The Hidden Assumptions Behind Common Metrics

Most organizations have a few numbers they return to again and again.

Enrollment. Retention. Response time. Satisfaction. Engagement. Utilization. Open rate. Headcount. Completion rate. Time to resolution. Number of programs offered. Number of people served. 

These metrics often become part of the organization's language. They show up in board reports, annual updates, cabinet meetings, strategic plans, dashboards, performance evaluations, and more. Over time, they start to feel neutral. Objective. Obvious. 

“Of course we track retention! Of course we measure satisfaction! Of course we monitor productivity!”

And, to be clear, these metrics matter. Good metrics can help organizations notice patterns, test assumptions, make decisions, tell stories, and stay accountable to the people they serve. There’s not a good case against measurement, but we believe there is a case for slowing down long enough to ask a better question: what are we assuming these metrics mean?

Every metric carries a story. Some of those stories are thoughtful and intentional, while others are inherited, outdated, incomplete, or quietly misaligned with the work we say matters most. The issue isn’t the use of common metrics; it's that organizations often use these metrics without naming the underlying assumptions and the purpose behind them. 

A metric is never just a number

Take participation, for example. Many organizations track how many people attend a program, complete a form, or use a service. Participation is relatively easy to track, report, and compare year-over-year or event-to-event. But these participation metrics often carry a hidden assumption: that if more people participate, the work must be more valuable. Sometimes that is true. If a food pantry serves more families, that may indicate increased reach. If more high school students access tutoring services, that may mean barriers to academic support are lower. If more employees attend a professional development session, that may reflect stronger engagement in the workplace. But participation can also mean something else. More people attending a meeting may mean the topic is confusing and people are looking for clarity. More service requests may mean a system is harder to navigate. More college students using emergency funds may reflect deeper financial instability or concerns about the economy. 

Additionally, standard participation rates, such as simple card swipes or count data, don’t always tell the whole story. People can attend an event for five minutes or two hours, and those experiences will be vastly different. Someone can swipe into an event because it’s mandatory (or, let’s be honest, because there’s free food) and get no actual value from the program. Participation metrics are available. The numbers are real, but the meaning is not automatic. This is where we see organizations get into trouble. They treat the metric as the conclusion instead of the beginning of a much deeper conversation. 

Common metrics often reward what is easiest to see

Many of the most common metrics used in organizations are not necessarily the most meaningful – they are the most visible. 

It is much easier to count how many people attended a workshop than to understand whether or not the workshop achieved its espoused learning outcomes. It is much easier to track the number of initiatives launched than to assess whether the organization had the capacity to implement them effectively. It is much easier to count how quickly a team responds to a service request than to understand whether the response actually solved the problem long-term. 

Again, none of these metrics are bad on their own. Volume, speed, and satisfaction can all tell us something useful, but they rarely tell us the full picture alone. A long list of completed tasks does not always equal meaningful progress. A fast response is not always a good one. A high satisfaction score does not always mean deep learning. This matters because what we measure often shapes what people prioritize. If we celebrate volume, people may aim for quantity over quality. If we reward speed, people may optimize speed at the expense of orientation to detail. If we only ask whether people are satisfied, teams may avoid the discomfort that comes with meaningful change. 

The danger is not the metric itself, but in mistaking the metric for the whole story. 

Metrics can hide values we have not named

One of the most important things a metric does is reveal what an organization values, whether intentionally or not. 

For example, when an organization tracks “time to resolution,” it may be valuing responsiveness, efficiency, and accountability. Those are all good things! But if that metric becomes the primary (or only) way a team’s performance is judged, staff may learn that closing the ticket matters more than understanding the person’s experience. 

When a college tracks “number of students served,” it may be valuing access and reach. Also good things! But if the college never asks who is not being served, what barriers remain, or whether the service actually meets the students’ need, the metric may create a false sense of success. 

Metrics can make some things visible while pushing other things into the background. That does not mean we should abandon them; it means we should interrogate them. A useful metric should invite better questions, not shut them down. 

The hidden assumptions worth naming

When we work with organizations on strategy, assessment, or stakeholder engagement, we often see the same pattern: The organization has the data, but it has not always created space to examine the assumptions behind the data. 

Here are a few questions that can help:

• What do we believe this metric tells us? Be specific. Not “this tells us how we are doing,” but “this tells us whether people are accessing the service,” or “this tells us how quickly we are responding to requests.” A clear answer helps prevent the metric from being stretched beyond what it can reasonably say. 

• What does this metric not tell us? Every metric has its limits. Naming those limits is not a weakness; it is a sign of thoughtfulness. A retention rate does not tell you why people stayed or left. A satisfaction store does not tell you what tradeoffs people are willing to accept. A utilization number does not tell you whether the service was easy to use or effective. 

• What behaviors might this metric unintentionally encourage? People respond to what gets measured, because it speaks to what the organization values. This is especially true when metrics are tied to things like evaluation, funding, reputation, promotion, or leadership recognition. If the organization always rewards the metric of speed, people may move quickly at the expense of depth. If it rewards activity, staff may create more unnecessary programs instead of improving and investing in those that already exist. 

• Whose experience is centered in this metric? Some metrics look clean at the aggregate (or whole) level while hiding very different experiences underneath (at the disaggregate, or sub-group level). Averages can flatten the story. Overall satisfaction can mask frustration for specific user groups. Organization-wide engagement can hide team-level dysfunction. A metric may be accurate and still incomplete. 

• Does this metric match our current goals? Organizations keep measuring something because they have always measured it. But strategy changes. Communities change. Expectations evolve. A metric that made sense five years ago may no longer reflect what the organization needs or values most today. 

Better measurement starts with better conversations

We encourage organizations to treat metrics as conversation starters instead of final answers: When a number goes up or down, ask: why? What changed? When a number stays the same, ask what the metric might be missing. When a number looks good, ask who is still not experiencing the benefit. When a number looks bad, ask whether the metric is still capturing the right thing. 

Good measurement does not remove judgment from leadership. It improves the quality of judgment. It gives leaders, teams, and communities a shared space to begin, then asks them to bring the context, curiosity, and courage to the interpretation. 

This is especially important in mission-driven organizations, where the work is often relational, complex, and deeply human. The most important outcomes are not always the easiest ones to count: trust, belonging, confidence, clarity, alignment, learning, momentum. These things can be measured, but rarely through one simple number. 

They may require multiple forms of data. Quantitative numbers. Qualitative feedback. Stakeholder stories. Process review. Observation. Benchmarking. Reflection. Sometimes the most important insight comes from the gap between what the metric says and what people are experiencing. The goal is not simply to measure more. It is to learn more. Use metrics to spark the conversations that help your team see what is working, what is missing, and what needs attention next. When data and lived experience are considered together, measurement becomes a tool for more thoughtful, responsive, and meaningful action.