In this episode of "Exploring CTV and Retail Media Networks," hosts Dan Massimino and Evan Hovorka are joined by Sean Muller, the founder and CEO of iSpot. Join them as they discuss the rapid shift from linear TV to CTV and the rise of ad-supported models in CTV. They also explore the importance of standardization in the industry, integrating with partners, and all things AI.

Sean has founded and served as a key executive in half a dozen ventures throughout his career, including Demand Media, MediaNet Digital, eNom and The Wedding Tracker, and brings a real wealth of experience to the show. If you enjoyed this episode, don't forget to subscribe and review on Apple Podcasts, Spotify, and Google Podcasts.

Episode Highlights:

[16:54] Sean discusses the emerging trends in the video and streaming space, noting the rapid shift from linear TV to CTV and the rise of ad-supported models in CTV (as opposed to a subscription-based model). He also talks about the nontraditional TV networks in CTV, such as YouTube (50% of which is viewed on a TV screen) and Amazon (which has a TV network and owns Thursday Night Football)o. Sean then highlights the industry trend towards retail media networks.

[23:29] Sean emphasizes the need for standardization in measuring video advertising across platforms, from “traditional linear TV”, to on-demand streaming services, to social media sites such as TikTok. He notes that historically TV ads were measured by overall viewership, rather than the actual reach (ie impressions). Sean’s company, iSpot, has focused on creating a standardized metric for video impressions, meaning that each ad, regardless of platform, is measured equally. While the industry is moving towards this approach, Sean suggests that not every publisher or advertiser has agreed on a standard.

[32:35] Sean discusses how iSpot handles integrating data from "walled gardens" (platforms with restricted data access) by using a sophisticated approach based on household and individual identity mapping. He explains that Ispot integrates with partners by starting with their ID spine, mapping out households and individuals across the United States. This approach allows for seamless integration with partners and simplifies the process for advertisers and vendors; it also allows advertisers to understand how their ads perform across different platforms, ensuring accurate deduplication and comparison.

[34:48] Sean distinguishes between traditional AI (advanced machine learning) and generative AI, which involves generating different versions of ads or ad components, saying that there is a significant amount of “machine-learning AI that could be done to optimize the media”. He also mentions their investment in AI infrastructure and how they use their rich history of TV ads to generate ideas and speed up the creative process by creating storyboards or scripts. Sean emphasizes that AI won’t necessarily be used to create an entire ad, but could speed up some of the more time-consuming or expensive aspects involved in their creation.

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