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Digital app marketing is rapidly evolving, reshaped by rising user-privacy expectations and significant regulatory updates. Global ecosystem shifts — including Apple’s upcoming rollout of its new App Tracking Transparency (ATT) policy — limit how consumer data is collected and used for advertising. These changes are a continuation of industry wide trends to give people more transparency and control over their online data and interactions with advertisers. It’s important for brands to invest in innovations that deliver privacy-safe experiences for customers and protect marketing performance. To help, here are three strategies businesses can apply for success.
Establish strong measurement foundations through first-party relationships
Building stronger relationships with new and existing users of your app should be a centerpiece of your privacy-first growth strategy. First-party user data — data that companies collect directly from their customers with consent — is an important source of observable interactions that can help you understand your customers’ journeys, from tapping on an ad to taking an action in your app. It serves as a foundation for conversion modeling, where Google’s machine learning applies statistical patterns from observable conversions to portions of your app traffic where data is incomplete or missing.
Engaging directly with your app users can also help you build trust with your customers. Provide easy, intuitive ways for people to share information about themselves, and demonstrate that you will use this information to make their app experience more personalized and helpful. Communicate the value that you’re giving users in return for their data — whether it’s providing a promo code for an email address, access to exclusive items, customized recommendations, or a simpler checkout process.
Building stronger relationships with new and existing users of your app should be a centerpiece of your privacy-first growth strategy.
Strengthening first-party relationships and developing strong infrastructure to facilitate the process could mean modifying your onboarding and transaction flows, creating loyalty programs, or even adopting new features, such as in-app chat. This can take substantial time and effort, but you should treat these changes as investments to future-proof your marketing campaigns and boost customers’ lifetime value. At Google, we’re working on measurement and reporting solutions, including privacy-preserving functionalities in the Google Analytics for Firebase SDK, that will consider and account for upcoming privacy changes. To get the most out of such solutions, you need to ensure you have robust data inputs that can hold up over time.
Drive campaign performance with the right conversion data
Ad platforms, including Google’s, are grappling with some of the same growing pains you do as an advertiser — we are also adapting in real time to ongoing changes in the ecosystem. The evolving and nuanced nature of the challenges we’re addressing contributes to discrepancies in the ways conversions are measured and reported across networks.
For example, App campaigns run on both web and app, and serve text, image, and video ads. Marketing across channels and formats can present challenges for compatibility and consistency between App campaigns and Apple’s SKAdNetwork. Different approaches to custom conversion windows, web-to-app attribution, and measurement of re-installs and video views make it difficult to draw direct comparisons among conversion metrics you may see on Google Ads, SKAdNetwork, and other ad networks. We encourage you to learn more about these discrepancies as you evaluate your metrics and make decisions about your app marketing.
The evolving and nuanced nature of the challenges we’re addressing contributes to discrepancies in the ways conversions are measured and reported across networks.
To make our conversion reporting as robust as possible, we will only include modeled data when we are certain that a conversion took place, and when we have enough information to confidently model. By upholding a high threshold for modeling and data quality, we avoid overreporting and ensure conversions that fail to meet a rigorous standard are not shown to you or used to optimize your campaigns.
