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The Frontend Metrics Your Startup Should Actually Track

The Frontend Metrics Your Startup Should Actually Track

Most startup metrics dashboards measure what's easy to measure. Here's the set that's actually worth tracking — and how to implement the instrumentation.

The Frontend Metrics Your Startup Should Actually Track

There are two types of metrics problems in startups: not tracking enough, and tracking too much of the wrong thing.

The first is common in very early-stage products. The second is common in products that have added analytics reactively — one event for each feature as it shipped, with no coherent framework.

Here's a framework and the implementation approach that scales from MVP to Series A.

The metric categories that matter

Acquisition: How do people find you?

  • Organic search (SEO), paid ads, direct, referral, social
  • Source/medium breakdown for sign-ups specifically (not just visits)

Activation: Do new users reach a meaningful experience?

  • % of sign-ups who complete your defined activation milestone
  • Time from sign-up to activation

Retention: Do users come back?

  • Day 1, Day 7, Day 30 retention (% of users who return after each period)
  • Feature-level retention (users who use feature X in their first week are 3x more likely to be retained at 30 days)

Revenue: Are people paying?

  • Monthly Recurring Revenue (MRR)
  • Conversion rate from free to paid
  • Average Revenue Per User (ARPU)
  • Churn rate

Referral: Are users bringing others?

  • Viral coefficient (sign-ups generated per existing user)
  • NPS (Net Promoter Score, measured in-product or via email)

What to instrument in the frontend

The framework above tells you what questions to answer. Here's what events you need to track to answer them:

User lifecycle events:

  • user_signed_up (with source/medium properties)
  • user_activated (when the user completes your activation milestone — you define this)
  • user_upgraded (free → paid)
  • user_churned (subscription cancelled)

Core feature events: For each core feature: feature_name_started, feature_name_completed, feature_name_abandoned

Funnel events:

  • onboarding_step_completed (with step property)
  • checkout_started, checkout_completed, checkout_abandoned

Engagement signals:

  • session_started (with session duration on close)
  • feature_discovery_first_use (first time a user uses a feature)

Implementation in Nuxt

Use Posthog or Mixpanel as your analytics backend. Both have good Vue integrations. Here's a reusable composable:

// composables/useAnalytics.ts
export function useAnalytics() {
  const { $posthog } = useNuxtApp()

  function track(event: string, properties?: Record<string, any>) {
    $posthog?.capture(event, {
      ...properties,
      timestamp: new Date().toISOString()
    })
  }

  function identify(userId: string, traits?: Record<string, any>) {
    $posthog?.identify(userId, traits)
  }

  return { track, identify }
}

Usage in components:

<script setup>
const { track } = useAnalytics()

async function handleProjectCreate() {
  await createProject(formData)
  track('project_created', {
    projectType: formData.type,
    teamSize: formData.teamSize
  })
}
</script>

Activation: defining your milestone

The activation milestone is the most important definition you'll make in your analytics setup. It's the moment after which users are significantly more likely to become retained customers.

To find it: look at your most retained users (Day 30 still active). What did they all do in their first session that users who churned didn't do? That's your activation milestone.

Common patterns:

  • Collaboration tool: created a project AND invited a teammate
  • Analytics tool: installed tracking code AND saw first data
  • Finance tool: connected a bank account AND ran a report

The milestone usually involves completing the core workflow, not just signing up or browsing.

Retention: the metric that predicts everything

Retention is the most predictive metric for long-term success. A product with good retention can be grown through acquisition. A product with poor retention can't be fixed by acquisition — you're filling a leaky bucket.

Measure cohort retention: of all users who signed up in week X, what % are still active in week X+4?

If this number is less than 20% at 30 days, you have a product problem that analytics won't solve. If it's above 40% at 30 days, you have something to grow.

Dashboard hygiene

The metrics dashboard should have:

  • One primary metric for each of: acquisition, activation, retention, revenue
  • Trends over time (not just point-in-time numbers)
  • Cohort-based retention chart
  • Funnel visualisation for your core conversion flow

What it shouldn't have:

  • Vanity metrics (pageviews, total sign-ups without activation context)
  • Metrics with no clear owner or action trigger
  • Raw event counts without normalisation

Review this dashboard weekly with the founding team. Align on which metric matters most for the current stage of the company, and orient product decisions around moving it.

The order of instrumentation

  1. First: user lifecycle events (signed up, activated, upgraded, churned)
  2. Second: core feature events for your 3 most important features
  3. Third: funnel events for your primary conversion flows
  4. Later: engagement signals, feature discovery, NPS surveys

Don't try to instrument everything at once. Get lifecycle events right, then add as you need to answer specific questions.

Building a product with proper instrumentation from day one? Let's talk →

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