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How to Measure App Performance in a Privacy-Centric World

Mobile apps are an important channel within many brands’ digital footprints, but the iOS 14 App Tracking Transparency updates have created challenges for brands seeking to measure app behaviors and performance. It’s become increasingly difficult to connect users’ media engagement to app actions, and without that view of performance, brands are struggling to understand how much to invest in media that promotes app installs and usage. Thankfully, there are several ways marketers can regain some of that lost visibility to effectively measure and optimize app-focused media.

Best practices for measuring media’s impact on app actions

Holistic measurement plays a critical role in understanding media impact. In the world of apps, an important component of that measurement comes from software development kit (SDK) integration. All the major players, including Google and Facebook, offer SDKs that enable advertisers to understand how users behave on their app when they arrive via ads on a third-party platform (like Facebook or Google). SDKs have advanced to the point that they typically don’t hurt app performance or speed, and the benefits far outweigh any downsides.

Since app privacy regulations are only in effect for iOS at this time, marketers can also create separate iOS and Android marketing campaigns to measure and optimize the two operating systems differently. Isolating performance in this way can be helpful for forecasting. It’s easier to understand how privacy changes are impacting iOS numbers with a clean “before and after” analysis without Android data muddying the performance picture.

SDKs and device-specific campaigns provide data, but that data needs to be cleaned, standardized, and combined with other sources to be truly useful. A data science team or partner can combine exposures across all media, deduplicate records, and tie actions to an anonymous user-level ID as part of a multi-touch attribution model. The multi-touch piece is especially important when measuring media buys because media tends to sit higher in the funnel than other digital touchpoints, meaning it’s undervalued in other attribution models (such as last click). Once the data is aggregated in this way, advertisers can visualize full consumer journeys and look at performance for specific ad channels and specific app goals.

Using multi-touch measurement to inform app campaign media strategy

Once aggregated, there are myriad ways that brands can use their data to help reach app campaign goals. Detailed user journeys give advertisers a very clear picture of how to entice consumers to take a certain action. Let’s say a brand is trying to generate app installs that will lead to a six-month subscription to their service. They can analyze the full journeys of other customers who signed up for a six-month subscription to understand what channels, types of messaging, and in-app content they engaged with. The brand can then include those elements in its media plan to try to reach similar ideal users and guide them in similar ways.

This understanding of the consumer journey is important for all app types, but particularly for paid apps. Acquiring installs for a paid app is understandably more costly than acquiring free installs, so being able to allocate ad budgets in ways that are most likely to drive action is critical.

Granular multi-touch app data also allows for more personalized ad content. Brands with a “freemium” model (free to download with a paid subscription upgrade option) want to convert as many free users to paid users as possible. To do that, they can look at what type of content customers are engaging with on app and create retargeting ads tailored to the user’s preferences. For example, users of a news app who engage mostly with weather content will likely respond better to weather-focused imagery and a weather-focused subscription offer (For example: “Save 15% and get premium weather insights when you sign up today!”). Tailoring retargeting messaging and content based on the user’s behaviors makes it more likely that the ad will resonate with them.

A full multi-touch attribution model also enables planning for short- and long-term goals because of the granularity it provides for both off-app and in-app behavior. Advertisers can optimize to not just app installs, but also to repeat app usage by analyzing the journeys of individuals who use the app frequently.

Privacy updates will continue to present challenges for advertisers across all digital channels, but there will always be ways to adapt to best meet customer demands. Staying on the cutting edge of tracking and measurement capabilities will best position brands to roll with the changes and use media effectively for advertising, for apps and beyond.