PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pennacchioli D., Coscia M., Rinzivillo S., Giannotti F., Pedreschi D. The retail market as a complex system. In: EPJ Data Science, vol. 3 (1) article n. 33. Springer Open Journal, 2014.
 
 
Abstract
(English)
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.
URL: http://www.epjdatascience.com/content/3/1/33
DOI: 10.1140/epjds/s13688-014-0033-x
Subject Complex Networks
Data Mining
H.2.8 Database Applications
D.2.8 SOFTWARE ENGINEERING. Metrics
68U99


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