The answer to that question might be more clear than you think. Your customers do, in fact, post plenty of personal information about themselves online that allow you to draw conclusions about them. You just have to know how to listen.
Take Twitter as an example, a social network often derided by marketers for its lack of targeting options. Unlike competitors like Facebook, which allow you to target your messages based on in-depth data like income, education level, and more, Twitter stops at geographical boundaries and stated interests. That’s not a lot to go off of - until you begin to analyze the issue more deeply.
In a recent study, University of Pennsylvania researchers analyzed more than 10 million tweets on the network, and came to an astounding conclusion. Having analyzed a portion of their sample according to self-professed surveys, they built a machine learning model that accurately predicted the financial means of the authors of these tweets.
As it turns out, lower-income people used the network as a means of social interaction, while their higher-income equivalents focused more on distributing information.
In separate studies, the team, led by post-doctoral researcher Preot¸iuc-Pietro, was able to replicate the same techniques for age, gender, political leanings, and even signs of PTSD.
If you think about it, those results make perfect sense. Of course our digital messages are a reflection of who we are. Analyzing those messages to better understand and reach your customers may be difficult, but is becoming increasingly possible. And this in-depth analysis and listening is precisely what will allow us to establish a truly user guided approach to marketing and product development.