Who will be affected by supermarket health programs?: Tracking customer behavior changes via preference modeling

Ling Luo; Bin Li; Shlomo Berkovsky; Irena Koprinska; Fang Chen

doi:10.1007/978-3-319-31753-3_42

Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling

Ling Luo, Bin Li, Shlomo Berkovsky, Irena Koprinska, Fang Chen

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

4 Citations (Scopus)

Abstract

As obesity has become a worldwide problem, a number of health programs have been designed to encourage participants to maintain a healthier lifestyle. The stakeholders often desire to know how effective the programs are and how to target the right participants. Motivated by a real-life health program conducted by an Australian supermarket chain, we propose a novel method to track customer behavior changes induced by the program and investigate the program’s effect on different segments of customers, split according to demographic factors like age and gender. The method: (1) derives customer preferences from the transaction data, (2) captures the customer behavior changes via a temporal model, (3) analyzes the program effectiveness on different customer segments, and (4) evaluates the program influence using a one-year data set obtained from a major Australian supermarket. Our results indicate that while overall the program had positive effect in encouraging customers to buy healthy food, its impact varied for the different customer segments. These results can inform the design of personalized health programs that target specific customers in the future and benefit more people. Our method can also be applied to other programs that use transaction data and customer profiles.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	20th Pacific-Asia Conference, PAKDD 2016. Proceedings, Part I
Editors	James Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	527-539
Number of pages	13
ISBN (Electronic)	9783319317533
ISBN (Print)	9783319317526
DOIs	https://doi.org/10.1007/978-3-319-31753-3_42
Publication status	Published - 2016
Externally published	Yes
Event	20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand Duration: 19 Apr 2016 → 22 Apr 2016

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	9651
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Country/Territory	New Zealand
City	Auckland
Period	19/04/16 → 22/04/16

Keywords

Customer behaviors
Temporal preference modeling
Health programs
Shopping data analysis
Customer behaviours
Temporal preference modelling

Access to Document

10.1007/978-3-319-31753-3_42

Cite this

Luo, L., Li, B., Berkovsky, S., Koprinska, I., & Chen, F. (2016). Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling. In J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang, & R. Wang (Eds.), Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. Proceedings, Part I (pp. 527-539). (Lecture Notes in Artificial Intelligence; Vol. 9651). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-31753-3_42

Luo, Ling ; Li, Bin ; Berkovsky, Shlomo et al. / Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling. Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. Proceedings, Part I. editor / James Bailey ; Latifur Khan ; Takashi Washio ; Gillian Dobbie ; Joshua Zhexue Huang ; Ruili Wang. Cham : Springer, Springer Nature, 2016. pp. 527-539 (Lecture Notes in Artificial Intelligence).

@inproceedings{7119ed75fc8b43449743217949e9d682,

title = "Who will be affected by supermarket health programs?: Tracking customer behavior changes via preference modeling",

abstract = "As obesity has become a worldwide problem, a number of health programs have been designed to encourage participants to maintain a healthier lifestyle. The stakeholders often desire to know how effective the programs are and how to target the right participants. Motivated by a real-life health program conducted by an Australian supermarket chain, we propose a novel method to track customer behavior changes induced by the program and investigate the program{\textquoteright}s effect on different segments of customers, split according to demographic factors like age and gender. The method: (1) derives customer preferences from the transaction data, (2) captures the customer behavior changes via a temporal model, (3) analyzes the program effectiveness on different customer segments, and (4) evaluates the program influence using a one-year data set obtained from a major Australian supermarket. Our results indicate that while overall the program had positive effect in encouraging customers to buy healthy food, its impact varied for the different customer segments. These results can inform the design of personalized health programs that target specific customers in the future and benefit more people. Our method can also be applied to other programs that use transaction data and customer profiles.",

keywords = "Customer behaviors, Temporal preference modeling, Health programs, Shopping data analysis, Customer behaviours, Temporal preference modelling",

author = "Ling Luo and Bin Li and Shlomo Berkovsky and Irena Koprinska and Fang Chen",

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doi = "10.1007/978-3-319-31753-3_42",

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Luo, L, Li, B, Berkovsky, S, Koprinska, I & Chen, F 2016, Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling. in J Bailey, L Khan, T Washio, G Dobbie, JZ Huang & R Wang (eds), Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. Proceedings, Part I. Lecture Notes in Artificial Intelligence, vol. 9651, Springer, Springer Nature, Cham, pp. 527-539, 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, Auckland, New Zealand, 19/04/16. https://doi.org/10.1007/978-3-319-31753-3_42

Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling. / Luo, Ling; Li, Bin; Berkovsky, Shlomo et al.
Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. Proceedings, Part I. ed. / James Bailey; Latifur Khan; Takashi Washio; Gillian Dobbie; Joshua Zhexue Huang; Ruili Wang. Cham: Springer, Springer Nature, 2016. p. 527-539 (Lecture Notes in Artificial Intelligence; Vol. 9651).

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

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A2 - Washio, Takashi

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Luo L, Li B, Berkovsky S, Koprinska I, Chen F. Who will be affected by supermarket health programs? Tracking customer behavior changes via preference modeling. In Bailey J, Khan L, Washio T, Dobbie G, Huang JZ, Wang R, editors, Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016. Proceedings, Part I. Cham: Springer, Springer Nature. 2016. p. 527-539. (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-319-31753-3_42