RED CROSS
Predictive Modeling to Win Back Lapsed Donors
Background
The Canadian Red Cross runs several direct-mail campaigns annually. They maintain a database of current and lapsed donors and sometimes exchanges lists with other non-profit organizations to use in prospecting campaigns. They found that results yielded from that approach were declining in recent years.
Analytic Objectives
The objective was to find the defining features of the Red Cross’s strongest regular donors, then use those features to identify lapsed donors most similar to its strongest donors.
Manifold’s Approach
We found the defining features of the Red Cross’s strongest donors using our postal code-level data. We then identified the top prospects who were most similar to the Red Cross’s donor profile.
Manifold’s Data Products Used in Targeting Lapsed Donors
The following variables in Manifold’s data products played a significant role in targeting lapsed donors:
- Demographics
- Household Spending Patterns
- Lifestyle Clusters (CanaCode)
Benefits of Using Manifold’s Data
The Red Cross mailed 22,000 copies of the same appeal to a control group and the Manifold top prospects group and the Red Cross found a 53% higher response rate than the control group, a 34% higher average gift per donor, and 104% greater revenue per name mailed than the control group.