Meaningful Insights From Raw Metrics: Virtual Worlds and Other Business Applications

Virtual worlds are a goldmine of untapped behavioral data with insights that can be applied to many online social systems, as well as to the physical world.

But unlike the physical world (where it is obtrusive and cost-prohibitive to follow distributed users around with video cameras and sensors), virtual worlds come readily instrumented. Anything a user says or does – including how often and in what ways they interact with other users and objects in their virtual environment – can be tracked over an extended period of time.

In this presentation, PARC social scientists will share findings and methods (e.g., customized scripts) they developed to extract behavioral data from online games. While we will use the example of World of Warcraft, a massively popular online multiplayer game that appeals to a broad demographic (and has an average user age of 30), our data collection/analysis methods have been applied to other virtual environments as well.

More importantly, we will discuss how we converted and processed raw behavioral metrics into meaningful psychological variables that can be applied to a broad spectrum of business applications and segments. Other questions we will address include: What are some of the unique data collection challenges in virtual environments? What are the pitfalls and advantages of large-scale data sets, real-time data monitoring, and more? How can one extrapolate insights from low-incident events to broader samples or domains?

Meanwhile, our other goals in this work (which is partially funded by the U.S. government) were to examine: whether behaviors in virtual worlds can be used to predict a user’s demographic and personality; which cues are most predictive; and how well can these variables predict real world behaviors? The process we developed and our findings can be used to create practical, actionable tools for automated segmentation, targeted marketing, and other business intelligence.