Date: Thursday, February 26, 2026 1:00 PM - Thursday, February 26, 2026 2:00 PM
Just about every fundraising organization, large or small, must answer some version of the “What’s in our pipeline?” or “How much are we going to raise?” question during the year. But while the use of predictive prospect scoring models has become industry commonplace in recent years, similar efforts for proposal pipeline forecasting have seemingly lagged in adoption, with many organizations still relying on “back-of-the-napkin math”. In an industry where most teams live or die by meeting their annual fundraising goal, how can organizations harness their data to build more accurate pipeline forecasts?
This presentation will detail one fundraising professional’s work developing a machine learning system to predict which proposals are likely to close and how much an organization can expect to raise for the year based on its existing pipeline. It will introduce attendees to the data science process, provide a walkthrough of how to perform a no-code correlation analysis in Excel, and explain the fundamentals of common machine learning approaches such as logistic regressions and decision trees. Along the way, it will present several statistical findings on proposal outcomes that even non-technical shops and professionals can use to inform their own forecasting systems, pipeline metrics, and portfolio review programs.
If you’ve ever found yourself wondering “How much can we expect to get from our current pipeline?”, “Why did those two major gift proposals have totally different outcomes?”, and “Which proposals are actually going to close this year?” This presentation is for you.
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