Garthwaite has an eclectic background, including experience at data-oriented companies like Rubicon Project, along with content creation as the assistant editor for the Emmy Award-winning PBS documentary "Girls on the Wall,” to a gig as research assistant at Universal Pictures. His current company just came off a strong 2016 by expanding into eleven countries across five continents including Canada, the UK, Australia, New Zealand, Japan, Panama and Chile.
Charlene Weisler: What are the biggest challenges to linear TV today?
Field Garthwaite: Consumers receive hundreds of channels from their cable providers, and the latest research shows that they watch less than 10% of the channels. But even then discoverability is a problem for networks and other video providers.
Meanwhile consumer viewing habits are trending toward on-demand and TV-centric services like Netflix, HBO Go, Amazon and Hulu.
So now linear TV programmers are competing with websites, apps and social media platforms that utilize interactive recommendation engines like IRIS.TV.
It is a big leap from traditional TV to personalized viewing experiences with business rules to guide the video viewing experiences. Traditional TV will not go away, but it will merge and become more interactive as consumption continues to move to digital platforms.
Weisler: How does IRIS.TV content curation work?
Garthwaite: Let's say the most compelling next video for a particular individual is sitting in the publisher's vault, untouched for months. Because it knows that video will be of genuine interest, IRIS.TV’s personalization engine, Adaptive Stream™ goes and finds it and "programs" it into a continuous stream (teasing it in a preview screen in the corner of the current video.) Suddenly, a video that the publisher hasn't monetized in a long while generates revenue. IRIS.TV can also provide “Netflix style” content recommendations to support user content discovery.
Weisler: How do you use data?
Garthwaite: To optimize video assets' discoverability, we optimize data structure and taxonomy by ingesting asset metadata. We also track viewer behavior and each video's performance so that publishers know what content works across a variety of parameters such as category, device, time of day, and in-stream (knowing what assets generate greater follow-on viewing). All of this happens in the background, automatically.
We also utilize data tools to help customers learn from their audience engagement and consumption patterns. When the Cubs won the World Series, every sports customer of ours had a dozen or more videos. But the reality is that one of those videos outperforms all the rest, and by helping our customers be more data-driven, we enabled them to put the best-performing video on their home page or on the trending article on the Cubs win.
Weisler: How does your company measure TV?
Garthwaite: We look at viewer behaviors such as the device the user is using, what time they are engaging, how long they’re engaged, bounce rate, geolocation, demographics, as well as market data, including advertising spend and revenue.
Weisler: How will viewing and measuring TV change with connected TVs?
Garthwaite: TV sets remain the number-one source for streaming TV and video content in America. If publishers invest in innovative video platforms for their channels and apps, connected TVs will enable media companies to personalize video programming using machine learning and give viewers the “sit-back-and-watch” experience that they love.
Connected TVs would make it possible to use data sets to structure and organize TV programming categories such as news, sports, reality TV, documentaries, and to pull content from top sources based on the individual consumer’s preferences. This will also clearly have a positive impact on their ability to get the right commercials to the right viewer.
AI-based personalization essentially lowers costs of presenting customized video programming and increases advertiser revenue, a win-win for video publishers.