With 30 years as an advancement executive and consultant under my belt at the time, I had this nagging feeling about how I, or my team, arrived at major gift ask amounts. So, I set out to address this vexing issue.
Research: I conducted research over several years during live sessions, asking 400+ fundraisers to suggest what pledge they would ask for using two case studies. One of a small and large donor using 21 data points about the donors. I kept the results, and when compiled, I found the variability in their responses was stark. The suggested ask amounts were:
Small Donor: $15K – $250K
Large Donor: $50K – $3M
This data illustrates the enormous influence of fundraiser money bias. Donor AbacusTM grew from this work.
Integrity: Our profession needs a bias-free, data-driven approach to bring integrity to our work with major donors. You can collect all the donor research you can, but you still need a rationale for an ask amount that is not too low, leaving money on the table or insulting the donor, and not too high, so you don’t lose your donor’s respect.
Example of bias: My animal welfare campaign client “saved” $50K by using Abacus. Abacus recommended asking a loyal donor couple for a pledge of $100K for the campaign. The CEO and board chair got cold feet when they entered the donors’ home because it was far from updated. They signaled to each other to go to plan B, a $50K ask. During the discussion about the campaign, the couple volunteered a pledge of $100K. If the presentation had gone to completion, the ask would have been $50K, potentially leaving $50K on the table.
Abacus fix: Personal biases about how people live, look, and behave must be removed from your equation. Abacus uses objective data about how the donor interacts with your organization so you can prepare for and have confidence during Gift Conversations with affluent donors, accurately value their Major Donor Pipeline and Portfolios, plan Capital Campaign Feasibility Studies and Campaigns, and manage Donor Portfolios and Fundraising Staff.
Abacus is remarkably accurate in predicting actual pledges.