3 April 2014: The pervasiveness of mobile phones in developing economies has made them an ideal platform for providing services centered on improving local living conditions. Although these platforms have their strengths and their limitations, they provide a means to reach millions of citizens in under-serviced communities in both urban and rural areas.
Our research in the area of technologies for emerging markets explores two differentiated lines: (i) an Observation Approach that focuses on understanding how cell phones are currently being used in developing economies, and (ii) an Intervention Approach, with a focus on the development of new cell phone-based applications in the areas of m-learning, m- government, m-agriculture or m-health.
The adoption of cell phones in emerging and developing economies has generated an unprecedented amount of data that holds information about behavioural insights, dynamics and mobility patterns in these countries. The analysis of these large datasets through machine learning techniques can help formulate usage trends characteristic in emergent economies. These analyses are of interest to both policy makers interested in the assessment of technology-based programs, and technologists and technology companies focusing on the development of personalized services for emerging economies.
Specifically, we focus on applying machine learning techniques to evaluate the impact that demographic and socio-economic indicators might have in the way people use technologies. The socioeconomic status of a population or an individual provides an understanding of its access to housing, education, health or basic services like water and electricity. In itself, it is also an indirect indicator of the purchasing power and as such a key element when personalizing the interaction with a customer, especially for marketing campaigns or offers of new products.
Traditionally, these studies have been carried out through interviews or focus groups. Our approach enhances the state- of-the-art by being able to draw conclusions automatically and from larger populations.
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