CASE STUDY

Higher Education / University

Nonprofit Fundraising

Predictive Scoring and Filtering Simplifies Alumni Fundraising

A university used the boodleAI platform to create a model that predicted which alumni are most likely to become first time donors.  Using this model, the university's development staff was able to in minutes score and filter each returning alumni class to identify the best new donor prospects. Previously, this process took the development team days to complete.

The Challenge

A university's development staff sought to increase the number of new alumni donors by reengaging alumni as they returned for class reunions.  However, the large number of alumni in each class made the process of screening and prioritizing those individuals time consuming and inefficient.

Use of Guidon by boodleAI

Using the boodleAI platform and its donor database, the university created a custom model that predicted which alumni were most likely to become first time donors. The university then uploaded a contact list of each returning class for scoring, filtering, segmenting, and sorting using this custom model, as well as a pre-built model that predicted giving capacity over the next five years. 

The Results

A process that used to take days for each class was instead completed in minutes. This enabled the development staff to start the process of alumni engagement faster and with much greater confidence.  

Features Used

  • Predictive Analytics (Custom Guidon)

Benefits Produced

  • Ability of organization to identify prospects with highest propensity to become a first time donor increased
  • Significant time and costs savings

Investment

  • Purchase of Guidon Annual License
  • Compilation of donor records
  • Compilation of contact lists
  • 10 minutes to upload data to Guidon
  • 20 minutes to create, test, and review a custom guidon
  • 20 minutes to apply instant and custom guidons to contact lists

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