The mobile marketing industry has always been a rapidly changing environment, and only those companies that are flexible and have managed to adapt to what’s new have been successful. Since the implementation of Apple’s ATT, and the impact that this paradigm shift has caused, privacy continues to be a great challenge for all marketers. Furthermore, the revolution does not end here.
Measuring mobile marketing
The recent announcement of SKAN 4.0, whose deployment is expected at the end of this year, or the elimination of the GAID (Google Advertising ID) by Google, represents changes in the short term that mobile marketing professionals will also have to face.
These, along with the launch of the Privacy Sandbox, an initiative led by Google to create web standards for websites to access user information without compromising privacy, scheduled for 2024; and the withdrawal of third-party cookies on the web, foresee that the environment will evolve with high levels of uncertainty and it will be necessary to make the most of all available resources.
User-level attribution used to offer granular data for optimization, bringing with it a sense of certainty that is now largely gone.
However, although not with the same level of precision, it is still possible to measure the impact reliably. In this sense, AppsFlyer shares a series of recommendations to continue evaluating the performance of the campaigns in a holistic way:
Accept the change and synchronize efforts
Greater complexity in the interpretation of the data requires synchronization between teams around the redefinition of common processes since the methodologies of each metric have changed. Dealing with multiple data sources to assess performance forces markets to be more specific about where their metrics come from, ensuring there are no misinterpretations.
In short, resistance to the fact that our ecosystem is being swept away by constant changes is useless, and the sooner marketing professionals accept the new reality, the better prepared they will be to face it.
Unify resources for a comprehensive assessment
For a complete evaluation of performance, marketers need to combine various sources, such as SKAN conversion data, Apple Search Ads, blended data, Media Mix Modeling (MMM), and incremental estimates, among others.
By combining various sources the picture becomes clearer and you can measure performance in a meaningful way and identify trends across channels and platforms. Even if some data is sometimes missing, there are still completely valid ways to get a good understanding of performance:
- Using a Primary Source of Truth (SSOT): Single Source of Truth (SSOT) is the practice of aggregating data from many systems within an organization. It allows you to combine multiple data streams into one, duplicate them, and ensure an accurate view of campaign performance.
- Consented cohort: Even if the opt-in has not been matched to the users attributed by the IDFA, performance after installation can be assessed by extrapolating the behavior of that cohort, as measured in size by SKAN or SSOT. IDFA data also offers greater granularity on retention or late conversion, among others.
- Descending measurement: On the one hand, incrementality makes it possible to identify incremental revenue drivers to optimize budget allocation; and, on the other hand, the MMM makes it possible to measure the impact of the campaigns and helps to determine how the different elements contribute to the results.
- Data Clean Rooms: It is a tool that allows you to take advantage of data insights at the user level without actually being exposed to them. Personally Identifiable Information (PII) or user-level attribution data is not visible to any of the contributors involved, making it impossible to target users with unique identifiers.
- Predictive Analytics: Allows marketers to make data-driven decisions with a high level of confidence, while relying on very limited data points. Although predictions can be used for all aspects of data-driven marketing, after the iOS 14 update they become essential, now more than ever. For example, predictive analytics allows marketers to predict LTV based on limited data, thus better allocating their acquisition spend.
Improve the knowledge of the consented cohort
Requesting the consent of users through the ATT and grouping those who have given their consent allows us to obtain useful information and fuel the optimization of the campaigns.
With Apple’s postback window, full cohort analysis disappears, making long-term revenue and retention measurement confusing. However, sampling the data of who has shared their data allows you to evaluate the performance of the campaigns avoiding averaging everything according to the limits of SKAN.
The importance of understanding measurement
With more limited precision in bottom-of-funnel events and the critical role of creatives in performance, ad networks are increasingly considering engagement data from first-party ads for delivery. Marketers have to analyze the metrics in more detail than before the implementation of the new privacy framework.
It is no longer just about CTR, but how users interact with ads. For example, engagement metrics like interest rate or average watch time provide a better understanding of what resonates with your audience the most.
As Apple’s SKAN 4.0 and Google’s Privacy Sandbox demonstrate, privacy-driven changes are not a passing trend, but an ever-expanding reality. The key to success lies in being up to date with the latest advances and best practices to understand this changing ecosystem with so much uncertainty.
