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Characterizing Mobile In-App Targeted Ads

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Characterizing Mobile In-App Targeted Ads MAdScope: Characterizing Mobile In-App Targeted Ads Suman Nath Microsoft Research [email protected] ABSTRACT 1. INTRODUCTION Advertising is the primary source of revenue for many mo- In-app advertising is a key economic driver in mobile app bile apps. One important goal of the ad delivery process is ecosystem, funding a wide variety of free apps. Gartner targeting users, based on criteria like users' geolocation, con- predicts the mobile ad market to grow to $13.5 billion in text, demographics, long-term behavior, etc. In this paper 2015 [9]. The growth is primarily due to increasing engage- we report an in-depth study that broadly characterizes what ment of users with apps|users are reported to spend signif- targeting information mobile apps send to ad networks and icantly more time on mobile apps than on traditional web, how effectively, if at all, ad networks utilize the information and the gap is increasing [6]. for targeting users. Our study is based on a novel tool, called Of central importance in the ad delivery process is targeting| MAdScope, that can (1) quickly harvest ads from a large col- the process of selecting specific ads to display to a given user lection of apps, (2) systematically probe an ad network to of an app. In-app ad controls, which fetch ads from backend characterize its targeting mechanism, and (3) emulate user ad networks and display them to users, target users based profiles of specific preferences and interests to study behav- on criteria such as app type, device attributes (e.g., screen ioral targeting. Our analysis of 500K ad requests from 150K size), user's long-term behavior, demographic, and geoloca- Android apps and 101 ad networks indicates that apps do tion. The presumption is that targeting benefits all parties not yet exploit the full potential of targeting: even though in the ad ecosystem: users, app developers, ad networks, ad controls provide APIs to send a lot of information to ad and advertisers. Note that targeting is not unique to in- networks, much key targeting information is optional and is app ads; it has been successfully used for in-browser ads as often not provided by app developers. We also use MAd- well [1, 8, 11, 30]. Scope to systematically probe top 10 in-app ad networks Despite its importance, in-app ad targeting is relatively ill- to harvest over 1 million ads and find that while targeting explored (compared to its in-browser counterpart). Existing is used by many of the top networks, there remain many results on in-browser ads (e.g., [1,3,7,8,11,23,30]) cannot be instances where targeting information or behavioral profile directly generalized to in-app ads for several reasons. First, does not have a statistically significant impact on how ads unlike in-browser ads that cannot easily access user infor- are chosen. We also contrast our findings with a recent study mation because of the same origin policy [29], in-app ads of targeted in-browser ads. run with the same permission as the app itself, making it easier for ad controls to exfiltrate user information. There- Categories and Subject Descriptors fore some targeting information (e.g., user's geolocation) is likely to be used more commonly by in-app ads than by in- C.4 [Performance Of Systems ]: [Measurement tech- browser ads, while some information (e.g., all or frequently niques] used apps on the device) is unique to in-app ads. Second, ad controls often leave much targeting information optional Keywords to be provided by apps, and hence targeting behavior of even the same ad control may vary across apps depending In-app Advertising; Mobile Advertising; Targeted Advertis- on whether apps actually provide the optional targeting in- ing; User Profiles; Measurement formation. Third, some ad networks and advertisers such as mobile marketplaces are unique or more common in in- app ads and their characteristics have not been investigated before. Finally, the methodology for harvesting in-app ads is significantly different from that for harvesting in-browser Permission to make digital or hard copies of all or part of this work for personal or ads. classroom use is granted without fee provided that copies are not made or distributed In this paper, we present a first-of-its-kind study of tar- for profit or commercial advantage and that copies bear this notice and the full cita- geted display ads delivered to mobile apps. Our objectives tion on the first page. Copyrights for components of this work owned by others than are to broadly characterize in-app display advertisement and ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- to understand targeting mechanisms employed by apps and publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. ad networks from an empirical perspective. The study seeks MobiSys’15, May 18–22, 2015, Florence, Italy. to understand several key questions including the following: Copyright c 2015 ACM 978-1-4503-3494-5/15/05 ...$15.00. What targeting information do apps choose to send to ad http://dx.doi.org/10.1145/2742647.2742653 . networks? How effectively, if at all, do different ad networks though the degree of utilization varies widely across net- utilize such information to target users? Do ad networks dif- works. Our results show that in-app ads differ from in- ferentiate between different apps or different users using the browser ads in multiple ways|in-app advertising employs same app, and if so by how much? What impact does tar- significantly less behavioral and demographic-based target- geting have on ad delivery process? ing but more location-based targeting, and it shows more Answers to the above questions are important not just entertainment-related ads. for a general audience inquisitive into in-app ad ecosystem, Surprisingly, our study points out many instances where but also for two important constituents of the ad ecosystem: targeting information or behavioral profile does not have a app developers and ad networks. An app developer can use statistically significant impact on how ads are chosen (within the answers to guide his decisions on what ad networks to our observation window of one day). We are not certain why choose to effectively target users. He can also reduce the ad networks collect targeting information if not for selecting targeting information that the app needs to send to only ads. Three plausible explanations for an ad network to col- the ones that influence targeting decisions, thereby reducing lect such data could be: (1) forward-compatibility|the ad bandwidth and exfiltrated user data. An ad network can network wants keep the option open to select ads based on use the answers to find how often app developers provide a the information in future, and still be compatible to current specific optional targeting information to other ad networks or old versions of its ad controls used by existing apps, (2) and how much targeting opportunity it currently misses by backward-incompatibility|the targeting information is no not supporting the targeting information. longer used by the ad network but is still provided by old An empirical study of in-app ad ecosystem is challenging versions of its ad control, and (3) information brokerage| for various reasons: The vast number of mobile apps that the ad network sells the information to other parties. show display ads, the large number of ad networks that serve We would like to contrast our work with existing works ads to mobile apps, the diversity and dynamics of targeting on in-app ads. Most of the prior work has demonstrated the mechanisms across ad networks and users all present sig- large extent to which apps are capable of collecting user's nificant challenges. Our first contribution in this paper is personal information and the potential implications to user's a novel tool called MAdScope that can address these chal- privacy [12, 17, 20, 24, 26] and device resources [4, 15, 19, 28]. lenges. Central to MAdScope are three key mechanisms to These works do not investigate user targeting. A recent (1) quickly harvest ads from a large collection of apps, (2) work [27] investigates targeting, but in a limited scale and systematically probe an ad network to characterize its tar- scope|it shows various categories of targeted ads shown geting mechanism, and (3) emulate user profiles, i.e., inter- to 100 apps by one ad network, but does not characterize act with the ad ecosystem as if interactions were by users how apps use optional targeting information and how effec- of specific preferences and interests, in order to understand tively ad networks utilize targeting information and behav- if an ad network does behavioral targeting. Various aspects ioral profile. To the best of our knowledge, no prior work of these mechanisms are inspired by recent works on profile- investigates the key questions we investigate in the context based ad crawling [1], differential correlation [16], and ad of in-app ads. classification [27], but adapted to in-app ads. All these ca- In the rest of the paper, we first discuss backgrounds and pabilities are essential for understanding the full spectrum of challenges of measuring in-app ads in Section 2. We then targeting behavior of in-app ad ecosystem. We believe that describe MAdScope's mechanisms and our results charac- MAdScope and its mechanisms will be valuable for future terizing apps (Section 3) and ad networks (Section 4) from studies of in-app ad ecosystem as well.
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