Association for Behavior Analysis International

Consumer behavior analysis draws on behavior analysis, behavioral ecology, behavioral economics, and marketing science to further enhance the understanding of all aspects of consumption. New technologies such as in-store analytics, improved eye tracking, customer feedback software tools and targeted, measurable, and interactive digital media are not only changing the face of the retail landscape but are also a relatively untapped opportunity in this discipline. Current activities in the digital marketplace are generating immense amounts of techniques, tools and behavioral data that can offer possibilities for more detailed analyses of consumer-marketing relationships from a behavior analytical viewpoint. In this symposium we will discuss recent theoretical developments and empirical analyses related to how consumers learn to adapt to highly competitive economic environments. The symposium starts with a theoretical paper on the relevance of consumer behavior analysis to digital marketing in the context of the Behavioral Perspective Model. The second paper explores the use of digital technology in scrutinizing in-store customer foraging in its natural surroundings focusing on key customer touch-points. The third study investigates the impact of online customer ratings on online hotel booking using the concept of probability discounting. The final paper uses behavioral conjoint as a methodology to study consumer trade-offs and reinforcement value maximization in online retailing.

The paper discusses the need for a thorough understanding of consumer choice of a product carrying equipment (e.g. cart, basket or nothing) as a key customer touch-point at the beginning of a customer journey in grocery retailing. A four-term contingency framework with conversion rate modeling was used, and the data consisted of actual choice behavior detected through video-surveillance. In-store antecedents such as consumers' shopping goals and carrying equipment positioning were analyzed and manipulated while random consumers were observed individually from the point they entered the store to all the way to their exit. The measurements involved consequences of different in-store antecedents (goals and equipment) on in-store behaviors such as walking speed, number of purchases per minute, time spent on different zones (e.g., in the fruit section) and the proportion of healthy food in the total shopping. The data was analyzed using a Shopper Flow Tracking System where the software is designed both to give automatic data on shopper behavior and to assist human observers in tracking individual shopping trips. We discuss behavioral classifications, methodology, validity and implications related to the data from the consumer tracking efforts.