Personalized Advertising: Balancing AI Innovation with Regulatory Challenges

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In recent times, the digital advertising sphere has faced several hassles with the impending deprecation of third-party cookies, spearheaded by tech giant Google. Initially set to bid farewell to these cookies, Google Chrome’s unexpected announcement in June to extend their lifespan until the end of 2023 brought temporary relief to stakeholders. However, the underlying challenges persisted.

According to a study done by the National Association of Broadcasters (NAB) and Borrell Associates in 2022, this situation has disastrous financial effects. The huge amount of money that U.S. stations could lose each year is $2.1 billion, which represents 6.3% of all the revenue they make from digital ads. The ramifications for publishers are even more dire, with estimates projecting potential losses amounting to a whopping $10 billion without third-party cookies, according to an IAB and McKinsey Report.

A lot of TV and radio leaders aren’t doing anything about the fact that cookies are going away soon, even though they know bad things are going to happen. Shockingly, only 27% of broadcasters have dedicated teams actively working on transition plans, as revealed by the NAB report.

Braiin Wilson, Vice President of Media Acquisitions at Epsilon, talks about the problems with third-party cookies and how they make it easier for bias to creep into data collection. “But third-party cookies have issues. Third-party cookies cause blind spots in the data publishers have available. It is possible to have a strong first-party data plan, and we often talk to publishers who think it will help them more.”

As more businesspeople recognize the importance of first-party data strategy, many consider this. The problems are already insurmountable for people who don’t know where to begin.

The EU’s AI Act and Its Implications

Regulatory action makes things even more complicated in a world that is already very unstable. The EU suggested the AI Act in April 2021 as a way to deal with the issues brought up by fast technological progress and to lower the risks linked to AI. But when ChatGPT came out in December 2022, it slowed down the voting process by a lot. The General Data Protection Regulation and the AI Act both place limits on how ML and AI can be used, especially when it comes to gathering personal user data. New rules have been added to this regulatory system to keep up with the constantly changing nature of digital advertising.

The EU’s AI Act will significantly impact how advertisers leverage AI for their campaigns. This legislation aims to regulate AI development and use, prioritizing ethical and legal compliance. One of the most significant changes will be increased scrutiny of targeting practices, prohibiting AI-based ad targeting that relies on sensitive data like race, religion, or sexual orientation.

The AI Act also cracks down on manipulative techniques and algorithmic bias. Deceptive practices like generating fake reviews or using subliminal messaging to influence user behavior will be banned. A study by Accenture found “83% of global executives believe it’s crucial to address bias in AI.” Advertisers will need to ensure their AI-powered ad generation adheres to ethical standards and avoids perpetuating biases.

The AI Act further emphasizes fairness and explainability in AI algorithms. Advertisers may need to invest in tools that shed light on how their AI systems arrive at targeting decisions, demonstrating that these decisions are not biased against certain demographics. Better still, advertisers need to look for solutions that will not be discriminatory in approach.

The Future of Personalization  

It’s important to remember that by 2025, 80% of marketers who have invested in personalized marketing will pull the plug. On their side, individual users often feel like they’re in the dark about what data big tech companies collect, how it’s used, and with whom it’s shared. A Pew Research Center survey found that 81% of US adults believe companies collect more data than they need, and only 9% feel very confident that companies will protect their data privacy.

This is mostly because managing customer data is hard, users’ trust in corporations is diminishing, and return on investment may not be optimal with the current methods. Now, with new legislation and regulations breathing down on data collection, it looks like it’s time to close up shop.

Yet, let’s not dismiss the importance of personalization. We all crave tailored experiences and customized recommendations that effortlessly fit into the rhythm of our lives. And marketers know the importance and undisputed impact this approach can have on their companies’ top lines. The key is to leverage technology innovation to render these personalization efforts smoother, efficient, regulation-abiding and hassle-free.

With buyer tastes and rules changing all the time, Lerna AI and other new technologies show us the way personalized advertising could look in the future. Lerna AI uses a federated approach, which means that user preferences are processed directly on their devices. This is better than standard methods, which require collecting a lot of user data. According to Lerna AI CEO Georgios Depastas, the company boosts engagement and conversion rates by giving users personalized suggestions based on their current situation. They do this by using advanced privacy techniques such as federated learning, secure multiparty computation, and differential privacy.

Lerna AI  packages these complex technologies in an easy-to-integrate product where developers can reap the benefits of novel technology without any understanding of AI or the technical complexities that exist under-the-hood. Their core product is a light-weight mobile library (SDK) that integrates with mobile apps with just the addition of a few lines of codes. This library utilizes in-app interactions, device data, and sensor data, in order to better understand the individual user, without taking any of these sensitive raw data out of the device.

Unparalleled Personalization: The Promise of Lerna AI

Depastas says that personalization is harder to achieve because of more rules prohibiting the sharing of data to third parties. He says, however, that customization is necessary in today’s mobile world. Lerna AI’s cutting-edge solution solves this problem especially  when it comes to tailoring communication, engagement and suggestions to each individual user.

The federated learning method from Lerna AI makes it possible to offer personalized content effectively by identifying the user preferences, context, and current situation in real-time. Multiple tests have shown that the platform increases clickthrough rates twice as well as traditional methods. This demonstrates the effectiveness  of such a solution to engage users and boost the revenue for the companies that adopt it.

When it comes to personalized mobile experiences that respect users’ privacy, Lerna AI is in a great position to lead the way. The company aspires to change how people interact with businesses via their phones worldwide by proving that advanced technology can create a personal and safe connection with individual  users. Lerna AI is working with B2C mobile apps that have a large number of offerings (products/ content items), like in the media & entertainment, social, and browsing spaces.This way, Lerna AI helps businesses give their customers experiences that are as unique as the customers themselves.