By taking a close look at the respondents' mobile activities, he was able to discern patterns that could be used to predict illiteracy. For instance, Sundsøy found that they don't send out a lot of text messages, if any, and tend to communicate with very few people. They also spend most of their time in poor regions, such as slums, which could mean that they don't hold jobs in wealthier locations that would have required them to write and send out resumes.

Sundsøy said "deriving economic, social and mobility features for each mobile user" enabled him to "predict individual illiteracy status with 70 percent accuracy." His method still requires more testing, but if it really is that spot-on, non-profit orgs could adopt it to make sure they allocate enough resources for areas where they're most needed.