The predictability of consumer visitation patterns

Krumme, Coco; Llorente, Alejandro; Cebrián, Manuel; Pentland, Alex; Moro, Esteban

doi:10.1038/srep01645

Cited by 84 publications

(63 citation statements)

References 28 publications

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“…In [19] the authors apply the Markov models to WiFi traces at Darthmouth campus and find that the best performing model is order 2 and has a median accuracy of about 65-72%. Krumme et al [20] applied Markov models on shopping locations inferred from credit card transactions, and found that models trained on the aggregate data from a large number of people perform better than individual models. Bapierre et al [21] applied a variable-order Markov chain to the Reality Mining [6] and Geolife [22] datasets.…”

Section: Related Workmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

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Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors -in terms of modeling approaches and spatio-temporal characteristics of the data sources -have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.

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Section: Related Workmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

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“…Yet, they exploit the temporal dimension to explain the purchasing patterns of the products at a global scale and their outcomes do not explain the customers behavior with respect to time. The temporal dimension of purchasing habits is exploited in [11] to understand how predictable are consumers in their merchant visitation patterns by using a Markov model for predicting the customer's next shop location. Note that in these works the customer behavior is generalized at global level, while our model is personal and describes the customer habits and preferences in a concise way.…”

Section: Related Workmentioning

confidence: 99%

Discovering temporal regularities in retail customers’ shopping behavior

et al. 2018

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In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer's temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.

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“…Desgraciadamente los trabajos de investigación sobre el comportamiento humano basados en este tipo de registros son todavía escasos, debido a la dificultad para acceder a estos datos. Krumme et al (2013) utilizaron datos de tarjetas de crédito para predecir las pautas de comportamiento de los consumidores. Sobolevsky et al, (2014a y 2015a) partieron de datos de los registros de transacciones con tarjetas de crédito de un gran banco para determinar el atractivo de cada una de las ciudades españolas.…”

Section: Registros De Transacciones De Tarjetas Bancariasunclassified