior on the Internet (i.e., electronic commerce). The authors outline opportunities for new research and highlight several emerging areas likely to grow in future importance. Inherent limitations of clickstream data for understanding and predicting the behavior of Internet users or researching marketing phenomena are also discussed.     

Choice and the Internet: From Clickstream to Research Stream

The authors discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data (the electronic records of Internet usage recorded by company web servers and syndicated data services). The authors compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data), highlighting the differences relevant to choice modelers. Though the application of choice models to clickstream data is relatively new, the authors review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The paper offers directions for further research in these areas and discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation. Notwithstanding the numerous challenges associated with clickstream data research, the authors conclude that the detailed nature of the information tracked about Internet usage and e-commerce transactions presents an enormous opportunity for empirical modelers to enhance the understanding and prediction of choice behavior.     

Identifying multiple preference segments from own- and cross-price elasticities

The authors develop an approach to decompose a market-level matrix of own- and cross-price elasticities to reveal potentially overlapping preference segments. The approach is grounded on the premise that markets may be represented by a parsimonious number of relatively homogeneous segments. Market-level elasticities are expressed as functions of segment weights and within-segment market shares. These relationships permit segment weights and within-segment market shares to be estimated from the market-level elasticity matrix and patterns of brand substitutability to be analyzed. The approach is illustrated with data on the grocery coffee category.The authors wish to thank M/A/R/C Inc., Las Colinas, Texas and Information Resources, Inc., Chicago, Illinois for their assistance in collecting the data used in this study. This research was supported in part by the Dean's Fund for Faculty Research of the Owen School.     

Improving food retailing in developing Asian countries

The adoption of horizontal and vertical integration in retail systems in the marketing of food in developing countries has long been the recommendation of researchers in this field. The author suggests that such proposals may not achieve the desired results for the poorer consumers in low-income Asian countries. An alternative approach, based upon the strengthening and improvement of traditional methods, is suggested as a more appropriate course of action in those countries.     

Competition among European food distribution systems in concentrated markets

This research investigates the quality of competition among food stores in Sweden through a statistical evaluation of prices in two major food markets in the country. The analysis finds mixed results. There is evidence of superior market performance in the passage to the consumer of savings from scale economies and promotional discounts. At the same time indications of weakness were found in the difference in prices levels between major markets with similar costs and competitors employing the same technologies. Public policy recommendations to maintain high levels of competition are derived.     

When worlds collide: The implications of panel data-based choice models for consumer behavior

Panel data, both diary and scanner, have been analyzed by marketing scientists for over thirty years. One of the important uses of panel data is to better understand consumer behavior by developing and testing hypotheses using the revealed preference data rather than experimental data that uses only self-reported behavior or behavior in a simulated choice environment. The purpose of this paper is to suggest areas of research where panel data can be used to better understand the underlying behavior of the panel members.     

From Decision Support to Decision Automation: A 2020 Vision

The authors discuss the long-run future of decision support systems in marketing. They argue that a growing proportion of marketing decisions can not only be supported but may also be automated. From a standpoint of both efficiency (e.g., management productivity) and effectiveness (e.g., resource allocation decisions), such automation is highly desirable. The authors describe how model-based automated decision-making is likely to penetrate various marketing decision-making environments and how such models can incorporate competitive dynamics. For example, the authors foresee that close to full automation can ultimately take place for many decisions about existing products in stable markets. Partial automation could characterize decision making for new products in stable markets and existing products in unstable markets.     

The Co-Diffusion of Complementary Innovations: Supermarket Scanners and UPC Symbols

Co-diffusion is the positive interaction between the demands for complementary innovations that have separate adoption tracks. This interaction is of growing importance to business strategy because a firm can facilitate the diffusion of an innovation by supporting and participating in the development and commercialization of related complementary technologies. This phenomenon is of special importance where connectivity among products is central to end-user value. Louis P. Bucklin and Sanjit Sengupta report results of their empirical investigation of the co-diffusion of laser scanners in supermarkets and Universal Product Code (UPC) symbols printed on the packages of stocked items. Their results show asymmetric two-way co-diffusion effects between these innovations. Co-diffusion effects are found to be stronger than innovation effects. Finally, the analysis reveals differential co-diffusion effects across major market segments.     

Choice in Interactive Environments

In the early 21st century, firms are thinking seriously and practically about an interactive marketing paradigm—one that integrates mass scale with individual responsiveness. The focus of this paper is on how this interactive environment is changing the customer decision-making process. With the increased amount of information available, the existence of sophisticated decision aids such as intelligent agents, and more latitude in how to interact beyond the basic desktop and laptop computers (e.g., personal digital assistants, cellular phones, tablet computers), customers have more choices than ever about how, when, and how much to interact with companies and each other. In this paper, we attempt to cover a few of the major areas of research on how customers make decisions in these environments.