Replacing Nielsen With Big Data


I’m a fan of television. I like watching interesting programs. It’s been a few years since one has caught my attention long enough to keep my watching for multiple seasons. Part of that reason is due to my fear that an awesome program is going to fail to reach a “target market” and will end up canceled just when it’s getting good. It’s happened to several programs that I liked in the past.

Sampling the Goods

Part of the issue with tracking the popularity of television programs comes from the archaic method in which programs are measured. Almost everyone has heard of the Nielsen ratings system. This sampling method was created in the 1930s as a way to measure radio advertising reach. In the 50s, it was adapted for use in television.

Nielsen selects target audiences that represent the greater whole. They ask users to keep written diaries of their television watching habits. They also have the ability to install a device called a set meter which allows viewers to punch in a code to identify themselves via age groups and lock in a view for a program. The set meter can tell the instant a channel is changed or the TV is powered off.

In theory, the sampling methodology is sound. In practice, it’s a bit shaky. View diaries are unreliable because people tend to overreport their view habits. If they feel guilty that they haven’t been writing anything down, they tend to look up something that was on TV and write it down. Diaries also can’t determine if a viewer watched the entire program or changed the channel in the middle. Set meters aren’t much better. The reliance on PIN codes to identify users can lead to misreported results. Adults in a hurry will sometimes punch in an easier code assigned to their children, leading to skewed age results.

Both the diary and the set meter fail to take into account the shift in viewing habits in modern households. Most of the TV viewing in my house takes place through time-shifted DVR recordings. My kids tend to monopolize the TV during the day, but even they are moving to using services like Netflix to watch all episodes of their favorite cartoons in one sitting. Neither of these viewing habits are easily tracked by Nielsen.

How can we find a happy medium? Sample sizes have been reduced signifcantly due to cord-cutting households moving to Internet distribution models. People tend to exaggerate or manipulate self-reported viewing results. Even “modern” Nielsen technology can’t keep up. What’s the answer?

Big Data

I know what you’re saying: “We’ve already got that with Nielsen, right?” Not quite. TV viewing habits have shifted in the past few years. So has TV technology. Thanks to the shift from analog broadcast signals to digital and the explosion of set top boxes for cable decryption and movie service usage, we now have a huge portal into the living room of every TV watcher in the world.

Think about for a moment. The idea of a sample size works provided it’s a good representative sample. But tracking this data is problematic. If we have access to a way to crunch the actual data instead of extrapolating from incomplete sets shouldn’t we use that instead? I’d rather believe the real numbers instead of trying to guess from unreliable sources.

This also fixes the issue of time-shifted viewing. Those same set top boxes are often responsible for recording the programs. They could provide information such as number of shows recorded versus viewed and whether or not viewers skip through commercials. For those that view on mobile devices that data could be compiled as well through integration with the set top box. User logins are required for mobile apps as it is today. It’s just a small step to integrating the whole package.

It would require a bit of technical upgrading on the client side. We would have to enable the set top boxes to report data back to a service. We could anonymize the data to a point to be sure that people aren’t being unnecessarily exposed. It will also have to be configured as an opt-out setting to ensure that the majority is represented. Opt-in won’t work because those checkboxes never get checked.

Advertisers are going to demand the most specific information about people that they can. The ratings service exists to work for the advertisers. If this plan is going to work, a new company will have to be created to collect and analyze this data. This way the analysis company can ensure that the data is specific enough to be of use to the advertisers while at the same time ensuring the protection of the viewers.


Tom’s Take

Every year, promising new TV shows are yanked off the airwaves because advertisers don’t see any revenue. Shows that have a great premise can’t get up to steam because of ratings. We need to fix this system. In the old days, the deluge of data would have drown Nielsen. Today, we have the technology to collect, analyze, and store that data for eternity. We can finally get the real statistics on how many people watched Jericho or After MASH. Armed with real numbers, we can make intelligent decisions about what to keep on TV and what to jettison. And that’s a big data project I’d be willing to watch.

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One thought on “Replacing Nielsen With Big Data

  1. I’m pretty sure TiVo has been sending data back for years for their own analysis purposes.

    See also: http://www.dslreports.com/shownews/ATTs-DVRs-Are-Watching-You-Watching-It-123050

    It’s not a big stretch for any set top box with a return channel for data. I’d be amazed in fact if most STBs did not already track this data. However, if it’s not being shared with other companies like Nielsen, we won’t get that big data you want. Maybe Nielsen doesn’t want to pay for that data? Maybe it’s a privacy thing?

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