The Battle for ATTENTION: Will AI Save The Advertising Industry in a Privacy-Driven Economy?

How First-Party Data & ML Work Together for Better Customer Insights

By SCUBA Insights

If machine learning (ML) is an engine, then data is the fuel that powers it. And while these generators require massive quantities of data to run on, it’s not just a “more is better” situation—data quality matters too. 


The success of any ML model hinges on a continuous stream of relevant, first-party data. That’s always been the case. But as user data becomes more elusive and less reliable—thanks to new privacy regulations, platform-level changes, and waning public trust—access to first-party data has become an absolute necessity.


So, how can mobile advertisers achieve measurable, meaningful, and monetizable results in this shifting digital landscape? Read on as we delve into the intelligence behind ML algorithms, the untapped potential of first-party data, and its potential to drive decision intelligence.


How first-party data fuels mobile advertising

“Know your audience.” This is one of advertising’s most established principles, and first-party data is a key source of customer intelligence. You can think of first-party data as anything collected directly from your relationship with the end-user or customer. 


While in the past this might have included inputs from comment cards or suggestion boxes—today’s advertisers look to in-app behaviors like logins, ad impressions, and click-throughs to shed light on user behaviors and preferences. Plus other sources, like emails and phone numbers collected through forms, purchase history, or other website engagement metrics. 


First-party data lets you optimize bids in real-time and on a bid-by-bid basis. It makes campaigns more relevant, targeted, and supports hyper-personalization. Up until recently, mobile advertisers supplemented this with data from third-party sources (think cookies, tracking pixels, and device-level advertising IDs assigned by Android or Apple) for a more complete picture.

The critical role of data quality in ML models

Poor data is the number one enemy of success in using machine learning for decision intelligence. But before we go any further, what does “high-quality” data even mean?


IBM defines data quality as a measure of accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose. Machine learning models trained on poor data—consisting of duplicate inputs, missing values, or irrelevant identifiers, for example—can potentially lead to adverse business outcomes. Gartner reports that poor data quality costs organizations an average of $12.9 million each year. 


Imagine an automated mortgage lending tool trained on false exchange rates or accepting too wide (or too narrow) of a range of credit scores. The combination of inaccurate and invalid inputs would cause the predictive model to go haywire, possibly awarding mortgages to poor candidates at unrealistic rates. 


Or, let’s say you’re building a model to support omnichannel measurement, but key steps of the customer journey are missing. Without a complete data set, the model has no chance of delivering the 360-degree insights and predictions needed to make accurate, data-driven decisions. 

Why the future of ML & marketing lies in first-party data

Despite the growing mountains of audience usage data, performance marketers have fewer resources at their disposal.


Consider existing wide-reaching policy changes like GDPR in Europe and CCPA. Or, platform-level changes such as Apple's AppTrackingTransparency and Google's Privacy Sandbox. With more to come, measures like these are already closing off the supply of usable audience intelligence data


Of course, this data was never perfect to begin with. On their own, third-party insights often proved inaccurate or out-of-context. And when you consider the associated legal, financial, and reputational risks, it’s hard for brands to see the benefit of third-party tracking. 


According to CSO data privacy fines are far from miniscule: 


  • Amazon paid out €746 million ($877 million) in 2021 for breaches of the GDPR.
  • In November 2022, Meta received a €265 million ($277 million) fine for compromising 500 million users’ personal information.
  • Instagram owes over $400 million to Ireland’s Data Protection Commission for violating children’s privacy.


Even if you can avoid fines, third-party ads often alienate or annoy users—kind of like that creepy feeling you get when Facebook serves up an ad about something you were just talking about.


As customers become more wary of their data being used, and new protective regulations continue popping up, advertisers are seeking new methods for audience segmentation and targeting. High-quality, first-party data has emerged as the clear path forward in this new era of privacy-driven analytics—you just have to know how to harness it.

Leverage first-party data for ML-powered decision intelligence with Scuba

The world is changing and performance marketers need new options. Get ahead of the game—reduce your reliance on third-party data with Scuba’s privacy-led decision intelligence platform. 


Scuba leverages your first-party data to build powerful ML models so you can make fast, data-backed decisions that lead to better results. All while respecting user privacy. 


Imagine finally getting that 360° customer view. Never having to wait for insights. The ability to easily visualize patterns and trends, and perform A/B experimentation to determine your next move. Scuba lets you do all that and more: 


  • Dynamic hyper-personalization
  • Real-time cross-channel measurement
  • Predictive customer journey analysis
  • Privacy-driven analytics
  • Unified data, analytics, and AI/ML 
  • Segment analytics
  • No-code exploration


The service is fully-managed, and our zero-touch deployment makes your job easy. Sit back and relax, and we’ll get you up and running in a flash. And don’t worry, you maintain 100% control of your data.

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