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

Peter Chun Yu Yau; Dennis Wong; Woo Hok Luen; Joseph Leung

doi:10.1145/3404687.3404705

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

Peter Chun Yu Yau
, Dennis Wong
, Woo Hok Luen
, Joseph Leung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2020 5th International Conference on Big Data and Computing, ICBDC 2020
Publisher	Association for Computing Machinery
Pages	13-17
Number of pages	5
ISBN (Electronic)	9781450375474
DOIs	https://doi.org/10.1145/3404687.3404705
Publication status	Published - 28 May 2020
Externally published	Yes
Event	5th International Conference on Big Data and Computing, ICBDC 2020 - Virtual, Online, China Duration: 28 May 2020 → 30 May 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th International Conference on Big Data and Computing, ICBDC 2020
Country/Territory	China
City	Virtual, Online
Period	28/05/20 → 30/05/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 12 Responsible Consumption and Production

Keywords

Big data
Consumer behavior
Heat map
Internet of Things (IoT)
Smart Space
Spending pattern
Visualization

Access to Document

10.1145/3404687.3404705

Cite this

Yau, P. C. Y., Wong, D., Luen, W. H., & Leung, J. (2020). Understanding Consumer Behavior by Big Data Visualization in the Smart Space Laboratory. In Proceedings of the 2020 5th International Conference on Big Data and Computing, ICBDC 2020 (pp. 13-17). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3404687.3404705

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title = "Understanding Consumer Behavior by Big Data Visualization in the Smart Space Laboratory",

abstract = "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.",

keywords = "Big data, Consumer behavior, Heat map, Internet of Things (IoT), Smart Space, Spending pattern, Visualization",

author = "Yau, \{Peter Chun Yu\} and Dennis Wong and Luen, \{Woo Hok\} and Joseph Leung",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 5th International Conference on Big Data and Computing, ICBDC 2020 ; Conference date: 28-05-2020 Through 30-05-2020",

year = "2020",

month = may,

day = "28",

doi = "10.1145/3404687.3404705",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

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Yau, PCY, Wong, D, Luen, WH & Leung, J 2020, Understanding Consumer Behavior by Big Data Visualization in the Smart Space Laboratory. in Proceedings of the 2020 5th International Conference on Big Data and Computing, ICBDC 2020. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 13-17, 5th International Conference on Big Data and Computing, ICBDC 2020, Virtual, Online, China, 28/05/20. https://doi.org/10.1145/3404687.3404705

Understanding Consumer Behavior by Big Data Visualization in the Smart Space Laboratory. / Yau, Peter Chun Yu; Wong, Dennis; Luen, Woo Hok et al.
Proceedings of the 2020 5th International Conference on Big Data and Computing, ICBDC 2020. Association for Computing Machinery, 2020. p. 13-17 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85090339411

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DO - 10.1145/3404687.3404705

M3 - Conference contribution

AN - SCOPUS:85090339411

T3 - ACM International Conference Proceeding Series

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BT - Proceedings of the 2020 5th International Conference on Big Data and Computing, ICBDC 2020

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Y2 - 28 May 2020 through 30 May 2020

ER -

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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