MadTech is Making The Dream of Advanced TV a Reality

"Marketers want to know as much as they can about their audiences, and to know that they are using the tsunami of customer data now on hand to effectively reach the right people across all screens." -Nick Chakalos, senior VP and GM of data & emerging products, VideoAmp

Despite the inflammatory headlines about declining ratings and measurable drops in ad spend, TV remains one of the most compelling mediums for consumers, and thus the advertisers that want to reach them.

Nielsen found that adults in the U.S. spend the majority of their time (roughly 4 hours and 46 mins) watching linear and time-shifted TV vs. just 46 minutes watching on a “TV connected device,” and 10 minutes each on a desktop or mobile phone.

But, in both the largest agency holding companies on Madison Avenue and Silicon Valley’s marketing and advertising technology circles -- let’s call that intersection “MadTech” -- there’s a strong push for data-driven insights and TV audience targeting, measurement, and planning that goes beyond simple age and gender demographics.

Marketers want to know as much as they can about their audiences, and to know that they are using the tsunami of customer data now on hand to effectively reach the right people across all screens. And given that linear TV buys can be some of the most expensive line items on a media plan, they’re increasingly focused on getting granular performance metrics on campaign effectiveness and ROI.

Meanwhile, with increased competition from the likes of Netflix, Google, and Facebook -- and an overall assault on people’s attention from all fronts -- the pressure is on for broadcasters and MVPDs in particular, to change the narrative around the future of conventional, linear TV.

That’s where TV viewership data -- including which households watched which shows and were exposed to which commercials -- and the technology platforms that can analyze it, combine it with other data assets, and put it to work, comes in.

TV viewership data that can be matched to privacy-compliant digital identity is key to making the dream of Advanced TV a reality for the entire video ecosystem -- from the aforementioned broadcasters and MVPDs, to OTT providers, Smart TV manufacturers and advertisers. And that’s also why marketing and advertising technology platforms -- or the MadTech “pipes” through which all this data flows and is activated -- are a crucial new component of this overall ecosystem.

No wonder attendance at converged content and advertising conferences like Advertising Week and TV Week hit all time highs this fall, and C-Space @ CES 2019 will be rife with TV industry talk instead of the typical programmatic pablum. The age of MadTech -- wherein the largest and most sophisticated, omnichannel advertisers’ needs are being tackled by a new breed of MarTech and AdTech providers -- is upon us.

If data is the Advanced TV oil, MadTech platforms are the refineries

Intel CEO Brian Krzanich famously called data “the new oil,” and while other industry thought-leaders quibble with the quote for a variety of reasons, at a high level, it’s applicable when it comes to thinking about the audience data that powers Advanced TV.

Like oil, audience data is valuable. Like oil, audience data can be hard to extract, refine and otherwise make useable.

But just like oil, once audience data is aggregated, cleansed and processed, it has the potential to power a variety of Advanced TV products and services -- from programmatic algorithms that allow advertisers to bid on specifically-targeted inventory, to audience insight platforms that help content providers better segment, package and sell access to those audiences through specific shows.

Among those new platforms will be systems that ultimately hope to unify the full cycle of planning, buying, and measuring advertising across the linear TV and digital ecosystems, which now include Connected TV and OTT.

But nearer term are systems like our own Marketing Investment Platform, which measures linear TV, OTT and digital media investment across TV upfronts, scatter, programmatic and non-programmatic channels in a deduplicated fashion, and then recommends new plans (from upfront linear TV to complete cross-screen / cross-channel) and in-flight optimizations to maximize return on investment.

Starting with helping brands and their agencies automate the upfront TV buying process -- and more intelligently allocated spend across all channels, including reaching “the unreachables” on CTV and OTT -- these platforms will aim to enable a new and truly cross-screen media investment strategy. They will leverage TV Viewership data to map TV advertising campaigns directly to business outcomes and enable brand marketers to be more completely and consistently data-driven, accountable, effective, and efficient.

How it works: From crude data to refined content and audience insights

Making linear TV data actionable requires a platform that can refine three specific types of TV viewership data, each with their own benefits and pitfalls:

Panel-based (or logs of what a consumer household has viewed)

As the original source of audience data, panel-based insights are used and trusted by advertisers worldwide. Yet, because of the (relatively) small sample size, this audience data can be difficult to scale from a demo or household across an entire population (or nation). Never mind the difficulty of a consumer remembering what they’ve watched for panels based on hand-recorded logs.

Set-top box-based (or devices that record the content being transmitted)

Set-top box data delivers the largest footprint in terms of scale, and there’s no need to ask viewers to remember the content or advertising that they were exposed to. The caveat is that most people don’t turn off their set-top or cable box, so the device will continue to record and deliver “viewership” data, even if the TV is off and everyone has left the room. There are also no standard data collection standards so that each operator collects different consumer actions, records them differently, and interprets them differently.

Smart TV/ACR-based (or devices that report what content has been displayed on the screen)

ACR audience data is still relatively new and exciting, since Smart TV providers have invested significantly in being able to determine (and communicate) the actual programming that viewers are seeing on the screen. But the current ACR footprint isn’t nearly as large as the set-top box or panel-based audience pools, and so the process of making broader predictions based on the data is precarious.

That’s why the most pressing work on the innovative edge of our industry is the task of marrying this cleaned, refined and aggregated linear TV data with digital audience data -- which typically comes in the form of cookies, Device IDs, and even custom audience segments that vary according to provider and brand -- and doing so in a privacy-protected manner that would pass GDPR, and eventually CCPA, muster.

So while there are some signs that the appetite for new investments in MarTech or AdTech startups has waned, innovative data science and engineering companies with the ability to clean and make these disparate data sets useful, will ultimately be the ones to make Advanced TV + digital video convergence a simple, everyday reality.

MadTech here we come. See you at CES.