Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries
Muhammad Khan, University of Washington; Joshua Blumenstock*, University of Washington
Billions of people around the world live without access to banks or other formal ﬁnancial institutions. In the past several years, many mobile operators have launched “Mobile Money” platforms that deliver basic ﬁnancial services over the mobile phone network. While many believe that these services can improve the lives of the poor, in many countries adoption of Mobile Money still remains anemic. In this paper, we develop a predictive model of Mobile Money adoption that uses billions of mobile phone communications records to understand the behavioral determinants of adoption. We describe a novel approach to feature engineering that uses a Deterministic Finite Automaton to construct thousands of behavioral metrics of phone use from a concise set of recursive rules. These features provide the foundation for a predictive model that is tested on mobile phone operators logs from Ghana, Pakistan, and Zambia, three very diﬀerent developing-country contexts. The results highlight the key correlates of Mobile Money use in each country, as well as the potential for such methods to predict and drive adoption. More generally, our analysis provides insight into the extent to which homogenized supervised learning methods can generalize across geographic contexts. We ﬁnd that without careful tuning, a model that performs very well in one country frequently does not generalize to another.
Filed under: Classification