November 21, 2024
AI Can Be Your Most Powerful Tool for Growing Midlevel Giving
Tips and best practices for building a reliable and efficient task management process.
Greenpeace AU wanted to reduce churn in their regular giving program. Using Dataro’s RG Churn propensity scores to identify the most ‘at risk’ donors, they commenced a monthly ‘Thank You’ calling program with GiveTel. The aim was to proactively engage with at risk donors to reduce churn. With this approach, Greenpeace saved an estimated 64 donors in a single month, raising an estimated extra $23,040 for the planet.
Greenpeace achieved a significant reduction in churn by contacting at risk regular givers via a ‘thank you’ calling program. This graph compares the expected churn rate, based on Dataro’s predictive scores, against actual churn in the call group, showing a significant improvement.
Dataro used machine learning to generate propensity scores for all of Greenpeace’s active regular givers. These scores showed how likely each donor was to miss three gifts in a row in the next 6 months. Donor scores were loaded directly into Salesforce, allowing Greenpeace to select the donors most at risk of churn to be included in GiveTel’s calling program. Three months after the calls we saw:
This sort of Engage & Retain campaign is only possible thanks to highly accurate predictive scoring.
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