TY - GEN
T1 - Understanding Consumer Behavior by Big Data Visualization in the Smart Space Laboratory
AU - Yau, Peter Chun Yu
AU - Wong, Dennis
AU - Luen, Woo Hok
AU - Leung, Joseph
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/5/28
Y1 - 2020/5/28
N2 - 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.
AB - 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.
KW - Big data
KW - Consumer behavior
KW - Heat map
KW - Internet of Things (IoT)
KW - Smart Space
KW - Spending pattern
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85090339411&partnerID=8YFLogxK
U2 - 10.1145/3404687.3404705
DO - 10.1145/3404687.3404705
M3 - Conference contribution
AN - SCOPUS:85090339411
T3 - ACM International Conference Proceeding Series
SP - 13
EP - 17
BT - Proceedings of the 2020 5th International Conference on Big Data and Computing, ICBDC 2020
PB - Association for Computing Machinery
T2 - 5th International Conference on Big Data and Computing, ICBDC 2020
Y2 - 28 May 2020 through 30 May 2020
ER -
