The predictability of consumer visitation patterns
Author(s)Llorente, Alejandro; Cebrian, Manuel; Moro, Esteban; Krumme, Katherine Ann; Pentland, Alex Paul
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We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population.
DepartmentMassachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Nature Publishing Group
Krumme, Coco, Alejandro Llorente, Manuel Cebrian, Alex (“Sandy”) Pentland, and Esteban Moro. “The Predictability of Consumer Visitation Patterns.” Sci. Rep. 3 (April 18, 2013).
Final published version