Abstract
Many approaches have been proposed to allow customers’ segmentation in retail sector. However, very few contributions exploit the existing semantics links that may exist between objects and resulting groups. The aim of this paper is to overcome this drawback by using semantic similarity measures (SSM) in customers’ segmentation to provide clusters based on product’ topology instead of numerical indicators usually used (i.e. monetary indicators). More precisely, we intend to show the main advantage of SSM with a product taxonomy in the retail field. Usually, traditional approaches consider as similar three customers buying respectively apple, orange and beer. However, human intuition tends to group customers who buy orange and apple because both are fruits. Our approach is defined to identify this kind of grouping through SSM and abstract concepts belonging to product taxonomy. Experiments are conducted on real data from a French Retailer store and show the relevance of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berrah, L., Mauris, G., Montmain, J.: Monitoring the improvement of an overall industrial performance based on a Choquet integral aggregation. Int. J. Manag. Sci. OMEGA 36, 340–351 (2008)
Griva, A., Bardaki, C., Pramatari, K., Papakiriakopoulos, D.: Retail business analytics: customer visit segmentation using market basket data. Expert Syst. Appl. 16(1), 1–16 (2018)
Ching-Hsue, C., You-Shyang, C.: Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst. Appl. 36(3), 4176–4184 (2009)
Chen, Y.L., Kuo, M.H., Wu, S.Y., Tang, K.: Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electron. Commer. Res. Appl. 8(5), 241–251 (2009)
Khajvand, M., Zolfaghar, K., Ashoori, S., Alizadeh, S.: Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study. Procedia Comput. Sci. 3, 57–63 (2011)
Lingras, P., Elagamy, A., Ammar, A., Elouedi, Z.: Iterative meta-clustering through granular hierarchy of supermarket customers and products. Inf. Sci. 257, 14–31 (2014)
Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: Semantic similarity from natural language and ontology analysis. Synthesis Lectures on Human Language Technologies (2015)
Harispe, S., Snchez, D., Ranwez, S., Janaqi, S., Montmain, J.: A framework for unifying ontology-based semantic similarity. Study in the biomedical domain. J. Biomed. Inform. 48, 38–53 (2014)
Hong, T., Kim, E.: Segmenting customers in online stores based on factors that affect the customers intention to purchase. Expert Syst. Appl. 39(2), 2127–2131 (2012)
Aeron, H., Kumar, A., Moorthy, J.: Data mining framework for customer lifetime value-based segmentation. Expert Syst. Appl. 19(1), 17–30 (2012)
Park, C.H., Park, Y.H., Schweidel, D.A.: A multi-category customer base analysis. Int. J. Res. Mark. 31(3), 266–279 (2014)
Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: The Semantic Measures Library and Toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinformatics 30, 740–742 (2014)
Harispe, S., Imoussaten, A., Trousset, F., Montmain, J.: On the consideration of a bring-to-mind model for computing the Information Content of concepts defined into ontologies. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2015)
Kim, H.K., Kim, J.K., Chen, Q.Y.: A product network analysis for extending the market basket analysis. Expert Syst. Appl. 39(8), 7403–7410 (2012)
lbadvi, A., Shahbazi, M.: A hybrid recommendation technique based on product category attributes. Expert Syst. Appl. 36(9), 11480–11488 (2009)
Pesquita, C., Faria, D., Bastos, H., Falcao, A., Couto, F.: Evaluating go-based semantic similarity measures. In: Proceedings of 10th Annual Bio-Ontologies Meeting, vol. 37, p. 38 (2007)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of IJCAI 1995, pp. 448–453 (1995)
Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138 (1994)
Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in WordNet. In: 16th European Conference on Artificial Intelligence, pp. 1–5 (2004)
Schlicker, A., Domingues, F.S., Rahnenfhrer, J., Lengauer, T.: A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinform. 7, 302 (2006)
Murtagh, F.: Wards hierarchical agglomerative clustering method: which algorithms implement wards criterion? J. Classif. 31, 274–295 (2014)
Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22, 31–72 (2011)
Srikant, R., Agrawal, R.: Mining generalized association rules. Future Gener. Comput. Syst. 13, 161–180 (1997)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Poncelet, J., Jean, PA., Trousset, F., Montmain, J. (2019). Semantic Customers’ Segmentation. In: El Yacoubi, S., Bagnoli, F., Pacini, G. (eds) Internet Science. INSCI 2019. Lecture Notes in Computer Science(), vol 11938. Springer, Cham. https://doi.org/10.1007/978-3-030-34770-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-34770-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34769-7
Online ISBN: 978-3-030-34770-3
eBook Packages: Computer ScienceComputer Science (R0)