The predictability of consumer visitation patterns

TL;DR

This study analyzes millions of consumer transactions to assess the predictability of individual merchant visitation patterns, revealing high long-term predictability with stochastic short-term variations, and demonstrating the effectiveness of Markov models.

Contribution

It provides a large-scale analysis of consumer visitation predictability, introducing models that incorporate population-level transition probabilities to improve accuracy.

Findings

Long-term visitation patterns are highly predictable.

Short-term behavior shows stochastic variability.

Population-level models enhance prediction accuracy.

Abstract

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.