Abstract
In the retail context, there is an increasing need for understanding individual customer behavior in order to personalize marketing actions. We propose the novel concept of customer signature, that identifies a set of important products that the customer refills regularly. Both the set of products and the refilling time periods give new insights on the customer behavior. Our approach is inspired by methods from the domain of sequence segmentation, thus benefiting from efficient exact and approximate algorithms. Experiments on a real massive retail dataset show the interest of the signatures for understanding individual customers.
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Notes
- 1.
For the sake of simplicity, we focus here on a bitmap representation. To cope with memory consumption, a more efficient representation method, such as the dynamic bit vector (DBV) architecture, could be used [13].
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Gautrais, C., Quiniou, R., Cellier, P., Guyet, T., Termier, A. (2017). Purchase Signatures of Retail Customers. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_9
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DOI: https://doi.org/10.1007/978-3-319-57454-7_9
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