How AI is supercharging programmatic advertising

Good to see you again for the eighth edition of Xenoss MadTech Digest. As always, we are back with a closer look at trends and issues that have lately been stirring the waters of AdTech. This digest is an outlet for us to share a technology-driven take on the challenges and opportunities within the ecosystem.

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For this edition, we decided to follow up on what turned into a trending theme of all AdTech gatherings: the applications of AI in programmatic advertising.

AI forms the backbone of the AdTech ecosystem, driving its ability to create tailored ads, optimize campaigns, detect fraud, and automate bidding and audience targeting. As AI's significance in AdTech continues to grow, the platforms equipped with superior machine learning algorithms are positioned to outperform their competitors. Let's explore the practical ways in which AI is empowering ad companies across various fronts.

Automated Bidding

Automated bidding strategies lie at the core of AdTech platforms, driving the optimization of bid prices for impressions. This crucial process encompasses a range of AI applications that go beyond historical data analysis and click probabilities. Let's explore some notable examples of how AI is revolutionizing bidding strategies in the AdTech industry:

Amazon Advertising: Amazon's AI-powered bidding technology utilizes predictive algorithms to determine the likelihood of conversions and customer behavior. By factoring in a wealth of customer data, such as search history and purchase patterns, Amazon's bidding algorithms enable advertisers to target the most relevant audiences effectively. Taboola : Taboola, a leading content discovery platform, employs AI algorithms to optimize bid prices and deliver personalized content recommendations. By analyzing user behavior, demographics, and contextual signals, Taboola's bidding system ensures the most relevant content is presented to each user, driving engagement and conversions. The Trade Desk :  By leveraging real-time data and predictive modeling, Koa AI empowers advertisers to make data-driven decisions, deliver highly relevant ads, and unlock new performance levels.

By harnessing the power of machine learning and data analysis, these platforms deliver improved targeting, better campaign outcomes, and enhanced ROI for advertisers.

Audience Targeting and Segmentation

As the ad targeting landscape continues to evolve, AI helps companies to adapt to the privacy changes like the phase-out of cookies, the introduction of Apple's App Tracking Transparency (ATT) framework and the upcoming Privacy Sandbox on Android.

These privacy-driven transformations necessitate innovative approaches to audience targeting, and AI-powered lookalike modeling emerges as a compelling solution. Various companies have already leveraged AI to develop robust lookalike models, enhancing their advertising campaigns and driving better results:

Pinterest utilizes AI algorithms to create lookalike models based on user interests and behaviors. By understanding the preferences and actions of engaged users, Pinterest's AI recommends content and products to similar audiences, driving higher engagement and conversion rates. Criteo employs AI-powered lookalike modelling to optimize ad targeting. By analyzing vast amounts of user data, including browsing history, purchase behavior, and product preferences, Criteo's AI algorithms identify patterns and identify audiences with similar characteristics, enabling advertisers to effectively reach their target customers.

Ad creation, optimization, and management      

Currently, running a dynamic creative optimization (DCO) campaign involves a lot of deliberate decisions by the media buying teams, with only a few select tech partners specializing in fully automating their DCO campaigns. However, the future holds the promise of AI revolutionizing this process, introducing greater sophistication and automation.

AI-powered solutions are transforming creative management platforms. Companies like Ad-Lib.io, Zeta, Bynder, Celtra, and others employ ML to analyze ad campaign performance and historical data. By leveraging AI algorithms, these platforms can detect declining engagement, low CTRs, or other issues, providing valuable insights and recommendations to advertisers for optimizing their ad campaigns.

Looking ahead, the integration of DCO capabilities into demand-side platforms (DSPs) is a promising development. Advertisers could simply upload various creative components into a DSP, allowing it to leverage AI-driven decision-making to select the most effective creative for each impression based on real-time data and user signals. Companies like Adform are already exploring AI-powered DCO capabilities, pushing the boundaries of ad personalization and optimization.

SSPs are also embracing AI to expand optimization parameters. By identifying high-performing headlines or captions and augmenting assets to match the aesthetics of the site, AI enhances the range of available parameters for optimization.      

Fraud detection and prevention

According to Statista, the estimated cost of digital ad fraud worldwide will reach $100 billion in 2023. Ad fraud detection is another area where ML application is gaining traction. 

AdTech companies fortify their platforms with ML to catch ad fraud patterns by leveraging anomalous-based and statistic-based fraud detection subsystems. The statistical-based approach aims at creating algorithms that analyze traffic and detect non-human activity. 

For example, VeraViews , an open ledger advertising ecosystem, used AI for building their fraud identification technology to analyze dozens of factors and identify whether the ad views are valid or fake. Anomalous-based techniques, in turn, segment the traffic, spot the outliers, and analyze them to identify suspiciously high CTR or other metrics.

Takeaways 

With AI in AdTech on the horizon, the industry is set to undergo a groundbreaking revolution. 

With the phase-out of ad IDs and increasing privacy regulations, AI will be instrumental in driving innovation. Automated bidding and lookalike modeling powered by AI will optimize campaigns and boost conversions. AI-driven creative management platforms will deliver personalized and engaging ad creatives at minimal costs. Marketers, agencies, and technologists that are tapping into the power of AI are uniquely positioned to succeed in today’s market. Those that aren't at risk of falling behind. 

However, to use AI effectively, companies need to implement AI best practices, set realistic expectations, and understand the maintenance scope—talent, computing power, and resources for data updates and algorithm retests — before authorizing an AI project. 

Follow up for more to stay updated on treacherous industry shifts and new lucrative developments!

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