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Explaining the product range effect in purchase data

In our market society, buyers are considered rational entities, driven by two utility functions: i) the amount of money spent, a universal quantity to be minimized; and ii) the individual needs to satisfy, a personal quantity, varying from person to person, to be maximized. In this paper, we propose an analytic framework based on big data to measure the personal utility function and we prove that this function has a stronger effect on customer behavior than the price. By focusing on the purchases in an Italian supermarket chain, we discover and describe a range effect of products: the more sophisticated the needs they satisfy, the more cost the customers are willing to pay to buy them, in terms of distance to travel more than in terms of the price of the item itself. We exhibit a striking empirical evidence of this theory by tracking the geographical information about points of sale and customers, in a large dataset containing tens of thousands of customers and thousands of products. We create a data mining framework able to scale to possibly hundreds of thousands, or millions, of customers and to let emerge from the data the knowledge about the actual range of each product. As an application of this finding, we show how it is possible to accurately predict how long a customer will travel (or which shop she will choose) to buy a product, as a function of the product's sophistication.