Data and Reporting Methodologies in Cross-device Attribution

Welcome to the third blog in our cross-device attribution series, where we discuss how advertisers can leverage web campaigns as a meaningful acquisition medium for mobile apps and other platforms. Check out our previous posts on this topic, Part one and Part two, to get up to speed.

This recent post will address some of the critical data and reporting challenges that marketers face when implementing cross-device attribution. We’ll then present different methods to generating meaningful analytics when the underlying datasets are collected across multiple platforms.

Providing the correct views and analysis to marketers can often create the difference between having the ability to distinguish between your stronger and weaker channels and activities vs. running reports that lead to misinformed decision-making.

Cohort Analysis for Cross-device Attribution: The Basics

Let’s start with the basics and remind ourselves how we define cohorts in marketing analytics:

A cohort is a group of users with a common property. By looking at users with a common property, marketers can often isolate findings and effectively identify trends.

There’s no strict outline of how cohorts should be defined; however, certain industries have their own standard conventions. In mobile marketing, advertisers are often looking at app install campaigns, so Mobile Measurement Partners (MMPs) provide install-based cohorts, users grouped by install date. So if you go to your Singular dashboard and run a report that includes cohorted metrics, these metrics are calculated for a given install date, such as ad revenue, the total revenue during the first seven days after the install. A sharp reader may also call out that seven days can be calculated “on the calendar” or in 24-hour increments, and both would be valid! The MMP can decide on one of these or provide the option to the advertiser to choose the cohort definition that best works for them.

Lastly, we should also call out that cohorts are defined as groups of installs in mobile marketing, not users. This is because a new install might belong to the same user, but to the standard MMP, this would be a new install added to the cohort. Similarly, in web marketing, cohorts are often defined based on the user’s acquisition date, which is when (i.e., the date when) the user first landed on your webpage, which is not too different from the install date on mobile.

Cohort Analysis for Cross-device Attribution: User vs Device

When we look at cross-device, a single user can interact with ads and consequently convert on multiple devices and platforms, so the cohort definition needs to be redefined. With a single platform, the norm is to look at when the device converted. While with cross-device, we’d want to look at when the user is converting. In most products, this would be when the user sets up an account or signs in for the first time.

Let’s demonstrate this by example. My company built the latest social network, which offers 13-second chats with users you’ve never met before around the world! We have built this new network on mobile and web, so we get installs from the mobile stores and website visits from desktops and mobile phones. Users click ads, but as soon as they try to use the product on the respective platform, they need to sign up and log in. Another thing worth mentioning is that a user may click on multiple ads across multiple platforms before deciding to sign up and start using the product. And this is what we are interested in as the point of reference.

When thinking about this problem in terms of your standard marketing tools, an attribution provider that is limited to a single platform will present a partial snapshot of reality — and consequently, when working with several tools, there will be overlap, leading to inaccuracies in each tool’s cohorts. Determining a certain campaign’s LTV becomes difficult since you might be including existing users in that campaign, thus inflating the LTV from user acquisition (UA) activity.

Another thing worth mentioning is that even on a single platform, looking at users instead of installs or devices reveals a lot more insight. In addition to a more accurate LTV, the change in the definition of conversion carries a lot of value to ROI analysis and comparison. For this reason, in certain verticals such as Ecommerce, it is more common for marketers to define cohorts based on the first time a user makes a purchase, for example, which is much more meaningful than the time of install. However, the most important thing is to ensure that the data is accurate given a point of reference.

Cross-device Attribution and Analytics in Singular

By providing the option to select how cohorts are calculated in real-time, marketers using Singular can choose between device-based cohorts and user-based cohorts. This allows you to understand the actual LTV, or any other KPI, for a group of users instead of installs or website visits. It also enables you to understand how those users interact with your product across platforms. For example, you may acquire users at scale on the web, but they use the product on multiple platforms with different retention patterns. A particular web campaign may drive revenue on both mobile and web, depending on the campaign and the channel where the users are acquired.

Similarly, a particular mobile web may drive X new website visits where only a fraction is new users. Understanding these relationships is key to scaling your UA effectively.

This also suggests that on the Singular side, we have to distinguish between the marketing parameters that are attributed to the device (e.g., a new install on iOS) vs. different marketing parameters that are attributed to the user (e.g., Evie who has just signed up for a new account after clicking on an ad). The channel, campaign, creative, and more could be completely different. By getting this data also in raw form, marketers gain complete visibility into the user journey.

What’s next in the series?

Now that we’ve covered how reporting for cross-device attribution works, we’ll continue to discuss meaningful topics for marketers who run across multiple platforms or who want to diversify their UA to better prepare for iOS 14.5. In the next post, we’ll dive deeper into pixels, postbacks, and conversions to understand how your ad partners are also affected by how your cross-device attribution setup and what marketers should do to improve campaign performance. As always, we encourage you to learn more about Singular’s web, web-to-app, and cross-device capabilities and schedule a demo with one of our product experts.