Behavioral Insider: What do you see as the biggest gaps, misconceptions or limitations in current thinking by television content providers about audience/advertising targeting?
Darren Gill: Over the past decade we've seen the incredible targeting power of online media, but the television experience, both on a programming and advertising level, has not only lagged but gone backwards. All you need to do is look at the upfront process, where the traditional model and metrics get less compelling every year. Nearly all the new technologies introduced in recent years, whether it's TiVo, DVRs or video on demand, have had the cumulative effect of undermining and making obsolete the old advertising models.
BI: How do the next-generation of program personalization tools work?
Gill: Whether it's DirecTV, Comcast or major TV networks, the imperative for content owners and distributors is to provide increased user value across the three screens, the TV set, the PC and, increasingly, mobile devices. Essentially what we're doing is helping our content provider clients to enhance the viewing experience of subscribers by better customizing programming choices. We do this by giving customers tools to search and sort available programming by relevance to their interests, and also to rate and recommend content and receive recommendations from other subscribers.
BI: How are these program personalization tools likely to change how advertisers can approach tracking and targeting consumer video usage?
Gill: Before, in the now nearly 60-year-old paradigm, advertisers would use particular TV programs as a proxy for audiences because targeting household viewers directly was impossible. They would infer that because a certain program was thought to appeal to households with x or y characteristic, then by inference, the way to reach those households was through that proxy. The potential now is to move from this second order indirect pproach of targeting by proxy to target actual households themselves, based on how they actually consume programming. Not only by what they watch--but by how and when they watch it.
In one sense it's an extension of what cable TV did in relation to broadcast when it enabled targeting by geographic zones and sub-zones, but it's a far more radical change. Cable altered and tweaked the broadcast model slightly--but what multichannel and on-demand viewing augurs is a fundamental change.
BI: How is data generated and what types of information can be gleaned?
Gill: Eliciting information about user preferences and generating recommendations, [we help] networks accomplish two goals. We help our clients to enhance the value of their content offerings to subscribers, which builds loyalty and trust. We also help them develop a better profile of audiences both by stated preference and behavior. They learn incrementally not only what specific programs [viewers] choose to watch, but why they do do-- and how those choices express people's interests and tastes. By knowing what someone is interested in and how those interests drive his decisions as a consumer over time, the personalization engine allows content providers to predict with increasing precision what new types of information--including advertising-[that viewer] is likely to be interested in.
BI: How do you see this more personalized targeting being deployed in the next year or so?
Gill: At first what this will do is allow the same kinds of targeting they've always done--content, demographic and lifestyle targeting--but to do it directly to each household. By understanding user preferences, advertisers can begin to untie targeting not only from linear programming schedules, but also from pure contextual advertising based on program content. Now they can drill down to the different combinations of what viewers watch in a more behavioral fashion. Technologically the next frontier is to leverage audience profiles and user preferences to time the actual insertion of specific ads to specific households at specific times--based on who they are, what they're interested in, what kind of programming they like, and when and how they watch it.