Prediction of Consumers' Intension Through Their Behavior Observation in Ubiquitous Shop Space

Abstract

This paper has studied and observed the consumer behavior by collecting all kinds of actions of consumers and applying the ubiquitous environment which are RFID and camera sensors to gathering log data. The consumer's behavior was automatically saved as a log file and analyzed by using artificial neural network. From neural network model allows us to categorize theconsumers into 3 groups which are: A) the consumers who were certain in buying a product, B) the consumers who intended to buy a product but could not decide what to buy, and C) the consumers who did not intend to buy but stop by to view the product.

Kansei Robotics: Bridging Human Beings and Electronic Gadgets through Kansei Engineering

“…We also estimated the degree of dominant attributes based on the answer. 4. Each recommendation products of the Smart Shop and the questionnaire data obtained by the degree of dominant attributes.…”

“…Thus, we can statistically analyze a degree of interest on objects by each person's behavior log [4], [5], [6].…”

Modeling supermarket re-layout from the owner’s perspective

“…In the last decade, descriptive methods of customer movements within shops have developed rapidly, based on the application of radio frequency identification (RFID) systems (Larson et al 2005), and ubiquitous sensors have been widely used to collect information of shopper behavior (Sae-Ueng et al 2008;Tanaka et al 2007).…”