Abstract
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. In this paper we study if the information derived from the aggregated use of cell phone records can be used to identify the socioeconomic levels of a population. We present predictive models constructed with SVMs and Random Forests that use the aggregated behavioral variables of the communication antennas to predict socioeconomic levels. Our results show correct prediction rates of over 80% for an urban population of around 500,000 citizens.
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Soto, V., Frias-Martinez, V., Virseda, J., Frias-Martinez, E. (2011). Prediction of Socioeconomic Levels Using Cell Phone Records. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_35
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DOI: https://doi.org/10.1007/978-3-642-22362-4_35
Publisher Name: Springer, Berlin, Heidelberg
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