University sees 88% Reduction in Major Gift Donor Lead Time Through Data Modeling
One of WealthEngine’s clients is a large, private, research university in the midwest with an international student body of about 12,000 students.
The university’s fundraising team had a plan in place for fundraising at all levels. However, Major Gifts were a challenge. WealthEngine’s Client Engagement Manager (CEM) suggested that they use wealth modeling as a solution.
The development team was skeptical at first. By explaining that WealthEngine could build a custom model for the university, the CEM was able to convince the Major Gift Officers to try it as a pilot program.
The custom model included categorical and numerical data chosen by the university with guidance from the CEM. Custom data from the university was able to generate a model that automatically identified the top 10% of their donors.
These were donors with gift capacities in the $100,000 range. Over 200 members were identified as top prospects for the university’s major gifts program.
The model instilled confidence in the development team. As the next step of pilot testing, their Major Gifts Officers called a random sample of 10 members from their list. To their surprise, all 10 members answered their call and had a conversation with them. Thus, their effort resulted in a staggering response rate during the pilot stage.
From the 10 phone calls, the university was able to have in-person meetings with 3 major gift prospects. Therefore, the pilot saw a 30% engagement rate. Major Gift Officers are now in serious talks with 2 of the 3 members for gifts at the $100,000 level, resulting in a 20% overall lead conversion rate.
The exercise has made the university realize that only the top 10% of their database has a gift capacity of $20 million. Their major gifts program, therefore, received a significant boost from the custom model developed by WealthEngine. What’s more? Their Major Gifts Officers were able to go from initial phone call to a conversation about actual gift amounts within 2 months. The model was able to cut down the lead time by about 88% compared to the industry average.
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