Data-driven Development

Project Summary

Matthew Newman (Sociology), Sonia Xu (Statistics), and Alexandra Zrenner (Economics) spent ten weeks exploring giving patterns and demographic characteristics of anonymized Duke donors. They worked closely with the Duke Alumni Affairs and Development Office, with the goal of understanding the data and constructing tools to generate data-driven insight about donor behavior.

Themes and Categories
Year
2016

Project Results

The team used a variety of statistical techniques to build predictive models of giving behavior, and they also used sophisticated high-dimensional clustering techniques to group donors according to similarity of demographic characteristics and university experience. A key finding was that high-giving and low-giving donors exist in every cluster, an insight which will aid the Development Office in constructing strategies to cultivate future donors.

Download the Executive Summary (PDF)

Client

  • Stephen Bayer, Associate Vice President for University Development
  • Nathalie Spring, Duke University Development

Faculty Sponsor

Project Manager

Participants

Disciplines Involved

  • Economics
  • Psychology
  • All quantitative STEM

"The Data+ program was filled with intelligent people from all different fields, so it was a great learning experience. Furthermore, since we worked in teams, it taught me how to work with others in a more efficient, collaborative, and overall better level. Working to meet our clients' needs, I feel as if I gained real-world work experience in a classroom-like atmosphere (project mentor as my teacher, my group as the students). It is a great transition for people who are unsure of what they want to do with their careers or feel under-qualified to pursue a real internship."

-Sonia Xu, Duke University Statistics

Related People

Related Projects

Brooke Erikson (Economics/Computer Science), Alejandro Ortega (Math), and Jade Wu (Computer Science) spent ten weeks developing open-source tools for automatic document categorization, PDF table extraction, and data identification. Their motivating application was provided by Power for All’s Platform for Energy Access Knowledge, and they frequently collaborated with professionals from that organization.

Click here to read the Executive Summary

 

Jake Epstein (Statistics/Economics), Emre Kiziltug (Economics), and Alexander Rubin (Math/Computer Science) spent ten weeks investigating the existence of relative value opportunities in global corporate bond markets. They worked closely with a dataset provided by a leading asset management firm.

Click here for the Executive Summary

Maksym Kosachevskyy (Economics) and Jaehyun Yoo (Statistics/Economics) spent ten weeks understanding temporal patterns in the used construction machinery market and investigating the relationship between these patterns and macroeconomic trends.

They worked closely with a large dataset provided by MachineryTrader.com, and discussed their findings with analytics professionals from a leading asset management firm.

Click here to read the Executive Summary