The Anatomy of Good (And Bad) Metrics

The SMART framework transforms vague tracking ideas into powerful, actionable metrics. This structured approach ensures every metric drives meaningful action and provides clear direction.

A SMART metric includes these essential components:

• Specific: Clearly defines what's being measured
• Measurable: Can be consistently quantified
• Achievable: Sets realistic targets that stretch but don't break team morale
• Relevant: Directly connects to business objectives and user needs
• Time-bound: Includes clear timeframes^[1]

For example, "percentage of users who complete at least 3 tasks per week" is a SMART metric. It's specific (defines exactly what activity counts), measurable (can be tracked as a percentage), achievable (can be influenced through design), relevant (indicates meaningful engagement), and time-bound (measured weekly). But sometimes, you won’t have the right metric yet, and that’s fine. In such cases, your goal can be: “Be able to confidently track this metric within X weeks.” Making it a priority to define and track the right thing is a valid goal on its own.

Vanity metrics look impressive in presentations but provide little actionable insight for product decisions. These deceptive numbers often grow naturally with time or marketing spend, creating an illusion of progress without revealing true business health.^[2]

Common examples of vanity metrics include:

• Total page views or website visits
• Total impressions
• Active users
• Social media followers or likes
• Raw download counts
• Registered user totals (without activity qualification)
• Cumulative customer count (ignoring churn)

These metrics fail the crucial test: "Does this information help us make better decisions?" For instance, knowing you have 10,000 app downloads looks impressive, but if only 500 users become active and just 50 convert to paying customers, the download number alone misleads more than it informs. Instead, focus on metrics that reveal user value and business impact, such as active user retention, feature adoption rates, conversion percentages, and revenue per user. These metrics connect directly to business health and suggest specific actions.

Actionable metrics inform specific decisions and drive meaningful change. Unlike vanity metrics, these powerful indicators create clear links between user behavior, business outcomes, and product development priorities.

The most valuable actionable metrics share these key characteristics:

• They answer specific questions about user behavior
• They connect directly to business goals and revenue
• They suggest clear next steps when values change
• They reveal opportunities for product improvement

Creating actionable metrics starts with identifying the critical user behaviors that drive business success. Then design metrics that measure those behaviors in ways that suggest clear action paths. Finally, establish regular review processes where teams discuss metric changes and decide on specific responses. For example, tracking "percentage of new users who complete key actions within their first session" reveals onboarding effectiveness. If this metric drops, the team knows exactly where to focus: improving the initial user experience through better tutorials, simplified interfaces, or clearer value propositions.

Leading indicators are predictive metrics that signal future outcomes before they happen. For example, feature adoption rates, user engagement frequency, customer satisfaction scores, free trial conversion momentum, and onboarding completion percentages. These metrics act as early warnings. When engagement patterns shift, teams can step in before revenue takes a hit. For instance, a drop in session frequency often signals churn risk, giving product teams time to act before users leave.

Lagging indicators, on the other hand, reflect past performance after the results are in. For example, monthly recurring revenue, customer lifetime value, churn rate, market share, and profit margins. While they confirm what has already happened, they don’t provide much time to react.

A strong analytics approach includes both. Leading indicators help teams stay ahead, while lagging indicators validate the impact of past decisions. Together, they give a clearer view of product health. Leading indicators are also useful when you need to move fast. For example, if your goal is to improve month 2 retention, you don’t have to wait two months to learn if you’re on track. You can use day 1 retention or activation rate as a proxy and course-correct in real time.

Effective product measurement requires both numbers and narratives. Quantitative metrics offer precision and scale through concrete numbers and trends. These include conversion rates, time-on-task measurements, feature usage frequency, error rates, and revenue performance metrics. They excel at showing patterns across large user populations and detecting small changes that might otherwise go unnoticed.

Qualitative metrics provide context and emotional understanding that numbers alone cannot capture. User satisfaction ratings, Net Promoter Scores, sentiment analysis from feedback, usability test observations, and customer support themes all help explain the human experience behind the data. These insights reveal motivations, frustrations, and desires that drive the behaviors quantitative metrics measure.

The most powerful insights emerge when these approaches complement each other. For example, if session duration drops, quantitative data shows the extent, but only user interviews or feedback analysis can reveal whether it's because the product became more efficient (positive) or more frustrating (negative). In practice, you might run a few A/B tests or other quantitative tests, and then switch to usability studies when you’re not sure what to try next. Or you might start with a qualitative study to spot problems or opportunities, then use quantitative testing to measure the impact of changes.

Metrics only matter when they connect directly to business outcomes. This connection manifests through cause-and-effect relationships that teams can test and verify. For instance, higher user retention might directly impact recurring revenue, while increased feature engagement could reduce churn. However, these links are often hard to find, especially for early-stage teams without much data or analytics support. What matters most is building that understanding over time. Keep asking: how do our users create value for the business? Then use each metric to help answer that.

Creating relevance means starting with business goals and working backward. Begin with outcomes like revenue growth, customer acquisition cost, or profitability. Then figure out which user behaviors are most likely to influence those outcomes. Design metrics around those behaviors and not just activity for the sake of tracking. One way to do this is by cohorting users. You can group them by demographics or behavior, then compare business metrics across those groups. For example, look at average revenue for users who activated versus those who didn’t, or lifetime value for users who used a key feature versus those who didn’t. This helps you spot which behaviors actually drive value.

Most companies benefit from establishing a hierarchy of metrics with clear relationships between them. Leading metrics like engagement feed into business metrics like retention, which ultimately support financial metrics like revenue and profitability. This hierarchy ensures everyone understands how their work connects to business success and prevents teams from optimizing for disconnected metrics.

Metrics inevitably shape behavior, sometimes in unexpected ways. When teams face pressure to improve specific numbers, they may optimize for the metric rather than the underlying business goal. This is a phenomenon known as Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure."^[3]

Metric manipulation takes various forms, from harmless optimization to harmful distortion. For example, a team might focus on increasing session duration might add unnecessary friction to tasks, making the product less efficient but "improving" the metric. Similarly, focusing exclusively on short-term conversion rates or choosing revenue as your North Star metric could lead to aggressive tactics that boost numbers while damaging long-term customer relationships.

Detecting metric gaming requires establishing balancing metrics that catch unintended consequences. If optimizing for speed, also measure accuracy. If tracking conversion, monitor retention as well. Creating these health metric pairs helps ensure improvements in one area don't come at the expense of others. Additionally, regularly reviewing user feedback provides qualitative signals that can reveal when metric improvements don't align with actual user experience. Also, choose metrics that resist gaming by measuring outcomes rather than activities. For instance, "number of features shipped" encourages quantity over quality, while "percentage of features with high adoption rates" rewards meaningful innovation.

Product development often involves inherent tensions between different success indicators. Speed conflicts with quality, conversion conflicts with retention, and user satisfaction sometimes conflicts with monetization. Effective measurement frameworks acknowledge these tensions rather than pretending they don't exist.

The first step in balancing competing metrics is recognizing the natural trade-offs in your product. Short-term revenue growth might come at the expense of long-term user satisfaction. Feature simplicity might improve onboarding but limit power user capabilities. Security measures might reduce friction but increase abandonment. Document these relationships explicitly so teams understand the complexity of their decisions.

Creating balanced scorecards helps manage these tensions by ensuring no single metric dominates decision-making. Rather than maximizing any one dimension, aim for healthy performance across multiple indicators. For example, instead of purely optimizing for conversion rates, balance conversion with retention metrics to ensure you're not just acquiring users who quickly leave.

However, this requires regular stakeholder alignment on acceptable performance thresholds for each metric. Establish minimum performance levels for secondary metrics, even while prioritizing improvements in primary ones. This prevents overoptimization and maintains product health across all dimensions.

Metrics create value only when they inform decisions across the organization. Even the most sophisticated measurement framework fails if stakeholders cannot understand, access, or act upon the insights it provides. Your main dashboard should focus on the essentials: your North Star, objectives, and key results.

Other metrics can be placed on dashboards tailored to different stakeholders:

• Executive leadership needs high-level business metrics tied directly to strategy and financials.
• Product managers need detailed behavioral metrics that suggest feature improvements.
• Engineering teams need performance and technical metrics that highlight optimization opportunities.
• Each group requires different levels of detail, context, and visualization.

Accessibility also means creating consistent, trustworthy data sources. When different teams use different definitions or calculation methods for the same metrics, confusion and conflict inevitably follow. Document metric definitions clearly, including exactly how each is calculated, what data sources feed it, and what limitations or caveats apply. This transparency builds confidence and encourages wider adoption.

Visual presentation also dramatically affects comprehension. Dashboards should highlight trends and patterns rather than overwhelming viewers with raw numbers. Use consistent color coding for related metrics, provide clear titles and labels, and include brief interpretations that explain what the numbers mean. The best dashboards answer the question "so what?" by making the implications of metric changes immediately apparent to viewers.