By David Loshin

In the most recent posts, we have been exploring the emerging opportunity for developing demographic profiles for customers based on their virtual locations. More to the point, if we are using real-life (yet two-dimensional) geographies to help in developing customer profiling and segmentation models, how much more interesting would those profiles be when expanded to include behavior characteristics associated with many more dimensions?

In real life, (as my father used to say), you can only occupy a single chair
with a single bottom. But there are many virtual spaces that can be occupied by
one individual simultaneously, providing multiple dimensions for behavior
analysis. I can have a presence on any number of social networks, play different
online games, post comments at different venues, and tweet about all of these,
almost all at the same time.

Not only that, but recall that all transactions take place with the parties in a
real location, and that goes for online activity – much of our actions are still
pegged to some documented location, usually by IP address, which can be resolved
geographically based on the Internet topology maps. We can link real-world
individuals existing in real-live space to online activities, and we can link
online activities to real-world locations and real-world people.

By melding the characteristics of individuals associated with the different
virtual spaces with those characteristics associated with physical contact
mechanisms and locations, you begin to develop different segmentation models
that can intersect with real-world locations in different ways. Perhaps improved
resolution, precision, and hopefully quality of these expanded models will
account for any diminished precision associated with the gradual anachronistic
features of traditional approaches to geographic localization.


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