Audience Parting. Because Day Parting Is So 1996.

Nick Cicero

May 17, 2018

This blog was originally posted on
Conviva Acquired Delmondo in November of 2018 and has become Conviva Social Insights.

This post was originally published on TVREV. Visit for more original articles on the explosive growth in the TV space.

For decades, it’s been same old, same old for the people buying and selling television advertising. “Day parting” (buying and selling ads for specific times of day) to try and target the audience you think you want, based purely on those time slots Nielsen says resonate the most with your target audience. Want to reach senior? Try daytime TV. High school kids? 3 to 6 PM.

It was the perfect formula for an era where we didn’t know all that much about audiences, and when TV, radio and print were the only ways to reach them.

That era is long over and trends like time-shifting and binge viewing are blowing up the entire notion of day parts.

So why cling to day parting, when we have access to the sort of data that allows us to target the EXACT PEOPLE WE WANT TO REACH. With lots more data on the way.

That’s why we’re proposing that the industry stop thinking about day parting and move on to a system that uses a combination of audience data and social data to create custom scaled audience segments. We call it “Audience Parting.”

Audience Parting

Audience Parting is the concept of combining psychographic and consumption data to build a hyper-targeted audience pool. It’s different than addressable advertising because it takes more data into account, social data in particular.

Unlike day parting, which traditionally has looked at only a small section of data around viewers, audience parting combines the billions of data points being collected on social platforms with the audience data collected by Nielsen, Rentrak and other sources.

It’s not about finding the right time of day, but rather the right people at the right time, in the right location, with the right affinity for your brand.

One of the TV Industrial Complex’s holy grails has been the establishment of individual accounts, similar to what the telcos have going on with cell phones, and the vast amount of data that results when everyone has their own personal account.

MVPDs and networks would love to replicate that model and then combine the data coming out of all those individual viewing accounts together with the data from their users’ social accounts to create a rich and easily mined data bank.

You can see Audience Parting in practice with the rollout of Facebook Anthology, where ads and content are served up to specific audiences, based on their demographic profiles and their activity on Facebook and Instagram. Anthology is then able to determine which users reacted to the ads, what their reactions were and then add those learnings back to the database to increase its future accuracy.

What’s holding Audience Parting up? Inertia and resistance from the people doing the buying and selling. They’re afraid that it’s going to require a lot more work than day parting and that it’s going to be harder to sell, with giant holes of unsold audience parts that translate to less commission overall.

But that doesn’t have to be the case.

More perfectly targeted audiences should command higher prices as there’s almost no waste. You’re only reaching the people you want to reach. Not all those innocent bystanders. And the more data we have (did they respond to your last ad? What about the one before it?) the more precise your targeting becomes. Algorithms and other automated procedures for finding the right audience can turn the whole buying process into a race for the top, where programmers who consistently supply the best audiences are richly rewarded for it.

This is the dawn of a new era. Spray & Pray is on life support. We’re not killing weeds in our driveways people! When used in conjunction with audience data, social data can be your most valuable asset to reach the people you want to reach, when you want to reach them, in a way that ensures that everybody wins.

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