The Full Story:
Each year, the Musical Instrument Museum runs a fall appeal reaching tens of thousands of constituents. Historically, segmentation decisions were based on past performance and observed donor behaviors.
This approach separated museum visitors and concert attendees into distinct audiences with different messaging strategies. While the approach had produced results, the team began questioning whether it reflected how donors actually engaged with the museum.
To test a new approach, they analyzed four years of appeal data and built a predictive model using statistical analysis.
The model produced a ranked list of roughly 25,000 constituents and surfaced new prospects not typically included in prior appeals. It also reinforced a unified messaging strategy centered on the Artist Residency program, which connects education and performance programming.
The 2025 fall appeal results showed strong gains across revenue, participation, and gift size, including the largest gift of $20,000. Beyond performance, the model revealed that donor engagement is more interconnected than traditional segmentation had captured, and predictive modeling can surface that fuller picture.
