What is our data actually telling us?
A one-person fundraising office had 11 years of donor records and no way to see them whole. I built an interactive dashboard that leads with what to do next.
- Data analysis
- Insight to recommendations
- Self-serve reporting
Pro bono engagement. The Lamb Center, through Catchafire, April 2026. Donor records belong to the client, so every screen here is a schematic and the forecast runs on invented figures.
Start from the export
Eleven years of giving by about 5,000 donor households, exported from the client’s donor database as spreadsheets.
Schematic. Donor records are the client’s and are not shown.
Decide how to count before counting
Three decisions change every number that follows. Each is stated on the dashboard so the reader knows what a figure means.
- 1Households, not records
Gifts from members of one household are counted together.
- 2Credit the real donor
A gift that arrives through a donor-advised fund is credited to the donor behind it.
- 3The client’s fiscal year
July through June, labeled by the year it ends.
Give each question its own screen
Eleven screens in four groups. Pick one to see the question it answers.
Portfolio Overview
What does the whole donor base look like, year by year?
Retention Analysis
How many donors come back each year?
Growth Champions
Whose giving is growing?
At-Risk Donors
Who gave last year and hasn’t yet this year?
Multi-Year Lapsed
Who gave for years and then stopped?
Recurring Donors
Who gives on a schedule?
Top Donors
Which donors carry the most weight?
Donor Segments
How do individuals, faith groups, foundations and companies compare?
Campaigns & Giving
Which campaigns and payment methods bring gifts in?
Forecast Model
What would better retention be worth?
AI Findings Report
What should a one-person office do first?
Schematic. Donor records are the client’s and are not shown.
Lead with what to do
Charts don’t tell a one-person office where to start. The dashboard includes a written findings report that ends in next steps, and a model for testing a plan before committing to it.
Try the slidersForecast model, simplified
- Kept$200,000
- Won back$84,000
- Upgraded$72,000
Sample figures, invented for this page: at-risk donors last gave $400,000, lapsed donors $600,000 and growing donors $900,000. A returning donor gives 70% of the last gift; an upgrade adds 20%. The real model runs on the client’s data.
Rebuild it from the client’s feedback
On a follow-up call the development director asked for two changes. Both went into the next build.
She asked whether the donor lists could be sorted, as in a spreadsheet.
BuiltEvery donor list sorts by column and exports to a spreadsheet.
She pointed out that the organization names a fiscal year by the year it ends.
BuiltEvery year on the dashboard was relabeled to match.
Refreshing the data herself looked like more work than it would save.
OfferedShe sends a fresh export when she needs one, and I rerun the dashboard.
The challenge
The client wanted to see overall giving trends and learn which donors were giving more, which were giving less and why. The records existed. A way to read them didn’t.
What I built
One file that opens in a browser with nothing to install: 11 screens, searchable donor lists, a forecast model and a written findings report.
Where it applies
The same approach turns survey results or campaign data into something a leader can act on: decide how to count, give each question a screen and lead with the recommendation.