Even before the iOS 14.5 update, protecting consumer privacy has been a high priority for big tech and for brands and marketing. Over the next few years, major platforms are likely to implement more consumer controls and these controls can further complicate digital marketing strategies. Data scientists are addressing this challenge by figuring out how to preserve consumer privacy while also optimizing ad performance. 

The perceived tradeoff between privacy and targeted marketing. 

With the release of iOS 14, Apple’s users can now opt out of targeted tracking and they’re seizing the opportunity. More than 62% of people who upgrade to iOS 14 opt out, reducing the effectiveness of digital advertising, which in turn increases digital marketing costs by more than 20%.

With fewer users sharing information, brands are suddenly faced with a famine of the data that has powered targeted advertising for years and, as a result, brands are losing faith in the major social platforms’ ability to reach the right audiences. After the iOS changes, Facebook alone says it will lose $10 billion in revenue this year and other major platforms, like Snapchat, Instagram, Pinterest and TikTok, will also be impacted. 

While it is essential to protect consumer privacy, targeted advertising is also beneficial to the retail ecosystem: it offers consumers products and services that are far more likely to meet their needs. For brands, targeted marketing serves as a highly efficient acquisition tool, which ultimately factors into overall pricing and the consumer experience.  

Protecting consumer privacy vs. marketing: A balancing act

The answer lies in leveraging end-to-end encrypted data sources, which provide the best of both worlds. By having a wall between where the users engage (media platforms) and where data analytics live (proprietary databases), consumers have a choice around privacy while also receiving valuable brand information and content.  

Creating this “wall” between the user and their data helps combat the turbulence caused by the iOS 14 privacy option. It mitigates the lost opportunity from limited consumer tracking with an AI-based algorithmic approach. By constructing custom audiences, companies can put their digital ads in front of the right customers across major platforms.

Here’s how it works: 

    Data science helps drive revenue

    Using this approach, brands can protect the consumer while still enhancing profitability at scale. AI and machine learning are now being used to help drive revenue, reduce acquisition costs and increase return on advertising spend (ROAS).

    After the iOS changes were implemented, we worked with furniture company Apt2B to see if it could still profitably target audiences on Facebook using this new approach. The experiment worked. The company actually generated more revenue on less spend. Apt2B’s initial 60-day ROAS for its new curated audiences was 41% better than Apt2B’s standard campaigns. And after three months, they had generated $700,000 in revenue based on a $60,000 investment in advertising spend. 

    Apt2B's COO Alex Back said, “The new performance achieved was so cost-effective at scale that our team had to triple-check the results. While other businesses have looked elsewhere for advertising solutions, we're seizing the opportunity and are investing more in the Facebook platform."

    The release of iOS 14 may have been a big step towards greater consumer privacy protection, but it does not need to be the end of well-executed targeted marketing, as many had predicted.  With sophisticated technology, a large warehouse of anonymized data and machines that can learn as they go, it may just be possible to get the best of both worlds. 

    Alex Song is the founder and CEO of Proxima



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