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Getting Started with Mobile Attribution Table of Contents Getting Started with Mobile Attribution Table of Contents Introduction 3 Chapter 1 Chapter 2 Mobile Attribution 5-20 Post-Install Marketing 21-33 Under The Hood Analytics 1. Mobile Attribution Windows 1. In-App Events to Get Started With 2. Mobile Attribution Models 2. LTV Measurement 3. Integrated Partner Ecosystem 3. Cost Measurement 4.Deep Linking 4. Types of Reports 5. Re-Engagement and Retargeting 5. LTV vs. CPI - One Formula to Rule Them All Chapter 3 Fraud 34-37 Final Thoughts 38 1. Types of Fraud 2. The Face of Effective Fraud Protection 2 Introduction The Rise of Marketing-Driven Installs Welcome to the mobile-first era of digital marketing. With greater consumer adoption of advanced and yet affordable smartphones, and higher functionality being shifted to the apps within them, there can be no doubt that mobile has taken over. In fact, according to a recent study by eMarketer, mobile claimed about 70% of the total US digital advertising space in 2018, while AppsFlyer predicts app install ad spend will grow 45% this year to hit nearly $40 billion worldwide. The problem is that this success has also led to intensifying competition with over 4 million apps in the App Store and Google Play. As a result, the current reality of mobile is that app store discovery is broken. What this means is that driving “pure organic” users at scale is not a realistic outcome for most apps. That’s why app owners are realizing that marketing-driven, non-organic installs play an increasingly important role in the mix. 3 Intoduction According to our data, between 2016 and 2017 there was a Global App Install Ad Spend, In Billions (2017-2020) 22% rise in the share of non-organic installs in the app install pie. Between 2017 and 2018, the share jumped by 28%. This puts mobile attribution at the very heart of a marketer’s tech stack. Unlike organic traffic, marketers have far more control of their non-organic traffic, and they are able to leverage attribution and marketing analytics data to inform their decisions and maintain profitability against their spend. However, rising demand has also led to rising media costs, in addition to significant retention and monetization challenges. Therefore, apps cannot rely solely on non-organic installs. Marketers need high quality organic users – even a fraction of them – in order to reduce their effective cost and Spend (Billions) App Install Ad Growth Year-Over-Year drive profits. Ultimately, successful apps use a combination of non-organic and organic installs to rise above the competition. 4 Chapter 1 | Mobile Attribution Under the Hood Before we open the hood and take a look inside, let’s take 1. Deterministic matching uses multiple identifiers a step back and explore the core value of attribution: The including device IDs (Apple’s IDFA and Google’s GAID), mobile and digital ecosystems are highly complex with Google Play Referrer, and customer identifiers (i.e. hundreds and even thousands of media players. Without anonymized log-in data or hashed email addresses) to attribution, marketers lack the right kind of visibility into match and accurately identify users on whatever mobile their performance, which leads to significant overspending. device they may be using. In order to make sense of it all and pay for demonstrated value, marketers need to work with a partner that has a 2. Probabilistic matching is a statistical model that uses view from above and is a source of truth that can rule which anonymized attributes such as IP addresses, device types, marketing activity should be credited for delivering a desired browsers, geos and operating systems, to create likely action and which should not. statistical connections between devices. Because it is not as accurate as deterministic matching, it is always used as Mobile attribution works with multiple identifiers to measure a fallback mechanism. the pre and post install user journey with great accuracy and persistence across long stretches of time. These identifiers can be divided into two groups. Let’s dive in. 5 Chapter 1 Google Play Referrer An identifier unique to Android devices which enables marketers to attribute ad activity to media sources for Google Play Store apps (not supported on Android out of store). In this case, the attribution provider can send tracking parameters to the store, which then passes them back to the source when the app is downloaded. The tracking provider will most likely use the referrer method as it only depends on itself to create this match - it simply uses publicly available data from the referral source. Deterministic Attribution Methods 6 Chapter 1 ID Matching - GAID and IDFA There are two main types of device IDs that attribute installs the same way: ∙ A Google Advertising ID (GAID) is the device ID used to measure Android installs for both the Google Play store and Android out-of-store. ∙ An IDFA is the device ID used to measure Apple installs for the Apple App Store. It is important to note that device IDs are not supported in the mobile web and also require configuration on the media partner’s side, which means they are sometimes unavailable (whether not configured or not configured correctly). 7 Chapter 1 ID Matching With an SRN Unlike with regular ID matching, attribution performed with self-reporting networks (SRNs) is more limited due to the fact that the install data is kept with the network and not sent automatically to the attribution provider. SRNs include Facebook, Snapchat, Google Ads, Twitter, and a few others. In order to attribute installs to SRNs, upon the first app launch, the attribution provider checks whether the app is configured to receive traffic from these sources. An attribution provider queries all SRNs configured for the app, using the unique device ID of the new install. That is done via dedicated MMP (Mobile Measurement Partner) APIs, which SRNs enable the provider to use. Based on the returned answers, the provider can then attribute new users to SRNs. 8 Chapter 1 Fingerprinting Fingerprinting is an identification method that uses publicly available parameters (i.e. device name, device type, OS version, platform, IP address, carrier, among others) to form a digital fingerprint ID that statistically matches specific device attributes. Fingerprinting is vital, especially in iOS While probabilistic attribution methods are not as strong as deterministic methods in practice, the fingerprinting method is nonetheless critical to mobile attribution, Probabilistic particularly on a platform level. By default, iOS has fewer methods available with which it can create a user profile, as there is no referrer like for Android and Google Play. Attribution Therefore, when ID matching is not possible in iOS, fingerprinting is used as a backup. Method In fact, about 25% of installs in iOS are attributed with fingerprinting. This statistic becomes especially more important knowing the strength of iOS marketing and user acquisition despite only a 15% device share compared to Android. Although ultimately Android still leads iOS in both device share and non-organic installs, because of the high value of iOS users, as well as the overall user experience of iOS devices, Android is only 4x higher in global installs and only 35% higher in the US. Therefore, even though the attribution industry standard may turn to deterministic methods, fingerprinting plays a vital role nonetheless. 9 Chapter 1 The following illustration explains how it works: Beware: Outdated Fingerprinting Fingerprinting fails without IP uniqueness. It loses all value when thousands and tens of thousands of users share the same IP address, which is quite often the case with many wireless carriers or public WIFI networks, to name a few examples. To make matters even worse, some attribution providers use fingerprinting even when DeviceIDs are present on the click, substantially decreasing their measurement accuracy. AppsFlyer data shows that these fingerprinting inaccuracies double the number of attributed installs, wasting up to half of the marketing budget on otherwise organic users! Misattributed installs due to fingerprinting inaccuracy lead to a bleeding cash cycle for marketers, in which strong user acquisition performance is driven and the business is led to further invest in the wrong places. The bleeding cash cycle is particularly dangerous because it drives compounding losses, draining budgets today and wasting tomorrow’s spend as well. 10 Chapter 1 The Bleeding Cash Cycle Solution: Adaptive Fingerprinting Achieving IP uniqueness for shared, public addresses often stands as the main obstacle in effective fingerprinting, but advances in attribution technology have worked to overcome this challenge. Driven by massive scale, adaptive fingerprinting solutions assign an IP uniqueness rating to all collected IP addresses within the attribution provider’s database. From there, the platform uses a dynamic attribution lookback window which grows, shrinks, or closes depending on the population size of devices using a specific IP address. For example, an install from a home wifi network that is shared by only two devices will be given a longer attribution lookback window than an install from a more popular, and therefore less unique, IP address. At the same time, fingerprinting based on popular IP addresses shared by thousands or tens of thousands of users, such as in airports, are unreliable signals, and are removed altogether. 11 Chapter 1 The business side of install attribution is based on predetermined time frames, aka lookback windows. That means the period of time in which a user’s action preceding an install - whether ad click or view - counts as having an impact on the decision to download an app. There are two main types of windows: click-based and view-based. Click-Based Attribution Most attributed mobile installs come from user clicks on ads: banners, videos, interstitials etc.
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