At PlayAbly, as privacy rules got tighter, we had to ditch the old tracking methods. We tried getting user consent through a simple game asking for their preferences. The number of people who opted in shot way up. My suggestion is to build a similar SDK. It worked for us, letting us understand people based on what they actually told us, not their device ID.
Building AI to match reporters with sources showed me what happens when privacy rules tighten. We switched from tracking individuals to finding patterns in groups. That kept our outreach working even as platforms locked down data. AppLovin's AXON AI can stay effective by focusing on content context and device details instead of volatile user tracking. It's exactly how we saved our journalist matching system.
I've been doing digital marketing for clients who are pretty careful about privacy, and I'll tell you what works. Get creative with how you collect your own data, and make sure people get something good in return for opting in. When Apple changed everything with ATT, we started pushing for more activity inside our platforms and just asked people what they thought. Our ads ended up working better. So, just keep getting better at explaining privacy and give users a real reason to share their info.
When we were building AI for our education software, privacy rules got tighter. Instead of tracking individual students, we started looking at group patterns. This kept our scheduling tool accurate even when we could get less data. AppLovin could do the same. Using device-level signals instead of personal data can keep predictions working without the ATT headaches.
Running AI healthcare teams taught me something when privacy rules got stricter. We stopped relying on tracking data and shifted to what people voluntarily shared from their devices. I told users exactly how we'd use their information, and they kept sharing. Our prediction models stayed accurate because we built them on data people actually wanted to give us, not what we could take without asking.
I ran into this same privacy problem back at Meta. We figured out how to group data without tracking individual people. AXON AI could do something similar, just like we did for our experimental products. If they build their systems with privacy in mind from the start, their data stays useful even when new regulations hit. The key is asking for permission and showing people the value, so they keep sharing information.
Whether AppLovin can hold AXON AI's data edge or not under Apple's new privacy-first world with shifting policies and entry of tactics such as ATT, would greatly depends on how agile it is in responding and innovating within the new privacy reality. By tapping to first-party data from its owned and operated apps, AppLovin can leverage AXON AI with valuable user intelligence while staying privacy law compliant. Also an investment in contextual targeting and ML models which don't rely on user level data can protectting performance as well. Further strengthening ties with advertisers and focusing together around aggregated privacy-safe solutions will also be key to tackling these challenges effectively.
Pursuing the purchase of AXON AI, AppLovin got a major upper hand in big data. This is crucial because Apple has stronger privacy regulations. Advertising will also be impacted by App Tracking Transparency (ATT). AXON AI deploys cutting-edge machine learning. It also collects data well. That allows AppLovin to show targeted ads. AppLovin has to adjust its tech in order to retain this advantage. They must obey privacy regulations. They must also run good ads. They could seek out new sources of data. They also could use partnerships to ensure they maintain their robust dataset for AI.
With the recent AppTracking Transparency (ATT) and privacy developments from Apple, AppLovin has been able to strategically navigate through changes in mobile advertising by redefining its place in the ecosystem. AppLovin not only abandoned common user-level tracking, but went in the opposite direction by owning and controlling the mediation layer via MAX. The move gives AppLovin access to real-time auction data flow, which will serve as the basis of its proprietary data stream that powers its AXON AI engine.
AXON AI are going to have to break their data advantage away from just relying on what we traditionally think of as user level data and instead really start focusing on the data that publishers have directly provided to them, plus all the useful contextual clues being picked up from their own in-house setup. The main play here is to tap into the sheer scale of their network to spot the big behavioural patterns that emerge at a really high volume inside their various apps, then use that to supercharge the predictive power of their platform. Not so much relying on pinpointing individual users anymore, but more on the patterns that emerge when you group users together in useful ways, plus all the new efficiencies that come from smarter media buying. By making their AI system better at putting the right ads in front of the right people at the right time rather than just knowing who the right people are in the first place, they can make sure they've got a real competitive edge even after ATT starts to strip back some of the more aggressive data collection methods.