United StatesHealthcare and life sciences2024 – ongoingChildren's Hospital of Philadelphia
Machine learning for donor targeting
CHOP reported an 85% higher fundraising response rate with machine-learning-supported donor targeting
Problem
Conventional donor segmentation limited CHOP's understanding of supporters and its ability to personalize fundraising.
Approach
Moore integrated its Azure-based SimioCloud platform into CHOP's analytics engine, using machine learning to identify giving patterns. Predictive models identified potential supporters and lapsed donors for targeted outreach.
Results
Takeaway
CHOP's donor targeting combined giving predictions with supporters' hospital relationships to personalize outreach.
Categories
- Size
- Large enterprise
- Industry
- Healthcare and life sciences
- Business function
- Marketing and sales
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Vendor solution
- Evidence
- Vendor case study
Sources
Source summaries
Microsoft described CHOP's use of Moore's machine-learning donor models. It reported an 85% increase in response rate and a 25% increase in gift size.
Full details are in the original sources.
