Awhile back I came across this slide from the Money for Good project, which I thought was a sobering quantification of the rarity of donor decision-making based on nonprofit outperformance (cost-effectiveness etc). Hope Consulting got this data by surveying 4,000 US individuals with household incomes >$80k (top 30% income back in 2009, comprising 75% of overall individual donations), of which 2,000 were in the >$300k bracket.
Opportunity size for US retail donors in 2009 was ~$45B, so this works out to ballpark $1-1.5B which is still sizeable, e.g. it’s more than total annual EA grantmaking has ever been:
How did Hope Consulting get the 3% figure? Top of funnel:
Middle of funnel steep drop-off:
and:
Bottom of funnel has even steeper drop-off, because confirmation bias is the default:
How to raise the 3% figure for donors who give based on nonprofit outperformance? Hope Consulting suggest this framing:
(I disagree with Hope Consulting on that last point, but the rest seems useful.)
What are midsized retail donors like? I used to work in marketing analytics so this piqued my interest. Max / diff to elicit donor value trade-offs → cluster analysis (a few rounds) yielded these “donor personas”:
The lack of demographic variation somewhat surprised me:
As a closing note, the Money for Good project was a major undertaking: 6 months, 4 major funders (including Rockefeller), 4 research orgs (!) partnering with Hope Consulting, etc. This makes me wonder what the 80⁄20 version of this could look like, with judicious use of Claude Code and such.
Awhile back I came across this slide from the Money for Good project, which I thought was a sobering quantification of the rarity of donor decision-making based on nonprofit outperformance (cost-effectiveness etc). Hope Consulting got this data by surveying 4,000 US individuals with household incomes >$80k (top 30% income back in 2009, comprising 75% of overall individual donations), of which 2,000 were in the >$300k bracket.
Opportunity size for US retail donors in 2009 was ~$45B, so this works out to ballpark $1-1.5B which is still sizeable, e.g. it’s more than total annual EA grantmaking has ever been:
How did Hope Consulting get the 3% figure? Top of funnel:
Middle of funnel steep drop-off:
and:
Bottom of funnel has even steeper drop-off, because confirmation bias is the default:
How to raise the 3% figure for donors who give based on nonprofit outperformance? Hope Consulting suggest this framing:
(I disagree with Hope Consulting on that last point, but the rest seems useful.)
What are midsized retail donors like? I used to work in marketing analytics so this piqued my interest. Max / diff to elicit donor value trade-offs → cluster analysis (a few rounds) yielded these “donor personas”:
The lack of demographic variation somewhat surprised me:
As a closing note, the Money for Good project was a major undertaking: 6 months, 4 major funders (including Rockefeller), 4 research orgs (!) partnering with Hope Consulting, etc. This makes me wonder what the 80⁄20 version of this could look like, with judicious use of Claude Code and such.