Attribution is the marketing practice that assigns the value of an activity back to the source tactic(s), channel(s), or campaign(s) that drove the intended behavior. In multi-channel marketing campaigns, marketers deploy their budget across a variety of different channels to achieve a desired result or measurable event. These results can include actions such as a link click, a webpage visit, a completed survey, a form completion, a sign-up or registration, a purchase, a mobile app install, or any other meaningful and measurable outcome.
As the number of marketing channels (email, advertising banners, search ads, mobile ads, video ads, social media ad, webinars, blog posts, etc.) continues to increase and overlap, it becomes more complicated for marketers to understand the impact that each of these channels has on their marketing goals. In order to solve this problem, marketers rely on tracking codes that are built into each advertisement that allows them to detect whether that ad has been viewed or acted upon. Once the marketing campaigns have been deployed, a marketer can then review the performance of all of their campaigns, and determine which of those campaigns influenced or were partially or completely responsible for the outcome they are measuring in a process referred to as attribution. Many times, an event can be influenced or “touched" by multiple campaigns, in which case the desired event should be attributed to more than one factor. When it comes to mobile attribution, you can break down the attribution providers under web only, app only and across web and app attribution.
When a user interacts with a website through their browser, attribution providers attempt to achieve two things:
To track a user’s original source, there are several different methods. Web attribution providers will require the use of http referer, a parameter passed from the server telling the webpage what domain the user was on before. They will also make use of url parameters i.e. example.com/product?source=facebook&medium=cpc. Attribution providers can also use network based attribution methods to look at cross-domain cookies to help determine a user’s source.
Regardless of the method, the identification of a user’s source on the web is a solved problem.
As mentioned above, the second goal of the web attribution provider is to track users’ behavior once they have landed on the website. Ideally, the user’s behavior will be aggregated, not only from the initial session, but for every other session the user will take in the future. This is accomplished by the use of persistent cookie storage, meaning that, whenever a user lands on a webpage for the first time, the attribution provider will generate a unique identifier for that user that gets stored in a browser cookie until the user decides to clean their cookie - think of this as giving the user a unique name tag. Whenever that user returns to that same site, the attribution provider can look in the user’s browser cookies to see if they have a unique identifier stored, or locate that name tag, and if they do, they can use that identifier to tie that session to all of that user’s previous sessions. Essentially, the process is like adding a name tag to every user that visits your site regardless of whether you know who they really are.
Both of these goals, tracking user source and behavior, are imperative for a company to be able to understand which sources lead to higher user conversions and which sources lead to significant drop-off, hence the ability to optimize web campaign performance and ROI.
Mobile applications were built without the notion of cookies. They were inherently different than the web in the sense that they existed as siloed entities, not a part of the “web” like a website. Recognizing the need to identify users from app to app across multiple sessions, Apple and Google have developed their own solutions, the IDFA (IDentifier For Advertisers) for iOS and the GAID (Google Advertising ID) for Android. These are device-specific identifiers accessible by all native apps to allow users moving from app to app through advertisements yet still to be easily identified.
Since these identifiers are device-specific and accessible across all native apps, they are actually more reliable than the browser-specific cookies used on the web. Web cookies can be erased and they can not be shared from browser to browser, whereas these identifiers are inherently more stable* and can be shared across every app on the device.
The caveat that apps introduced was that users were being identified in a completely different way than they were previously being identified on the web. Since mobile apps had no access to browser cookies* and websites had no access to IDFA/GAIDs, it was impossible to recognize the same user across web and app. In other words, the same user is being assigned a different set of name tags, and there’s no way to reconcile the differences.
This identification inconsistency meant mobile attribution companies were forced to develop new ways in which they could match web users to app users. As a result, device fingerprinting was developed and has become the most commonly used tactic for cross-platform attribution.
The “fingerprint” method works by generating a snapshot of a web user consisting of a device’s IP address and user-agent (the device operating system, the OS version, and other device specific parameters), and generating another snapshot when the user opens the app. The attribution provider takes these web fingerprints and app fingerprints and sees if any of the fingerprints are a close match. This method results in varying degrees of accuracy, but can never be 100% accurate.
Of course, businesses can try to perform their own cross-platform attribution. To do that, a user must sign in to their account on the brand’s website, in the app, and in all the other in-app browsers in Gmail, Facebook, Twitter, etc. The issue is that a user will almost never be signed into their account across all of the browsers on their device. And more often than not, the user would be so frustrated during this high-friction process and bounce, leaving the brand with a broken digital footprint and leaky conversion funnel.
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