The Truth About TV Data

The Truth About TV Data

The truth.

We’re obsessed with it. And yet it’s so elusive that we’re always searching for it.

We watch the news hoping to learn the truth about what’s going on in the world. (And then we often look up the same stories on a different source just to see if the other source reported the truth.) We weigh ourselves in the morning hoping to learn some truth about our body composition. We look up ratings for restaurants or therapists or hotels to see if we can find some truth about them before we commit to them.

Think about academics, philosophers, detectives–their careers are a constant search for truth. But it’s not just those professions. It’s the TV industry, too.

TV data is a form of truth. And yes, we in the TV industry are hungry for it.

Stations hire research directors to learn the truth about viewership. Networks hire entire teams of data analysts to find the truth about a show’s performance. Companies hire business intelligence specialists to track the truth about marketing attribution and ad ROI.

As much effort as we put into trying to find it, real, accurate truth is simply hard to come by. You could say that truth falls on a spectrum (100% false to 100% true).

TV data truth spectrum

The spectrum is never perfectly objective.

One reporter may tell a story from one perspective, while another reporter may claim the facts say something very different. Which story is more true? The viewer gets to decide.

A Yelp rating and Google rating give very different reviews on a restaurant. Which one is more true? The consumer gets to decide.

Even in the case of TV data the spectrum is more subjective.

Two different TV data companies report a different number on total viewership for a certain program. Which one is more true? The interpreter of the data decides.

Objective or subjective, the truth rarely lies at the right extreme of the spectrum. And the case of TV data is no exception.

The nature of the TV data beast

The entire TV world wants to know the truth about viewership in the TV industry. The content owners want to know who watches their programs and who doesn’t. They want to know what TV viewers watch when not watching their channel. They want to know if their late news gets watched by the people that saw the promos.

And of course the advertisers want similar data, but for their ads. They want to know if their target audience watched their ad. Then, naturally, they want to know if those viewers took any steps toward purchase.

Gathering such data (for the content owners and advertisers alike) has posed a challenge to the TV industry since the first black and white, Colgate-sponsored program. Traditional TV has carried inherent barriers that make it hard for data companies to provide accurate truth about viewership.

gathering tv data has posed a challenge

The whole concept of a TV signal is that anyone at anytime within its reach can view it. It’s the perfect communications model, but it causes problems when you want to track viewer data. The following list explains three of the biggest challenges in gathering accurate TV data:

1. Lack of a return path
This is perhaps the greatest obstacle to collecting TV data. A lack of a return path means viewership can’t be tracked without an extra technology component or data-gathering process. Often this extra piece to the puzzle requires significant time (meaning delayed data) and increased variability, which can inhibit accuracy.

2. A variety of signal sources (OTA, Cable, Satellite, OTT)
Gathering data from one TV signal source is hard enough, but gathering data from multiple sources, and then combining it all to tell a comprehensive story with that data, has been a long-time industry challenge.

3. Inconsistent and unreliable measurement methodologies
The title of this issue speaks for itself. In their quest for good, clean data, companies continually update their data gathering methods. In many cases you couldn’t compare data from two companies, or even from two periods of time, because the methods differ so much. The uncertainty comes from human or device errors, explained below.

Noble attempts at getting clean TV data

A dozen or more companies exist that supply the broadcast world with TV data in some form. But they’re all subject to the same obstacles, and the truthfulness of the data they collect can be confusing to clients in return.

I’m not out to say that these companies are no good. They’ve offered truth (and continue to increase the accuracy of that truth) to the industry in really innovative ways given the technologies they have to work with. For example, collecting digests or using other manual viewer reporting devices addresses the challenge of not having a return path. The limited sample size of this method, however, can never offer complete accuracy to the degree wanted by many advertisers.

Separate companies gather data from set-top boxes, another innovative way to avoid the obstacle of no return path. However, the accuracy of this viewer data also comes into question because data gets read whenever the box is connected, regardless of whether the TV is on.

Other data collection methods face similar accuracy issues. Regardless of who provides the data, though, the inaccuracy gets noticed by advertisers. As a result, advertisers anxiously await stronger solutions to TV data pains.

In an interview with Variety, Linda Yaccarino, chairman for NBCUniversal ad sales and partnerships, expressed her frustration about how less-than-accurate TV data reflects on her as an advertising professional. From Variety:

Yaccarino lamented how NBC series like “Blindspot” are seeing as much as 700,000 viewers in the 18-49 demo not being accounted for….”Imagine you’re a quarterback, and every time you threw a touchdown, it was only worth four points instead of six,” vented Yaccarino.

Smart TVs usher in a new generation of more accurate TV data

The last few years have brought monumental changes to TV-related technology. Smart TVs (TVs made with internet capabilities) and advances in data collection have now opened a door for a new generation of more accurate TV data.

Because of their internet connectivity, smart TVs can offer the TV world many capabilities historically limited to digital and OTT content options. Add to this a staggering presence of 62 million smart TVs in the US (projected to jump to 114.2 million by 2021), and you start to see the possibilities for gathering rich, accurate viewer data directly from the TV.

To illustrate the power of smart TV data technology, the following list shows the issues mentioned before, now with an explanation of how advances in technology address each issue:

1. Lack of a return path

Smart TVs have a built-in return path because of their connection to the internet. This makes them capable of achieving data accuracy similar to digital.

 

2. A variety of signal sources

Data collection technology in smart TVs can account for any signal source. Because collection happens at the device level, the data includes all relevant viewership info regardless of signal source.

 

3. Inconsistent measurement methodologies

Data from smart TVs is not subject to human reporting errors because all reporting happens at the device level. As for typical device errors, smart TVs only collect data from TVs that are turned on.

Traveling the spectrum of the truth

TV data has made slow but steady progress over the years toward a greater understanding of the “true” side of the spectrum. Thanks to new technology, TV data can accelerate towards greater truth . Yet even with the invention of smart TVs you might argue that we haven’t arrived at 100 percent truth. And I would readily agree. I don’t doubt that advances in attribution technology and holistic viewership data collection will continue to move TV data farther right on the spectrum. But in the mean time, the use of smart TV data provides a powerful option for those who want to eliminate some of the existing issues in providing accurate TV data.

Sam Petersen
Sam Petersen
Content Marketing Manager
Sam is a conflicted marketer. He loves being creative, but also thrives on numbers and analytics. Sam has worked in several messaging-focused roles during his career, including journalism, PR, product marketing, and now, content marketing. When he's not stewing over his conflictual identity, he heads to the mountains to hike, ski, run, etc.