Mobile AD(D): Estimating Mobile App Session Times for Better Ads
John P. Rula, Byungjin Jun, Fabián E. Bustamante
In Proc. of HotMobile, February 2015.
EECS Department
Northwestern University
Evanston, IL 60201, USA
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Abstract
While mobile advertisements are the dominant source of revenue for mobile apps, the usage patterns of mobile users, and thus their engagement times, may be in conflict with the effectiveness of these ads. With any given application, a user may engage for anywhere between a few seconds to several minutes depending on a number of factors such as their location and goals. Despite the resulting wide-range of session times, the current nature of ad auctions dictates that ads are priced and sold prior to actual viewing, that is regardless of the actual display time.
We argue that the wealth of easy-to-gather contextual information on mobile devices is sufficient to make better choices by effectively predicting exposure time. We analyze mobile device usage patterns with a detailed two-week long user study of 37 users in the US and South Korea. After characterizing application session times, we use factor analysis to derive a simple predictive model and show that this model is able to offer improved accuracy compared to mean session time over 90% of the time. We make the case for including predicted ad exposure duration in the price of mobile advertisements and posit that such information could significantly improve the effectiveness of mobile advertisement, giving publishers the ability to tune campaigns for engagement length and enabling a more efficient market for ad impressions, select appropriate media for an ad impression and lowering the cost to users including network utilization and device power.
