Predictors of Facebook Engagement

This report from a panel discussion at a user group for R, an open source statistics package, shines a little light on what factors Facebook found helped predict a new users initial and ongoing engagement with their site.

Itamar conveyed how Facebook’s Data Team used R in 2007 to answer two questions about new users: (i) which data points predict whether a user will stay? and (ii) if they stay, which data points predict how active they’ll be after three months?

For the first question, Itamar’s team used recursive partitioning (via the rpart package) to infer that just two data points are significantly predictive of whether a user remains on Facebook: (i) having more than one session as a new user, and (ii) entering basic profile information.

For the second question, they fit the data to a logistic model using a least angle regression approach (via the lars package), and found that activity at three months was predicted by variables related to three classes of behavior: (i) how often a user was reached out to by others, (ii) frequency of third party application use, and (iii) what Itamar termed “receptiveness” — related to how forthcoming a user was on the site.

via How Google and Facebook are using R : Data Evolution.

2 thoughts on “Predictors of Facebook Engagement

  1. eas Post author

    Hey Luke. At some point I hope we have lots of user data to analyze, but that’s a ways off and we have no specific plans to use R. This article just came up in one of my feeds and I thought it was interesting.

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