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.
Original language | English |
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Title of host publication | CIKM '16 Proceedings of the 25th ACM international on conference on information and knowledge management |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 2197-2202 |
Number of pages | 6 |
ISBN (Electronic) | 9781450340731 |
DOIs | |
Publication status | Published - 24 Oct 2016 |
Externally published | Yes |
Event | 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: 24 Oct 2016 → 28 Oct 2016 |
Other
Other | 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 |
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Country/Territory | United States |
City | Indianapolis |
Period | 24/10/16 → 28/10/16 |
Keywords
- customer segmentation
- customer behaviors
- temporal modeling
- non-homogeneous Poisson process