Description
With an increasing number of avenues for philanthropy available to charitably inclined citizens, university offices of development are thinking of new means to identify and engage donors for consistent giving. In order to establish proof of principle for a new approach, we have analyzed large amounts of giving data captured by the various entities at Yale.
We will present the development of predictive models for two types of giving to Yale. One model estimates the likelihood of donating to Yale through selected types of charitable contributions, including charitable gift annuities; a second model estimates alumni participation in 50th reunion gift campaigns. Data identification, preparation, curation and analysis for these models required input and collaboration from multiple cohorts across the University. The results from the models illustrate the complexities of incorporating statistical analysis into pathways for giving that have traditionally relied on personal connections to identify and engage alumni and affiliates.
Yale Development’s predictive analysis efforts differ significantly from big data analyses undertaken in typical research projects in pharma and other sectors, yet share the common goal of informing future strategies. Our analyses will help in understanding current trends in higher education fundraising; the scope of information collected and maintained by Yale’s Office of Development; how that data is used and protected; and some of the characteristics unique to Yale’s best fundraising prospects.
Included in
Using data to guide strategy: enhancing donor engagement at Yale University
With an increasing number of avenues for philanthropy available to charitably inclined citizens, university offices of development are thinking of new means to identify and engage donors for consistent giving. In order to establish proof of principle for a new approach, we have analyzed large amounts of giving data captured by the various entities at Yale.
We will present the development of predictive models for two types of giving to Yale. One model estimates the likelihood of donating to Yale through selected types of charitable contributions, including charitable gift annuities; a second model estimates alumni participation in 50th reunion gift campaigns. Data identification, preparation, curation and analysis for these models required input and collaboration from multiple cohorts across the University. The results from the models illustrate the complexities of incorporating statistical analysis into pathways for giving that have traditionally relied on personal connections to identify and engage alumni and affiliates.
Yale Development’s predictive analysis efforts differ significantly from big data analyses undertaken in typical research projects in pharma and other sectors, yet share the common goal of informing future strategies. Our analyses will help in understanding current trends in higher education fundraising; the scope of information collected and maintained by Yale’s Office of Development; how that data is used and protected; and some of the characteristics unique to Yale’s best fundraising prospects.