Spencer MacColl — Founder
Combining predictive impact analytics, decision frameworks, and learning systems to ground funding decisions in evidence, explicit assumptions, and clear tradeoffs, not intuition alone.
Where most funders get stuck
Most funders can clearly define what they fund and who they aim to reach. The harder questions, how much change actually occurs, and whether it would have happened anyway, remain largely unaddressed.
This is where I focus. Bringing structured, evidence-informed estimation into the decisions that matter most, while maintaining intellectual honesty about what the evidence can and cannot support.
How I Support Decisions
View all services →Decision platforms to compare expected impact across investments, incorporating scenario analysis and uncertainty to support capital allocation decisions.
→Decision-ready social ROI analysis for individual investments, combining evidence, explicit assumptions, and modeling to estimate cost-effectiveness, expected impact, and key uncertainties in 2–4 weeks.
→Portfolio learning strategy with AI-enabled systems that turn portfolio data and grantee input into real-time, decision-relevant insight, reducing reporting burden while improving learning.
→Frameworks that define what outcomes matter and how to measure them, aligning funding strategy, metrics, and evaluation to decision-making, not just reporting.
→Why this work matters
This work is ultimately about improving how capital is allocated, so that funding decisions are more likely to produce meaningful, measurable improvements in people's lives.
Decision-relevant rigor
Use the best available data and benchmarks. Make assumptions explicit. Quantify where it improves decision-making. Avoid false precision where evidence is weak.
Numbers inform judgement
Analytics are one part of decision systems that must also account for population priorities, equity considerations, and implementation risk.
Honest about uncertainty
The goal is not perfect measurement. It is better decisions, faster learning, and clearer tradeoffs, with transparent assumptions at every step.