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Through seven publications this dissertation shows how anonymized mobilephone data can contribute to the social good and provide insights into humanbehaviour on a large scale. The size of the datasets analysed ranges from 500million to 300 billion phone records, covering millions of people. The keycontributions are two-fold:  1. Big Data for Social Good: Through prediction algorithms the results showhow mobile phone data can be useful to predict important socio-economicindicators, such as income, illiteracy and poverty in developing countries.Such knowledge can be used to identify where vulnerable groups in society are,reduce economic shocks and is a critical component for monitoring poverty ratesover time. Further, the dissertation demonstrates how mobile phone data can beused to better understand human behaviour during large shocks in society,exemplified by an analysis of data from the terror attack in Norway and anatural disaster on the south-coast in Bangladesh. This work leads to anincreased understanding of how information spreads, and how millions of peoplemove around. The intention is to identify displaced people faster, cheaper andmore accurately than existing survey-based methods.  2. Big Data for efficient marketing: Finally, the dissertation offers aninsight into how anonymised mobile phone data can be used to map out largesocial networks, covering millions of people, to understand how products spreadinside these networks. Results show that by including social patterns andmachine learning techniques in a large-scale marketing experiment in Asia, theadoption rate is increased by 13 times compared to the approach used byexperienced marketers. A data-driven and scientific approach to marketing,through more tailored campaigns, contributes to less irrelevant offers for thecustomers, and better cost efficiency for the companies.

Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data