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Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty.

, , , and . BigData Congress, page 677-680. IEEE Computer Society, (2015)

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Moving targets: When does a poverty prediction model need to be updated?, , and . COMPASS, page 117. ACM, (2023)Note: Home Location Detection from Mobile Phone Data: Evidence from Togo., , and . COMPASS, page 685-692. ACM, (2022)Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda., and . ICTD, page 6:1-6:10. ACM, (2010)Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty., , , and . BigData Congress, page 677-680. IEEE Computer Society, (2015)Predicting Student Dropout in Higher Education., , , and . CoRR, (2016)Observing gender dynamics and disparities with mobile phone metadata., , and . ICTD, page 48. ACM, (2016)Understanding the Impact of Urban Infrastructure: New Insights from Population-Scale Data., , and . ICTD, page 4:1-4:12. ACM, (2017)Fairness and Representation in Satellite-Based Poverty Maps: Evidence of Urban-Rural Disparities and Their Impacts on Downstream Policy., , and . IJCAI, page 5888-5896. ijcai.org, (2023)Mining University Registrar Records to Predict First-Year Undergraduate Attrition., , , , and . EDM, International Educational Data Mining Society (IEDMS), (2019)An Investigation of Phone Upgrades in Remote Community Cellular Networks., , , , , , , and . ICTD, page 6:1-6:12. ACM, (2017)