research-article

Discovering Temporal Purchase Patterns with Different Responses to Promotions

Authors:

Irena Koprinska,

Shlomo Berkovsky,

Fang ChenAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2197 - 2202

https://doi.org/10.1145/2983323.2983665

Published: 24 October 2016 Publication History

Abstract

The supermarkets often use sales promotions to attract customers and create brand loyalty. They would often like to know if their promotions are effective for various customers, so that better timing and more suitable rate can be planned in the future. Given a transaction data set collected by an Australian national supermarket chain, in this paper we conduct a case study aimed at discovering customers' long-term purchase patterns, which may be induced by preference changes, as well as short-term purchase patterns, which may be induced by promotions. Since purchase events of individual customers may be too sparse to model, we propose to discover a number of latent purchase patterns from the data. The latent purchase patterns are modeled via a mixture of non-homogeneous Poisson processes where each Poisson intensity function is composed by long-term and short-term components. Through the case study, 1) we validate that our model can accurately estimate the occurrences of purchase events; 2) we discover easy-to-interpret long-term gradual changes and short-term periodic changes in different customer groups; 3) we identify the customers who are receptive to promotions through the correlation between behavior patterns and the promotions, which is particularly worthwhile for target marketing.

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Cited By

Bogina VKuflik TJannach DBielikova MKompan MTrattner C(2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
https://doi.org/10.1007/s11257-022-09335-w
Luo LLi BFan XWang YKoprinska IChen F(2022)Dynamic customer segmentation via hierarchical fragmentation-coagulation processesMachine Learning10.1007/s10994-022-06276-8112:1(281-310)Online publication date: 2-Dec-2022
https://doi.org/10.1007/s10994-022-06276-8
Chibah AAmer-Yahia SBerti-Equille L(2021)QeNoBi: A System for QuErying and mining BehavIoral Patterns2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00301(2673-2676)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00301
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Index Terms

Discovering Temporal Purchase Patterns with Different Responses to Promotions

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Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 October 2016

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October 24 - 28, 2016

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Cited By

Bogina VKuflik TJannach DBielikova MKompan MTrattner C(2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
https://doi.org/10.1007/s11257-022-09335-w
Luo LLi BFan XWang YKoprinska IChen F(2022)Dynamic customer segmentation via hierarchical fragmentation-coagulation processesMachine Learning10.1007/s10994-022-06276-8112:1(281-310)Online publication date: 2-Dec-2022
https://doi.org/10.1007/s10994-022-06276-8
Chibah AAmer-Yahia SBerti-Equille L(2021)QeNoBi: A System for QuErying and mining BehavIoral Patterns2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00301(2673-2676)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00301
Zheng HLuo LRistanoski G(2021)A Clustering-Prediction Pipeline for Customer Churn AnalysisKnowledge Science, Engineering and Management10.1007/978-3-030-82153-1_7(75-84)Online publication date: 7-Aug-2021
https://doi.org/10.1007/978-3-030-82153-1_7
Guan MCha MLi YWang YSun J(2020)From Anticipation to Action: Data Reveal Mobile Shopping Patterns During a Yearly Mega Sale Event in ChinaIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.3001558(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2020.3001558
Zhang SLuo LLi ZWang YChen FXu R(2020)Simultaneous Customer Segmentation and Behavior DiscoveryNeural Information Processing10.1007/978-3-030-63820-7_14(122-130)Online publication date: 17-Nov-2020
https://doi.org/10.1007/978-3-030-63820-7_14
Zhang YLuo LWang YWang Z(2020)FCP Filter: A Dynamic Clustering-Prediction Framework for Customer BehaviorAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_45(580-591)Online publication date: 6-May-2020
https://doi.org/10.1007/978-3-030-47426-3_45
Luo LLuo L(2019)Tracking the Evolution of Customer SegmentationsTemporal Modelling of Customer Behaviour10.1007/978-3-030-18289-2_7(95-117)Online publication date: 28-Apr-2019
https://doi.org/10.1007/978-3-030-18289-2_7
Luo LLuo L(2019)IntroductionTemporal Modelling of Customer Behaviour10.1007/978-3-030-18289-2_1(1-6)Online publication date: 28-Apr-2019
https://doi.org/10.1007/978-3-030-18289-2_1
Guidotti RGabrielli LMonreale APedreschi DGiannotti F(2018)Discovering temporal regularities in retail customers’ shopping behaviorEPJ Data Science10.1140/epjds/s13688-018-0133-07:1Online publication date: 6-Mar-2018
https://doi.org/10.1140/epjds/s13688-018-0133-0
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