Define Success - Identify the metrics that matter most
How to Use A Program Strategy to Drive Smarter Data Investments
Written by Gregory V. Jaros and Andrea Reyes, leveraging Advance Data Strategy’s proprietary methodologies developed by Joseph Lanasa and Ruby Liu.
This three-part series explores how organizations can bring structure, clarity, and confidence to their data investment decisions. At Advance Data Strategy, we help organizations treat their data as they would any other strategic investment—by quantifying its value. Our Portfolio management Service evaluates data initiatives across people, process, and technology, using cost-benefit analysis and ROI modeling to inform planning, budgeting and prioritization.
In this series, we share the methodology we use with clients to prioritize high-impact efforts and align data strategy with business outcomes. Our approach centers extensive collaboration and customization, empowering stakeholders at every step of the program planning and implementation.
This series includes:
- Start with Strategy – How to Build a Value-Driven Data Roadmap
- Define Success – Identify the Metrics That Matter Most
- From Metrics to Modeling – Calculate ROI and Prioritize Initiatives
Whether you’re launching new tools or reevaluating existing ones, this series will help you focus your resources where they’ll deliver the greatest return.
You’re reading Part 2: Define Success – Identify the Metrics that Matter Most
From Vision to Measurement
In Part 1, we discussed how a program strategy helps build a business case for data initiatives. The first step in that process—defining what success looks like—depends entirely on one thing: metrics.
Metrics create the bridge between strategic goals and measurable outcomes. They help leaders track progress, evaluate impact, and compare initiatives objectively. Without the right metrics, you’re left with intuition and assumptions instead of data-backed decisions.
Start with What You Know: Standard Metrics
Most organizations already track some level of core metrics—especially in areas like fundraising, donor engagement, and operational efficiency. These standard metrics are a great starting point because they provide benchmarks and continuity across time.
For Advancement and fundraising organizations, common examples include:
- Total dollars raised
- Cost per dollar raised
- Number of gifts closed
- Total asks made
- Total prospects assigned
- Ratio of managed to unmanaged major gift prospects
These metrics help quantify core performance and provide foundational insight into program effectiveness.
Go Deeper: Derived and Custom Metrics
Standard metrics tell only part of the story. The most meaningful insights often come from custom or derived metrics that are tailored to your institution’s goals, strategies, or operational context.
Examples include:
- Average major gift value— Indicates deal size and gift pipeline health
- Annual giving converted donors— Measures movement into mid/major pipelines
- Median CRM interactions logged— Reflects adoption and usage quality
- Unmanaged major gift prospects— Identifies gaps in cultivation coverage
- Ratio of gifts closed to managed prospects— Gauges fundraiser effectiveness
- Estimated productivity hours gained— Helps quantify manual effort reduction
These metrics add nuance and context to benefit modeling and are essential when quantifying the impact of process or technology improvements.
Why This Matters for ROI
By defining and agreeing on relevant metrics up front, you set the stage for clear, consistent ROI modeling. This ensures:
- Leadership alignment around what matters
- A shared language across teams (IT, Advancement, operations)
- More credibility in funding and planning conversations
When your ROI model reflects your real-world performance indicators, it’s much easier to earn stakeholder confidence—and much harder for initiatives to get derailed by opinion or assumption.
Final Thought
Not all metrics need to be perfect or complete to be useful. You don’t have perfect data—no one does. But that shouldn’t stop you from getting started. Even without a fully established data governance structure, you can begin by tracking what you have and what you know now. Choosing and tracking the right metrics early gives your data program the clarity and accountability it needs to scale.
Coming Next: From Metrics to Modeling—Calculate ROI and Prioritize Initiatives