While this may sound like an effective approach on the surface, if time elapses between the ad click and the app download, the ad that generated the app install will not be given credit, since the user’s app install will be separated from the pre-install ad click. This method relies on measuring and matching user IP addresses, as well as user agents (such as the device model and the device’s operating system), across channels and platforms.
Historically, the traditional approach to attribution has involved basic probabilistic modeling, a method that can be flawed by its own impersonal approach to collecting and processing data. The Shortcomings of Basic Probabilistic Modeling In fact, the urgency behind managing and optimizing cross-channel, cross-platform attribution flows for iOS and Android mobile apps is only increasing.Īs reported in our 2018 Mobile Growth Handbook, marketers are relying on more mobile channels and platforms than ever before to drive mobile app installs -everything from Apple Search Ads and social platforms like Facebook to Google Universal Ads campaigns and partnerships with other apps or social influencers. Establishing methods of tracking paid app downloads and organic app downloads enables mobile marketers to remove the guesswork from their growth stacks. In this complex cross-platform world, figuring out how to determine the true source (or sources) of downloads for your iPhone or Android app is just as important as figuring out how to get more app downloads.
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