Assessing the Impact of Your Design System

Everyone wants a high-performing design system. But few teams have a clear way to measure performance at the component level. Knowing which components truly matter, and why, is harder than it looks. It's not just about adoption. It's about impact. To make smarter decisions about what to invest in, evolve, or deprecate, we need a clearer way to measure the value of what we’ve built.

1. Internal Importance: How Often Developers Use It

The first signal is straightforward: how often is a component imported or referenced in code? This helps identify:

• Which components are relied on across projects
• Where your design system is saving the most dev time
• Opportunities to optimize high-usage components

Example: A base Button or FormInput might be imported in 90% of product repos. That’s a signal of utility and risk. If it’s inefficient or buggy, it affects everything.

Tip: Use static analysis, repo telemetry, or even linting tools to track component usage at scale.

2. External Importance: How Often Users Interact With It

Not all high-impact components are heavily imported. Some are just visible and essential to end users.

To capture this, measure how often a component appears in the product and how users interact with the component. Look for:

• Views per session
• Click-through or interaction rates
• Role in key flows (onboarding, checkout, search)

Example: A Modal component might be used only a few places, but if one of them is your pricing page or login experience, it’s doing heavy lifting.

Tip: Tools like Amplitude, Heap, or custom instrumentation can help attribute user interactions to specific components.

3. Business Impact: Is It Tied to Revenue (Directly or Indirectly)?

Design systems don’t exist in a vacuum, they enable product delivery. The next level of measurement is mapping components to business outcomes.

Start by asking:

• Is this component used in a revenue-generating flow?
• Does it support features that are part of a paid tier?
• Does it reduce dev/design time for monetized functionality?

Direct impact: A PricingTable or SignupForm that affects conversion. Indirect impact: A GridSystem or TokenProvider that accelerates teams building revenue-driving features.

Tip: Partner with product and analytics teams to trace component usage in high-value customer journeys.

4. Account-Level Value: Who Is Using It?

Volume alone doesn’t equal value. If a low-volume component is used by high-value enterprise accounts, then its strategic importance skyrockets.

Look at component usage by:

• Account tier (enterprise vs. self-serve)
• Customer segment
• Churn risk / renewal status

Example: An SSOConfigForm might be used in only 2% of accounts — but those accounts represent 60% of ARR.

Tip: Connect component usage analytics to the account metadata in your CRM or data warehouse.

5. Foundational Role: Is It a Building Block for Other Components?

Some components are like infrastructure — they aren’t visible to users but are critical to everything else functioning.

Examples:

• Layout systems (Grid, Container)
• Theming tools (ColorTokens, ThemeProvider)
• Primitive UI components (BaseButton, Text, Icon)

These components often:

• Enable flexibility and consistency
• Multiply efficiency across teams
• Require deep attention to accessibility and performance

Tip: Map component dependencies (via documentation or static analysis) to see which pieces your system is built upon.

Bringing It All Together

A truly high-impact component tends to score well across multiple dimensions:

✅ Frequently used by devs

✅ Highly visible or interacted with by users

✅ Found in monetized flows or enterprise accounts

✅ Underpins other parts of the system

And just as important: components with low usage but high strategic value shouldn’t be overlooked. Measuring both reach and relevance helps you make smarter investments in design systems over time.

Final Thoughts

Design systems are long-term bets. If you want yours to thrive, measuring what matters, not just what’s easy, is essential.

By looking at usage, visibility, business impact, account value, and technical dependencies, you can begin to separate noise from signal, and evolve your system based on real insights.