by    in About Ranker, Market Research

Game of Thrones Fan Report: Behind the Numbers

Last week, we published an info graphic with lots of “taste data” about “Game of Thrones” fans. Basically, we used all the data we’re collecting about people’s preferences in Ranker to make some educated guesses about what else people who like “Game of Thrones” might like. Why? Mostly because we can, but also because we figured people could potentially find it interesting.

After we showed the infographic to the world, a lot of people wrote to us asking how we actually arrived at these conclusions. (And yes, some of them just wanted to be sure we weren’t just making the whole thing up.)

It all starts with votes. Thousands of people have voted on Ranker lists on which “Game of Thrones” appears. If they’re on a list that’s “positive” (for example, “Best Premium Cable Shows”) and they vote “Game of Thrones” up, we know they like the show. If we notice they also vote for “Game of Thrones” on other lists (“Most Loving Caresses of Dragon Eggs in TV History,” for example), we know they REALLY like the show.

Then we look at all the other Ranker lists where that person has voted, and get a sense for what else they like, and what else they hate.

But we don’t stop there. The next step is to arrange people into clusters based on their specific preferences. If 80% of the people who vote on Ranker lists like “The Simpsons,” and 80% of “Game of Thrones” fans like “The Simpsons,” that’s not very meaningful at all. But if only 20% of people who vote like “The Simpsons,” and 80% of “Game of Thrones” fans like “The Simpsons,” then we’ve learned something statistically significant about these people.

But what about fans of “Simpsons” parodies of “Game of Thrones,” you might ask… if you were purposefully trying to confuse me.

These “clusters” of people with tastes that are aligned will teach us basically everything we need to know to make educated guesses about what random Ranker users will like. In our next post, we’ll explore exactly how we use these “taste clusters” to draw conclusions.