Streamlining The Ad Bidding Process

Daniel Elad, Chief Strategy Officer at TheViewPoint

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For CTV marketing professionals, the value of artificial intelligence and machine learning technology lies not only in its capacity to handle massive datasets and streamline outcome prediction but in its ability to facilitate seamless, real-time communication between CTV-ecosystem stakeholders.

As consumer audiences use an increasing number of platforms to access content, and with stricter user privacy updates, the future viability of cookie-based advertising is receding. As such, emerging advertising channels must respond with innovative approaches to optimizing publisher ad real estate value and advertiser ROI without forfeiting ethical data handling.

In essence, AI and ML technologies are software capable of performing human tasks autonomously. Continued developments in connectionism, creating an artificial neural network, and computer processing power have resulted in the advanced AIs of today—those capable of analyzing datasets and learning to perform complex human behaviors at scale, including:

• Pattern recognition and trend identification.

• Multistage decision making.

• Accurate predictive outcome modeling.

AI In Modern AdTech

AI already plays an integral role in advertising technology, facilitating processes that human marketers are incapable of performing themselves at the scale modern marketing platforms require—tasks such as:

• Automating ad inventory auctions.

• Autonomously identifying and targeting audience segments.

• Testing ad variations and predicting variation success.

• Analyzing ad performance.

• Making predictive changes to improve ad impact.

Yet current marketing industry models primarily rely on user cookie data to execute these tasks.

As consumers use more platforms to access online content, companies like Apple and Google are rapidly tightening data protection policies. The amount of data transmitted between AdTech stakeholders is also increasing daily, meaning the future success of cookieless, scalable and relevant marketing efforts will depend on ethical data collection, sharing and use—industry issues that AI can help overcome.

Increased Data Protection Regulations

In 2021, 74% of U.S. internet users found themselves the most concerned about online privacy they’ve ever been. With improved awareness regarding personal data sharing, user expectations for privacy safeguarding have understandably evolved, leading to industry overhauls on how user data is collected, stored and used.

New regulations protecting user data, such as the General Data Protection Regulation in Europe and the California Consumer Protection Act in the U.S., mean the AdTech industry must adapt to data-protection policies and adopt a transparent approach to collecting and using consumer data.

One way AI/ML can help CTV marketers make more accurate targeting decisions without cookies is by taking user page-level signals from data-rich, real-time browser use and making this information available to both advertisers and publishers during that user’s session.

This approach, therefore, switches from audience profiling based on stored cookie data to audience behavior, which better aligns with ethical data handling. To take this a step further, AI/ML algorithms can then make this information available across platforms, helping to facilitate a congruent advertising experience for the target audience.

Poor Cross-Platform Communication

On average, Americans had access to more than 10 connected devices in their homes in 2020, including smart speakers, video game consoles, CTV boxes, tablets, smart TVs, computers, mobile phones, etc.

With such a high degree of cross-platform use, competitive ad separation, ad deduplication, frequency capping and ad repetition are crucial considerations for advertisers looking to meet budgets. Buyer-side signals, such as ad clicks, provide inadequate information for advertisers trying to avoid ad repetition. Other strategies must be implemented to optimize advertiser budgets while delivering a desirable viewer experience.

AI/ML algorithms are well-placed to improve the cross-platform audience experience while reducing ad spend for advertisers by offering CTV marketers access to technology that can leverage other signals to prevent duplication and repetition. Moreover, the overwhelming amount of data sent between platforms and ad exchanges increases daily, necessitating software capable of streamlining the ad bidding process.

Data Overload

With hundreds of billions of bid requests broadcast daily, sorting through irrelevant requests and supply path optimization is one of the most critical roles AI can play in the future of CTV AdTech.

For ad exchanges and stakeholders on both sides of the ad bidding process, ensuring demand-side platforms receive only highly relevant bid requests from CTV inventory sources is vital to managing the technological challenge of a high server load and making the overall process more cost-effective.

While these solutions AI brings to the CTV marketing industry are required to advance CTV as an emerging and effective marketing channel, CTV professionals must remember that marketing is a human-first endeavor and keep that principle at the heart of their brand’s value.

Embrace AI, But A Word of Caution

Eighty-six percent of consumers still prefer communicating with a human instead of AI software, a statistic that demonstrates public opinion on the perceived adoption of AI into their daily lives.

As such, while AI can perform the repetitive background tasks necessary for effective advertising, marketers must ensure their brands don’t over-automate their processes.

AI can offer analytical insights into historical data, but ultimately the decision must lie with humans on what action to take when presented with these insights.

Consider Google Maps. A user initially gives consent for the software to use their location-specific data and tells the AI program where they want to go. The software makes real-time calculations according to road conditions, vehicle type and time of day to select the fastest route possible. According to this data, Maps suggests several routes to the user, who then chooses the path they’d like to follow.

This interactive relationship between AI and human is vital to the successful future of AI in AdTech.

AI/ML algorithms have the potential to collect, analyze, share and streamline the volumes of cookieless, real-time, cross-platform data on user behavior at the page level required for effective ad targeting. As consumer empowerment regarding data privacy evolves, companies that embrace a human-first culture while ethically leveraging the power of AI can earn a competitive edge within the CTV AdTech industry going forward.

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