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How to Leverage AI to Power Adtech Impact While Respecting Consumer Privacy

The AdTech space is crushing hard on artificial intelligence (AI) — with good reason when it comes to respecting consumer privacy. An industry that thrives on innovation and technological advancements, such as programmatic advertising, CTV, and personalization, is perfect for leveraging AI and machine learning to improve processes and increase conversion.

The love affair makes sense.

Plus, adtech promises to only grow. IDC predicts that by 2024, the AI market is expected to break the $500 billion mark.

Artificial intelligence hesitancy?

However, not everyone is chomping at the bit to implement these technologies. In fact, a Gartner report revealed that many companies are still hesitant and, therefore, missing out. The top three concerns companies have about implementing AI? Staff skills (56%), the fear of the unknown (42%), and finding a starting point (26%), as per Gartner.

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But none of those factors should hold us back; companies such as Making Science exist to complement your internal capabilities and the total benefits of AI far outweigh the drawbacks. Beyond the benefits of increased efficiency and accuracy, AI delivers value across the digital advertising value chain. At the macro level advanced marketers are creating AI-driven marketing mix models (MMM) able to accurately gauge ad spending incremental value and generate optimum investment. And at the micro level companies are going beyond the habitual personalization using machine learning to optimize campaigns with custom, first-party driven intelligent signals.

Creating personalized digital ads

AI-driven tools power virtually all digital advertising campaigns. AI tools and platforms have become an Internet mainstay delivering highly customized ads based on a detailed knowledge of each and every person’s historic activity on the Internet: what websites they visited, where they live, when they shop and much more.  This is the technology that you’ve seen when ads seem to follow you after abandoning a purchase on an e-commerce website. While this has been a successful strategy for many marketers, the creep factor it causes for consumers has led directly to the privacy changes revolutionizing the digital advertising market.

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AI in a cookieless world

The new wave of privacy changes has seen the phasing out of third-party (3P) cookies in Apple’s Safari and is scheduled to impact Google’s web browser, Chrome in 2023. Many marketers see this as a major pothole potentially rendering their digital ad strategies useless but we strongly believe that new opportunities are presenting themselves that are even higher impact than those employed in the past, however, the emerging strategies require a technology-led focus, creative thinking and a pivot towards maximizing ad effectiveness using AI and first-party data.

Incrementality and attribution

Knowing where to invest marketing budgets has always been a challenge but the evolution towards digital has given CMOs volumes of data to create sophisticated marketing mix models where upper, middle and lower funnel strategies can be systematically tested and optimized.

We’re seeing an evolution from last-click models that tend to prioritize search campaigns to a more complete vision that combines first-party data looking across social, display, video and other channels, understanding that they all contribute to driving sales. Offline sales data is often incorporated in order to get the most complete photo possible. Testing methodologies drive continuous improvement where we run competing strategies in different geographies, different platforms or different algorithms to identify winning strategies.

With the aforementioned privacy changes many predicted the end of these types of techniques but we’re seeing that AI is allowing algorithms to draw conclusions from reduced volumes of data thus preserving the value of these initiatives. This progression from volume to predictive capabilities is one of the key pillars underlying the growth of AI in the CMOs toolbox.

Optimizing ad campaigns

This combination of AI and first-party data carries over to the level of specific campaigns, where we see marketers developing customized audiences with enhanced information coming from the first-party data and feeding the platform algorithms. Want to attract low-churn customers, done. Or market-based on lifetime value, profit margin or other parameters? This can be done as well.

Consider a higher education institution whose primary goal is to recruit high-quality potential students and encourage them to apply. When interested students submit their information on the university website using a lead-capture form, the site collects its own first-party data. And this carries through to sales or other signals.

AI can analyze the submission and corresponding user behavior within the site to create a detailed user profile. The profile is then assigned a score, calculated to reflect the probability of the user being the “right fit” for the desired objectives. These scored profiles are fed to the advertising platforms so that they bring in a higher quality of potential buyers delivering an uplift on ROAS and sales. In addition, the university call center receives a prioritized list of potential applicants based on these scores to maximize the outcome of their outbound calls.

This is an example of digital campaign optimization and using AI for efficiency and effectiveness throughout the customer journey while protecting the user’s data and ensuring privacy. This example could ultimately be a more effective way to optimize conversions and create better customer experiences without relying on 3P data.

Refining AI for the future

Now is the best time to implement AI to develop optimal marketing strategies at both the micro and macro levels. Whether it be the creation of attribution / incrementality models or driving campaigns around first-party data, AI represents a compelling opportunity that can be harnessed today.


Specifically, there are steps you can take to leverage AI right now, including:

  • Identifying where your marketing and advertising is not fully meeting your business objectives and could be enhanced with AI.

  • Ensuring data capture and analytics systems are scalable and can support increased use and application of AI strategies.

  • Finding and recruiting talent – either as employees, contractors, or partners – who understand AI and can help implement and deploy efficient processes.

  • Keeping an open mind and being flexible and ready to adapt to the changing circumstances around AI and privacy.

Let’s avoid letting a lack of skills, resources, or knowledge of where to start hold us back from using AI. It is the future of adtech and we don’t want to be left behind.

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