Capture-Recapture Methods for Data on the Activation of Applications on Mobile Phones

Mamadou Yauck, Louis-Paul Rivest and Greg Rothman

Journal of the American Statistical Association, 2019, vol. 114, issue 525, 105-114

Abstract: This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can create a capture-recapture dataset about devices, that is, users, that “visited” the business: the units are owners of mobile devices and the capture occasions are time intervals such as days. A unit is captured when she activates an application, provided that this activation is recorded by the platform providing the data. Statistical capture-recapture techniques can be applied to the app data to estimate the total number of users that visited the business over a time period, thereby providing an indirect estimate of foot traffic. This article argues that the robust design, a method for dealing with a nested mark-recapture experiment, can be used in this context. A new algorithm for estimating the parameters of a robust design with a fairly large number of capture occasions and a simple parametric bootstrap variance estimator are proposed. Moreover, new estimation methods and new theoretical results are introduced for a wider application of the robust design. This is used to analyze a dataset about the mobile devices that visited the auto-dealerships of a major auto brand in a U.S. metropolitan area over a period of 1 year and a half. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Date: 2019
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DOI: 10.1080/01621459.2018.1469991

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