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Most alumni relations and annual giving operations have limited intelligence about who will be a strong reunion volunteer, annual giver, or alumni travel prospect—if the person has not previously participated in any of those activities. But rather than pulling a random database query and then reaching out at random to the contents of the entire resulting list, applying predictive analytics can help provide a more targeted allocation of your resources and more targeted messaging.
In this report, you’ll read about using predictive modeling to target:
- Alumni for reunion-year annual giving
- Alumni for giving in a non-reunion year—especially looking for prospects among lapsed and never givers
- Institutional “friends” for annual giving
- Prospective alumni reunion volunteers
- Alumni who are most interested in alumni tours
Learn from the University of Pennsylvania’s low-cost, minimal-staff approach, and take an in-depth look of the independent variables that Penn found to be statistically significant and helpful in guiding decisions in annual giving and alumni relations. With these variables in hand, you’ll be able to more effectively scour your own database to find the best prospects for various alumni relations and annual giving functions.
- If you already have an advanced knowledge of predictive analytics, read this report for insights on variable definition and selection, model structure, data conditioning, and assessment of model “validity” and performance.
- If you have little or no prior background in predictive modeling, read this report to gain an understanding of how one uses predictive modeling, the data requirements, the results you can obtain, and the potential impact for your department.
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