Marketing is among the few business functions already being profoundly impacted by artificial intelligence (AI). Recent advances in AI have laid the foundations for several new use cases across all industries. Actually, AI-powered marketing was more prevalent than most consumers realize, including its use in programmatic ad buying.

The explanation for this is pretty simple: marketing and technology work wonderfully in tandem. Since the first ad popups of the 1990s, the two have intersected quite regularly. Nowadays, all marketing efforts have a strong digital foundation. Therefore, print media utilizes QR codes, and even startups have Google Reviews!

This dependence on technology is one of the reasons why AI plays such an important part in marketing activities, especially in programmatic ad buying.

What is Programmatic Ad Buying and Why is it Suitable for AI?

Programmatic advertising is an auction-based framework that automates the purchase and sale of ad space. Using decision-making algorithms, advertisers (those acquiring ad space), as well as publishers (those offering ad space), can maximize the results they achieve.

Programmatic ad buying has always been guided by algorithms. They facilitate automated media purchasing in real-time in accordance with user data.

Platforms such as the Trade Desk, Google’s Display & Video 360, and Yahoo’s Demand Side Platform (DSP) employ algorithms to distribute ad spending and modify targeting in milliseconds with no human intervention.

This makes programmatic ad buying uniquely suitable for artificial intelligence applications.

AI and Programmatic Ad Buying: A Partnership of Many Years

Since its inception, programmatic ads have witnessed tremendous progress. Initially, it comprised core targeting options based on things like user demographics and location. In the past decade, however, it has grown into a sophisticated framework that includes real-time bids, ad exchanges, and data-driven decisions at the granular level.

The true game-changer has been the introduction of artificial intelligence into these decision-making processes.

To discover user behavior patterns, AI algorithms analyze massive amounts of data. This allows marketers to precisely target their intended demographic. AI can even forecast a user’s inclinations and behavior. This makes it an excellent personalization tool for any promotional or ad initiative.

Another expanding subset of AI is machine learning, which constantly decides the direction of programmatic ad buying. It enables real-time ad optimization by perpetually evaluating advertisement performance. It then “learns” from the resulting data and adapts campaigns accordingly.

This dynamic method makes sure advertisements are always effective and relevant, giving you maximum revenues from ad spend.

For example, machine learning algorithms work in the background to optimize data across a variety of dimensions, like:

• Visual appearance: The ad layout, fold placement, ad setting, and the publishing site or application itself.
• Technical context: The browser, type of device, and the OS.
• Different stakeholders: The locational demographic of the audience and the ad supply vendor

Moreover, programmatic advertising applies AI to build high-performing audience categories.

Remember that you are paying ad dollars for every click and interaction. If you target the wrong audience and clicks don’t convert, then that investment is wasted.

AI-driven segments that are dynamically updated using ML are more likely to stay in sync with your campaign targets, and reach the users most inclined to convert, take action, or make a purchase.

The Future: Generative AI in Programmatic Ad Buying

AI-driven marketing isn’t totally new. The exciting potential of new generative AI models, however, stretches well beyond the use cases we just talked about.

Deep learning, a subset of AI that is more refined and advanced, has supplanted general AI. Deep learning, patterned after the human brain, relies on networks of hundreds of thousands of interrelated nodes (neurons). This enables computationally demanding tasks like recognition of images, auditory recognition, or natural language processing.

These are used by platforms such as ChatGPT, Bard, and DALL-E to make sense of massive quantities of data and produce content on demand. This could have a profound impact on programmatic ad buying and placements.

Soon, programmatic ads enabled by generative AI could dynamically build and alter digital ad copy. This segment-specific content – going in as deep as “a segment of one” – would be determined by web page settings, user indications, and intentions, and other data types that futuristic AI can ingest.

Connected TVs: the New Arena for Programmatic Ad Wins

CTVs and online streaming services are fast superseding conventional web content as the preferred platform for viewing media. On these sites, generative AI can play a key part in contextually targeted and interactive ad placements.

Contextual targeting, as we just mentioned, offers several compelling possibilities:  Generative AI can recognize images and interpret what’s displayed on the screen.

AI is capable of analyzing the circumstances and contents of CTVs and producing insights regarding the most effective ad placements according to the obtained data. Businesses can focus on specific content categories determined by the interests and preferences of the people they want to reach.

This would also pick up insights from the topics, sentiments, and genres of various programs and events available for viewing – data  that only humans could previously assimilate.

Benefits of AI in Programmatic Ad Buying

The future of AI in the programmatic ad space is bright, thanks to ongoing innovations in generative AI technology and automation. This will result in:

1. Automated ad testing and faster creative innovation

Using machine learning algorithms, generative AI creates fresh material like ad copy or design assets. As this technology evolves, marketers are going to use it to automate persona-based programmatic ad creation and testing. This lets you create and refine new ad strategies quickly and efficiently.

2. Even sharper ad targeting and better returns

AI and machine learning will have an even greater role than they do right now, in influencing programmatic ad targeting principles.

Today, they are essential for audience targeting, allowing advertisers to connect with specific segments. Eventually, demographic, consumer behavior, interaction, and visual queue analysis, will all be in its ambit – refining targeting strategies. This will transform programmatic media buying, creating a new era of contextual, concentrated messaging.

3. Stronger user privacy protection

User privacy has turned into a major concern in the martech sector. New regulations regarding personally identifiable information (PII) will have a substantial effect, as this data plays an important role in determining audiences for targeting and analysis.

As a response, marketers may employ natural language processing. This can help better understand different forms of consent as they are naturally conveyed by the user.

Also, generative AI will enable a heightened level of personalization that the audience genuinely finds useful. This will make users more likely to opt into data sharing and programmatic ads.

4. Hyper-personalization at scale

Even as AI automates processes, perhaps even partially, brands will be able to take advantage of mass personalization powered by generative AI. This allows organizations to generate personalized content without manually writing or designing it.

Given that generative AI uses vast quantities of data to produce customized material that’s more likely to be applicable to each individual entity, it may help in changing how we think of personalization – unleashing greater accuracy and precision.

The Future of Programmatic Ad Buying is (Almost) Here

Even as businesses progressively test generative AI use cases, the ad tech sector continues to make significant strides. Advertising titans such as Meta, Microsoft, Amazon, and Google are rapidly honing their machine-learning capabilities in order to enhance their AI-led programmatic advertising toolkits.

Meta recently unveiled its updated machine learning tools along with an AI Sandbox for generative AI advertising experiments.

Likewise, Google announced new AI-powered advertising products, like Performance Max, which enables the development of creative assets through conversations with AI using natural language. Google aims to display AI-generated ad content on all of its major platforms, as part of its larger generative AI push.

The next step is for adtech vendors to rise to the occasion and explore how AI can be embedded into programmatic ad buy/sell workflows more seamlessly, making room for generative AI capabilities and tooling as well.

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