Welcome to Part 3 of our “Actually Useful Maths for Data Work” series.
Written by our very own Anthony Fawkes, this five-part series will provide fundraisers, data managers, analysts, and CRM heroes holding charity operations together with easy-to-apply solutions that strip away the stress from maths. The aim is to show you how everyday calculations (and a bit of logic) can level up your data confidence.
What Is Forecasting (And Why Should You Bother)?
Forecasting is just a fancy word for educated guessing, backed by data. In fundraising, it helps you plan realistically, spot shortfalls, allocate time and money better, and set clearer expectations.
Start With What You Know
You don’t have to predict the future from scratch. Look at past data: income trends, campaign performance, and conversion rates.
The Donor Funnel: Forecasting From the Ground Up
Example: 5,000 contacts -> 30% open -> 10% click -> 5% convert -> £25 avg gift. That gives you a realistic estimate of campaign return.
What Tools Can You Use?
| Task | Excel | Power BI | Python |
|---|---|---|---|
| Simple Forecasting | =TREND, =FORECAST.LINEAR | Forecast visuals | pandas, statsmodels, prophet |
| Budget scenarios | Manual modelling | What-if parameters | Dynamic scripts with inputs |
| Seasonality detection | Manual with AVERAGEIFS | Line charts, filters | Time series decomposition |
| Alerts/automation | N/A | Alerts in dashboards | Scheduled scripts + emails |
Including Costs? Staff Time? You Bet.
Consider total ROI. Factor in staff time, venue costs, printing, and opportunity cost to understand the true value of a campaign.
What About Forecasting Regular Giving?
Use cohort analysis. Track donor lifespan and average monthly gift to project ongoing income. Use Excel, Power BI, or Python.
TL;DR
Start small, layer in more detail, and automate when ready. A decent forecast beats a hopeful guess every time.
Coming Up Next in the Series…
- Post 4: Attrition Modelling – Spotting Supporter Drop-off Before It Hurts
- Future Topics: Python Automation
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