AI Unites Audiences: Bridging the Divide in Fragmented Advertising Landscape

?The premium video landscape is undergoing a remarkable evolution. With the emergence of diverse viewing platforms like Advertising-Based Video On Demand (AVOD), Subscription Video On Demand (SVOD), Free Ad-Supported Streaming TV (FAST), and traditional TV, consumer choices have expanded exponentially. However, this diversity poses significant challenges for content providers. To remain competitive, they must continually innovate and invest in new programming. The giants in the field, such as Netflix and Amazon, have demonstrated that subscription fees alone cannot sustain the industry's growth. Advertising, initially thought to be a universal solution, faces the same issue - fragmentation, mirroring the segmented nature of video services. As ad-tech vendors and content providers grapple with standardization and data sharing issues, Artificial Intelligence (AI) emerges as a potential game-changer.?

The Advertisers Dilemma

Advertisers aim to strike a delicate balance: reaching their target audience effectively without overexposure that leads to viewer annoyance. Audience compositions vary significantly across programs, compelling advertisers to scatter their ads over a broad spectrum of content to achieve desired reach. This strategy, however, often results in ad oversaturation. High-profile events like NFL games, where a 30-second Super Bowl spot can cost upwards of $7 million, attract advertisers due to their vast, unduplicated reach. Yet this leaves less viewed programs, which may still have a significant audience at a lower cost, underutilized.

The Evolution of Advertising Models

In the last decade, addressable advertising has risen as an effective way to manage campaign performance within services, offering the ability to control how often an ad is seen by individual viewers. This innovation helps balance ad frequency, reducing viewer fatigue and extending reach. However, this solution is typically confined to a single platform due to the non-sharing of data across platforms. Ad-tech vendors have caused several dominant players to deploy closed systems to reduce competition. Content providers have shown an unwillingness to share details about their audiences, believing this data is too revealing about their audience and business practices. The result is a series of walled gardens requiring separately targeted and managed campaigns.?

In-depth Look at AIs Role

Artificial Intelligence stands at the forefront of revolutionizing advertising strategies in this fragmented landscape.

• Audience Targeting and Reach: AI's ability to sift through massive data sets can pinpoint the most receptive audience segments for specific ad campaigns, ensuring ads reach the widest yet most relevant audience. By predicting user interest in particular ads and identifying the most effective programs for ad placement, AI can revolutionize audience targeting.

• Program Selection: In traditional linear TV advertising, where individual targeting and frequency capping are absent, AI can determine the optimal mix of programming to achieve the highest reach with minimal duplication. This approach is crucial for tapping into valuable viewership found in less popular programs, integrating them into comprehensive campaign strategies.

• Cost reduction:? advertising rates vary by provider, programming, desired reach, and scarcity of audience. AI can optimize the ad spend across services to leverage lower-priced placement opportunities where the target audience can be found.? By aggregating performance across a service that utilizes frequency caps, the advertiser does not have to pay for higher-rated programs to achieve the same level of unduplicated reach.??

• Cross-Platform Coordination: With unique viewer groups spread across various platforms, AI can predict overlaps in viewership, guiding advertisers on which platforms to prioritize and how to set effective frequency caps, thereby optimizing ad spend.
• Real-time Campaign Adjustments: AI's real-time processing capabilities enable swift modifications to campaigns, adapting frequency caps, budget allocations, and other parameters to maximize ad effectiveness and prevent audience saturation.

Case Study: AI in Action?

Consider a hypothetical scenario where an advertiser wishes to promote a credit card for frequent travelers. The advertiser believes the target market is made up of members of specific professions (sales, marketing, consulting, etc.) with a minimum yearly income of $250,000, and whose trips involve international travel. Believe it or not, this data is fairly easy to obtain by using a combination of third-party databases. The data is matched to data provided by the viewer measurement service which characterizes the viewership of the target audience. The result of this match may be that viewers are not evenly distributed across services. They may watch CNBC, Fox Business, CNN, Fox News, and MSNBC on cable. And for the sake of argument let’s say that while their households may also watch streamers, 20% of these target viewers do not stream content. How can the advertiser reach the entire market economically while avoiding oversaturation?

The advertiser wants to run a campaign across linear TV and several streamers including at least Amazon, Discovery+, Max, Hulu, Paramount+, Peacock, and Pluto TV. Ideally, the campaign will have a frequency of NBCUniversal five impressions with no viewer seeing more than 10 impressions.? The advertiser believes there to be 10 Million qualified potential customers.? Currently, the advertiser needs to place insertion orders on each of these service providers and cannot control frequency.? This is where an AI-based planning/buying platform may be useful.?

Using AI, the system might first isolate the TV-only viewers and come up with the right mix of spots to generate the desired reach and frequency within this group. The system will then look at the rest of the target audience and see what type of coverage had been achieved and then look across the services to determine from where the rest of the impressions should be generated to minimize cost and produce optimal exposure. An AI-generated insertion order will be generated that specifies the target audience, flighting controls, and the required frequency cap to reach objectives across the range of services used in the campaign. Results are monitored and the campaigns adjusted as needed.??

Challenges and Ethical Considerations?

The implementation of AI in advertising is not without its hurdles. The foremost challenge is building a comprehensive and accessible dataset.? AI is highly dependent on data – lots and lots of data.? This application of AI is no different. It is critical that the data used for prediction and measurement represent a wide range of viewers and is not skewed by the overabundance of specific types of viewers in a specific service. For example, subscribers of Disney+ are likely to be very different from those of Paramount+. While AI can work with limited data attributes, ensuring data accuracy and representativeness is critical for effective predictions. Whatever the data set, care must be taken to provide a system that respects user privacy and complies with regulations like GDPR and CCPA.?

The Road Ahead for AI

The advertising industry's fragmentation presents a significant challenge to the economic model of premium video. Addressing the fundamental issues of reach and frequency is crucial for the industry's sustainability. However, there are other use cases that will undoubtedly benefit from AI.??

AI can examine large datasets to determine the best audience for a campaign and ensure that ads are served to the widest possible relevant audience. It does this by combining ad impressions with attribution data to predict which viewers are most likely interested in ads. The target audience is only one consideration when determining when to play an ad. Other factors such as program context, ratings, time of day, day of week, and many other factors may affect how a viewer will react to an ad. Machine Learning can determine which factors drive campaign success and use them to make optimal ad decisions in real time.?

Other opportunities are more controversial as the services are currently performed by people whose livelihood might be threatened. The use of Generative AI in campaign development and the creation of ad copy can be seen as a threat to working professionals or as an aid to help them prototype ideas and be more creative.? Only time will tell.??

Conclusion

The integration of AI in video advertising is more than a technological advancement, it's a strategic imperative to address the fragmentation challenges. With its ability to analyze vast data sets, predict viewer behavior, and make real-time adjustments, AI stands as a beacon of hope for content providers and advertisers alike. However, this journey demands responsible AI implementation, prioritizing user privacy and ethical considerations. As the industry navigates these challenges, AI's role in crafting a more cohesive, efficient, and viewer-friendly advertising landscape becomes increasingly evident.

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