The retail market as a complex system
Pennacchioli D., Coscia M., Rinzivillo S., Giannotti F., Pedreschi D.
Modeling and Simulation Computational Mathematics Computer Science Applications Complex Networks D.2.8 SOFTWARE ENGINEERING. Metrics Data Mining H.2.8 Database Applications
Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country's GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products' volumes of sales with the customers' volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.
Source: EPJ 3 (2014). doi:10.1140/epjds/s13688-014-0033-x
Publisher: Spring Open Journal
2. Sun Y, Aggarwal CC, Han J (2012) Relation strength-aware clustering of heterogeneous information networks with incomplete attributes. Proc VLDB Endow 5(5):394-405
3. Chaudhuri S, Narasayya VR (2011) New frontiers in business intelligence. Proc VLDB Endow 4(12):1502-1503
4. Kocakoç ID, Erdem S (2010) Business intelligence applications in retail business: OLAP, data mining & reporting services. J Inf Knowl Manag 9(2):171-181
5. Brauckhoff D, Dimitropoulos X, Wagner A, Salamatian K (2012) Anomaly extraction in backbone networks using association rules. IEEE/ACM Trans Netw 20(6):1788-1799
6. Marinica C, Guillet F (2010) Knowledge-based interactive postmining of association rules using ontologies. IEEE Trans Knowl Data Eng 22(6):784-797
7. Montella A (2011) Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types. Accid Anal Prev 43(4):1451-1463
8. Hidalgo CA, Klinger B, Barabási AL, Hausmann R (2007) The product space conditions the development of nations. Science 317(5837):482-487. doi:10.1126/science.1144581
9. Hausmann R, Hidalgo C, Bustos S, Coscia M, Chung S, Jimenez J, Simoes A, Yildirim M (2011) The atlas of economic complexity. Boston, USA
10. Caldarelli G, Cristelli M, Gabrielli A, Pietronero L, Scala A, Tacchella A (2011) Ranking and clustering countries and their products; a network analysis. arXiv:1108.2590
11. Davis WL, IV, Schwarz P, Terzi E (2009) Finding representative association rules from large rule collections. In: SDM, pp 521-532
12. Maslow AH (1943) A theory of human motivation. Psychol Rev 50(4):370-396
13. Bascompte J, Jordano P, Melián CJ, Olesen JM (2003) The nested assembly of plant-animal mutualistic networks. Proc Natl Acad Sci USA 100(16):9383-9387. doi:10.1073/pnas.1633576100
14. Almeida-Neto M, Guimarães P, Guimarães PR, Jr., Loyola RD, Ulrich W (2008) A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117:1227-1239. doi:10.1111/j.0030-1299.2008.16644.x
15. Pennacchioli D, Coscia M, Giannotti F, Pedreschi D (2013) Calculating product and customer sophistication on a large transactional dataset. Technical report cnr.isti/2013-TR-004
16. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431-441
17. Hidalgo CA, Hausmann R (2009) The building blocks of economic complexity. Proc Natl Acad Sci USA 106(26):10570-10575. doi:10.1073/pnas.0900943106
18. Cristelli M, Gabrielli A, Tacchella A, Caldarelli G, Pietronero L (2013) Measuring the intangibles: a metrics for the economic complexity of countries and products. PLoS ONE 8(8):e70726
19. Guidotti R (2013) Mobility ranking - human mobility analysis using ranking measures. University of Pisa
20. Wang H, Song M (2011) Ckmeans.1d.dp: optimal k-means clustering in one dimension by dynamic programming. R J 3(2):29-33
21. Pennacchioli D, Coscia M, Rinzivillo S, Pedreschi D, Giannotti F (2013) Explaining the product range effect in purchase data. In: 2013 IEEE international conference on big data, pp 648-656
22. Krumme C, Llorente A, Cebrián M, Pentland A, Egido EM (2013) The predictability of consumer visitation patterns. CoRR. arXiv:abs/1305.1120
23. Cohen E, Datar M, Fujiwara S, Gionis A, Indyk P, Motwani R, Ullman JD, Yang C (2000) Finding interesting associations without support pruning. In: ICDE, pp 489-500
24. Nguyen K-N, Cerf L, Plantevit M, Boulicaut J-F (2011) Multidimensional association rules in Boolean tensors. In: SDM, pp 570-581
25. Chawla S (2010) Feature selection, association rules network and theory building. J Mach Learn Res 10:14-21
26. Pennacchioli D, Coscia M, Pedreschi D (2014) Overlap versus partition: marketing classification and customer profiling in complex networks of products. In: Workshop of the international conference of data engineering (ICDE)
27. Li H (2005) Applications of data warehousing and data mining in the retail industry. In: Proceedings of ICSSSM'05: 2005 international conference on services systems and services management, vol 2
28. Gabbur P, Pankanti S, Fan Q, Trinh H (2011) A pattern discovery approach to retail fraud detection. In: KDD, pp 307-315
29. Wagner MM, Robinson JM, Tsui F-C, Espino JU, Hogan WR (2003) Design of a national retail data monitor for public health surveillance. J Am Med Inform Assoc 10(5):409-418
30. Castellanos M, Dayal U, Hsu M, Ghosh R, Dekhil M, Lu Y, Zhang L, Schreiman M (2011) LCI: a social channel analysis platform for live customer intelligence. In: SIGMOD conference, pp 1049-1058
31. Balassa B (1965) Trade liberalization and 'revealed' comparative advantage. Manch Sch 33:99-123
32. Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv 38(3):9. doi:10.1145/1132960.1132963
33. Bousquet N (2010) Eliciting vague but proper maximal entropy priors in Bayesian experiments. Stat Pap 51(3):613-628
34. Shen Z-JM, Su X (2007) Customer behavior modeling in revenue management and auctions: a review and new research opportunities. Prod Oper Manag 16(6):713-728. doi:10.1111/j.1937-5956.2007.tb00291.x
35. Schich M, Lehmann S, Park J (2008) Dissecting the canon: visual subject co-popularity networks in art research. In: ECCS2008
36. Liu Y-Y, Slotine J-J, Barabási A-L (2011) Controllability of complex networks. Nature 473(7346):167-173. doi:10.1038/nature10011
37. Patefield WM (1981) An efficient method of generating random RxC tables with given row and column totals (algorithm AS 159). J R Stat Soc, Ser C, Appl Stat 30:91-97. doi:10.2307/2346669
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@article{oai:it.cnr:prodotti:303377,
title = {The retail market as a complex system},
author = {Pennacchioli D. and Coscia M. and Rinzivillo S. and Giannotti F. and Pedreschi D.},
publisher = {Spring Open Journal},
doi = {10.1140/epjds/s13688-014-0033-x},
journal = {EPJ},
volume = {3},
year = {2014}
}
0000-0001-5984-5137
