Understanding Consumer Behavior by Big Data Visualization in the Smart Space Laboratory

In this paper, we describe a proof-of-concept (PoC) methodology to understand consumer behavior and spending pattern via visualization analysis in a custom-made smart space laboratory. This laboratory simulates the real-world shopping environment, allows big data generation and collection from various kinds of shopping activities. Data were captured from the service users who are having their technical and business training in a controlled setting environment. Consumer behavior modeling will be described, technical detail such as environment construction, theory, logic, framework, infrastructure, and architecture will also be discussed in this paper. Preliminary results showed that both "holding time" and the "frequency on the spots" have a certain relationship to the purchase decision which made by the consumer (i.e. service user in the laboratory): the longer stay time where the service user is located, the higher chances that the product(s) will be purchased.