How Conviva Helps Programmers Make Better Content Decisions

In our latest Thought Leaders Circle video, Conviva VP of Strategy (and OG TVREVver) Nick Cicero discusses how Conviva’s data can help programmers understand why certain shows become hits, which ones are likely to become hits, and the ways they can use existing hits to help popularize new ones.

NICK CICERO: What I want to know is how does a movie like 21 Jump Street all of a sudden get to the top of the Netflix top ten for a week?

I see that in the Hulu “Most Watched” list ot similar features on other services and I wonder why do these really random shows end up bubbling up?

Which is really interesting because you have the hits that you plan for and the hits that you don't plan for.

The hits that you plan for are going to be the ones that you're spending the most money on, the ones you're driving the most promotion for.

Yes, your research has said this is the right content to acquire, and so it’s one of the shows that you invest everything into. These shows are your marquee content.

But then you have all of the rest of that content. And what Conviva solves because we have second-by-second session level data is that we can actually see how those patterns evolve and their impact.

So if you take one of your hits—one of the hits that you plan for—or even of the ones that you don't— you can actually use Conviva data to unpack that and figure out “how did it get to that point?”

You can use viewer level pathing to understand that people may have gone from watching nothing to watching an NFL game to watching Yellowstone and that pattern was more prevalent than anything else.

Or maybe—I'll use Paramount+ as the example here, maybe you have a whole new segment of people that came in and all of a sudden they joined Paramount+ because they wanted to watch Yellowstone.

And within six weeks all of a sudden Paw Patrol is back at the top of the charts again.

Them, you can use the Conviva data to see that Paw Patrol had a really strong incoming path from new viewers who are watching Yellowstone and that those viewers then move onto Paw Patrol, and you can use that data to your advantage to understand why Paw Patrol is going to be a hit and why it’s going to be a big thing for your service and how you can plan for it in terms of promotion.

If you think about a show like Squid Game as another good example, Squid Game often seems like it came from out of nowhere.

But it didn't just come out of nowhere. It started with a small group of people who started watching it and then that informed an algorithm that recommended it to a larger group of people, and then to an even larger group of people.

That's one of the powers that the Conviva Sensor has.

And that's why we're working with so many of these different publishers right now, because we can actually unpack all of those hits to help them understand how they go back and manufacture success.