How Independent Apps Can Affordably Scale and Overcome Data Chaos in the Era of Privacy 

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By Shumel Lais, CEO Appsumer

The mobile ad ecosystem has been shifting significantly under the feet of app developers for the past year and a half. From changes to consent-based targeting, and shifting ad budgets, to how businesses look at ad data – advertising has been severely impacted by privacy restrictions and the mobile performance playbook has been ripped from its binding.

Adding to that, each stakeholder in the mobile ad space now has their own set of data to show how these ads are performing, none of which is an apples-to-apples comparison across channels and operating systems, creating more fragmentation at a faster rate than ever before.

No one said that playing by the new rules of user privacy would be easy.

As we all know, the world’s two biggest tech giants are at the forefront of these changes. Apple released its iOS AppTrackingTransparency (ATT) framework in 2021, effectively galvanizing the entire mobile advertiser ecosystem, and Google is committed to phasing out web-based third-party cookies and limiting the Android user identifier GAID, although the rollout date for Privacy Sandbox has been a moving, amorphous target.

What this means for developers and their advertiser partners is that understanding and utilizing data is now more complicated. Assessing an app campaign’s performance requires more steps – and making sense of this data as a scaling app developer results in many dozens of open tabs and spreadsheets and hiring or outsourcing data experts to solve the time drain and loss of visibility.

For advertisers, this translates into a greater focus on unifying fragmented data that are now coming in from multiple sources. This includes cost data from media channels (including self-attributing networks like Facebook, Snapchat, TikTok, more traditional ad networks, and demand-side platforms), ID-based attribution data from mobile measurement partners, non-ID-based attribution data on iOS from SKAdNetwork (SKAN), in-app analytics data, revenue data, and soon Google’s Attribution Reporting on Android.

Advertisers are increasingly turning to modelled data to fill in the blanks on measurement data lost to new privacy regulations with probabilistic attribution, media mix modelling, or incrementality measurement.

But the playing field is not level.

Since iOS 14.5 ushered in ATT, a markedly higher number of smaller-scale advertisers have suffered at the hands of this policy change compared to enterprise businesses. While advertisers with ad budgets in the millions are finding more success on iOS post-ATT, those with lesser budgets, especially under $250,000 a month, have been significantly impacted.

Here are some of the challenges they face:

Mobile ad data is more chaotic than ever before: A large part of the scaling problem is making sense of performance to find opportunities. With a bigger set of resources, the largest advertisers have been able to quickly build data infrastructure around Apple’s SKAN attribution solution to get a better understanding of campaign performance.

But not all independent app companies have a small army of data engineers and scientists to crunch performance data and optimize for scale at the pace that is needed.

Privacy thresholds hurt smaller scale advertisers most: A key part of SKAN is the privacy threshold, which is a mechanism to anonymize SKAN postbacks. On Facebook, unless your campaign has greater than 128 installs per day, the SKAN postback will include no information on conversion events. This threshold is lower on other channels due to Facebook’s SKAN mechanics. However, it’s a big challenge for smaller advertisers to reach the scale required with their budgets. With SKAN 4.0 this burden may be eased somewhat.

Less diverse channel mix: Smaller-scale advertisers have less of a need to diversify their channel mix up until now, running on 2-3 channels with their scale of spend. However, with sweeping privacy changes, some channels have been hit harder than others. For many smaller advertisers, if the performance for one of their channels was hit hard, their spend was reduced. Larger advertisers have a more diverse channel mix and therefore less macro impact.

Solving the Data Challenge

At the heart of the problem sits data. It’s a necessity at this point to have a robust data infrastructure to make sense of data fragmentation. With partial data structured differently across multiple sources, a new data layer is required to manipulate and model data and present this performance view in a digestible format to make effective performance decisions. With the rapid growth of TikTok and Connected TV, it’s important to diversify the channel mix and increase your portfolio to protect against further privacy impacts.


As the industry learns to adapt to the evolving privacy landscape, app developers need to find new ways to bridge the gap between fragmented and incomplete data. This means normalizing and unifying data for accurate comparisons across channels and operating systems in dashboards and reports that can then be accessed by user acquisition managers. While competing against the data and engineer resources that larger app businesses have in place is a challenge, a complete view of performance will help you overcome data chaos, power more effective growth decisions, and help you protect your business in the privacy-first world.