How Privacy Policy Changes Influence the Revenue from Mobile Apps
Public concern about data collection and privacy is growing, and major companies like Apple and Google are responding with privacy policy changes that make it increasingly difficult to track user activity. As advertisers and app developers lose access to the metrics that have guided their advertising efforts for years, they’re forced to change the way they think about customer data and their approach to in-app ad revenue.
In order to keep up with the shifting landscape of privacy policies, the advertising sector must evolve. Businesses must look ahead to new strategies for targeted advertising and alternative app revenue models that retain the profitability of former models while upholding privacy-friendly standards and practices. First-party data, artificial intelligence, and machine learning are poised to fill in the gaps left by privacy policy changes, but advertisers and developers must be informed about these ongoing changes, threats to their revenue models, and new available technologies if they want to keep their businesses ahead of the curve.
What affects app revenue models?
For app developers and owners, understanding the threats to current revenue models posed by ongoing privacy policy changes is essential to maintaining profitability.
Of course, for apps that rely on in-app advertising and data monetization, changes to data collection and usage present a major challenge to their revenue model. Many advertisers rely on data collected from apps, and stronger privacy policies for users mean increased costs for marketing activity and a decrease in mobile ad spending.
As stated before, the public’s concern over privacy issues online is growing, and a more savvy consumer base means less engagement with apps that collect excessive personal data. Even without the updated privacy policies coming from Google and Apple, consumers themselves are becoming more wary of apps collecting information without transparency on what the data is used for.
Apps that employ revenue models that anticipate the shift in privacy standards could see increased revenue as advertisers and app developers take time to adjust to the new privacy landscape.
Recent key privacy policy changes
In response to public concern about data collection and privacy, Google and Apple are implementing changes that fundamentally shift the data collection strategies of organizations that use customer information for advertising and app revenue. These new features give individuals the power to block websites and apps from harvesting their data.
In 2021, Apple began enforcing a policy that requires all apps to prompt the user with a request for permission to track their activity across third party apps and websites using Apple’s Identifier for Advertisers, or IDFA. This App Tracking Transparency (ATT) feature dramatically reduces advertisers targeting capabilities. Additionally, iOS 15 has provided users with the option to hide their IP address from trackers. This means that developers will not be able to bypass app tracking opt-out by tracking their IP address.
Google is also taking drastic steps to improve the data security of their customers. Google has announced that they will end third-party cookies from their chrome browser. Since their announcement in 2021, they’ve delayed the final phase out. However, the company plans to phase out the cookie by 2024 finally. While first-party cookies will remain unaffected, the loss of third-party cookies in Chrome means that advertisers will be faced with a lack of detailed data used to build visitor profiles. Marketers relying on pinpointed audience-targeting strategies will need to adjust their approach.
Third-party cookie phase out is only one element of the Google Privacy Sandbox initiative. Announced in 2019, the program’s goal is to render current tracking strategies obsolete in favor of new technologies that give companies and developers the information they need to grow their businesses as well as protect the privacy of the individual. The Privacy Sandbox is touted as a collaborative effort that invites developers to work with Google toward viable privacy-first alternatives to the third-party cookie system.
App revenue models
Shifting away from ad based revenue models for mobile apps is one way to eliminate the danger threatened by volatile privacy policy changes. There are many profitable app revenue models that entrepreneurs should consider when monetizing their apps. The following are proven-profitable frameworks for mobile apps.
In-app purchase
In-app purchases provide users with access to features, services, or content within the app for an extra fee. This model has a variety of options for implementation, such as purchases of in-app consumables that are bought and used only once or freemium models that place special features behind a paywall.
In-app advertising
In-app advertising is quick to implement and provides a relatively simple path to app monetization. This model provides revenue to app developers by selling ad space within the app to other businesses.
Subscriptions
Popular with streaming services, magazines, news sources, and storage services, subscription based models provide a steady, reliable income stream that charges users on a regular basis for the service provided.
Data monetization
Data monetization is a clean way to generate revenue with minimal disruption of the user experience. Despite ongoing changes to data collection and privacy policies, apps still generate valuable first-party data that can be leveraged to increase app revenue.
Affiliate marketing and lead generation
Affiliate marketing provides space in-app for advertisements for products from other providers. Each time a customer engages with these ads and the interaction results in a sale, the commission from the purchase is shared with the original app’s owners.
Lead generation works in a similar fashion. However, instead of ending in a purchase, lead generation is about gathering contact information from interested users.
Paid apps
While becoming less popular, paid apps still account for a sizable portion of apps available on major mobile app stores. This revenue model relies on apps providing valuable features to consumers and the ability of a business to keep the user base growing in order to ensure stable revenue.
The future of in-app advertising
AI and ML have the potential to change in-app advertising using first-party data. While third-party data and tracking are becoming more and more inaccessible due to major shifts in the privacy policy, first-party data is still an incredibly valuable resource. The automated processes of AI and ML can use this information to adapt to changes and learn from user behavior rapidly.
Algorithms are far more capable of analyzing volumes of unstructured data than humans. They can enable ad campaigns with laser-focused specificity of timing and content, all without requiring personally identifiable information. Furthermore, modern AI programs have displayed remarkable efficacy in reducing the kinds of mobile advertising fraud that threaten advertisers’ returns on investment.
As AI and ML continue to develop and learn, their role in the future privacy-centric advertising landscape will become increasingly essential.
Summary
As technology giants, Apple and Google, continue to prioritize the privacy and security of online users, it has become increasingly evident that the challenges faced by app developers and advertisers are not going to simply disappear. To ensure sustainable growth and profitability from mobile applications, it is essential for these industry players to stay ahead of shifting public sentiment toward excessive data collection. Moreover, it’s crucial to understand the significance of accessible data in marketing strategies for apps in the near term. The shift in attitudes towards data privacy is having a significant impact on the way app developers approach their products and the way advertisers plan their campaigns. Looking ahead, it’s vital to be well-informed and adapt to the changing landscape to maintain the success of mobile apps in the long run.
