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American Idol Predictions from Likester

Did you watch American Idol last night? If not, here's my amateur recap: James rocked the house, as usual; Scotty had the girls swooning, again, as usual; Lauren won the judges over with both her song...
American Idol Predictions from Likester
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  • Did you watch American Idol last night? If not, here’s my amateur recap: James rocked the house, as usual; Scotty had the girls swooning, again, as usual; Lauren won the judges over with both her song selections; and Haley, although criticized for her first song choice by Jennifer Lopez and Randy Jackson, came back and showed her determination to win in her second performance.

    Who do you think should win American Idol? Tell us your vote in the comments.

    Although many of us have likely made predictions about whom we think should go home each Thursday night, a new startup prides itself in making Idol predictions based on actual data. The new service is called Likester, and the data is based on Facebook “likes.”

    Likester Idol goes through Facebook and analyzes the “like” data of each contestant both before and after the Wednesday night show. From this, they are able to determine how many new fans, or “likes,” the contestants have gained based on their performances. On Thursday around noon PST, Likester makes their predictions.

    Last week, they predicted that Jacob Lusk would be sent home, which proved to be correct. Will they be right again tonight? Check back here for frequent updates.

    It’s an interesting concept, isn’t it? Not only is Likester fun for entertainment purposes such as Likester Idol, but it is also useful in other areas. Likester serves as a search database for Facebook “likes.” In addition, it categorizes the “like” data in several ways to show users all the Facebook pages that they have liked in the past, the pages that their friends like, the pages that are currently trending, and the pages that areĀ  “liked” the most in any given area.

    Likester is also experimenting with affinity and anti affinity data. This data would make recommendations to users based user trends. For example, if you “like” Pepsi, Likester would suggest a list of items to you that others that “like” Pepsi also “like.” The anti affinity aspect would show items that users didn’t “like.”

    “Our goal is to help users understand the things that they’re already liking and, potentially, things that they could like,” said Kevin McCarthy, the Founder and President of Likester.

    Likester’s database grew to 2.5 million items in just a week’s time, which means that it is collecting a lot of rich data. McCarthy told us that brands are also finding the data very useful for their local marketing efforts.

    “I think we’ve done a really good job of putting the data out there. Our challenge is going to be about surfacing the good stuff for the users, so that they don’t have to do all this searching,” he said.

    Aside from the business and entertainment uses, we can also see Likester playing an important role in the upcoming elections in 2012.

    How much of an impact do you think Facebook “likes” will play in the elections next year?

    Update: According to this week’s Facebook “like” data, Likester predicts that Lauren Alaina will be going home tonight. Will this prediction come true? We’ll find out tonight…

    Update 2: It appears that the Facebook “like” data wasn’t as telling this week – Likester predicted Lauren to be the one going home and, ironically, James Durbin, who has 129,137 “likes” on Facebook, was the one America voted off the show. Does this mean that the “like” metric isn’t as influential as we originally thought?

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