Meta Description: Learn which analytics your MVP actually needs. Skip vanity metrics and focus on the data that drives product decisions. Practical setup guide included.
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Most MVPs have too much analytics or too little. Either founders install every tool they've heard of and drown in dashboards, or they ship with nothing and guess about user behavior.
The right approach sits in the middle: track what you need to make decisions, ignore everything else. For an MVP, that's a surprisingly small set of metrics focused on one question: Is this product working?
This guide covers what to track, what to skip, and how to set up analytics without overengineering.
Why Analytics Matters for MVPs
Analytics exists to reduce guessing. Without data, you're making product decisions based on intuition and anecdote. Sometimes intuition is right. Often it's not.
MVP analytics answers specific questions:
Are users doing what we built this for? If your booking app exists to book sessions, are people booking sessions?
Where do users get stuck? If users start the signup flow but never finish, you have a problem. Data shows you where.
Which features matter? You shipped five features. Which ones get used? That informs what to build next.
Stop planning and start building. We turn your idea into a production-ready product in 6-8 weeks.
Is the product growing? Are new users showing up? Are existing users coming back? Growth problems are different from product problems.
The key word is "decisions." Every metric you track should inform a decision you're prepared to make. Metrics that don't inform decisions are noise.
The MVP Analytics Stack
You don't need many tools. For most MVPs, three layers cover everything:
Layer 1: Product Analytics
Track what users do inside your product. Page views, clicks, feature usage, conversion flows.
Recommended: PostHog, Amplitude, or Mixpanel.
PostHog is our default for MVPs. It's open source, offers a generous free tier, and handles product analytics, session recording, and feature flags in one tool. You can self-host for full control or use their cloud version.
Amplitude and Mixpanel are more established but more expensive at scale. For MVPs, their free tiers usually suffice.
Not recommended for MVPs: Google Analytics 4. GA4 is built for marketing analytics—where traffic comes from—not product analytics. It's confusing for tracking in-app behavior and overkill for MVP needs.
Layer 2: Error Tracking
Know when things break before users tell you.
Recommended: Sentry.
Sentry captures JavaScript errors, API failures, and performance issues. When your checkout flow crashes at 2 AM, Sentry sends an alert with the stack trace. Without error tracking, you find out when angry users email.
Layer 3: Session Recording (Optional)
Watch real users interact with your product.
Recommended: PostHog (included) or LogRocket.
Session recordings show what analytics summarize. A conversion funnel shows 40% drop-off at step 2. The recording shows why—a confusing form field, a button that looks disabled, a mobile rendering bug.
Recordings are optional for MVP launch but valuable for post-launch iteration.
What to Track: The Essential Metrics
Start with a small set of metrics that directly relate to your product's purpose. You can add more later. Removing tracking is harder than adding it.
Signup and Activation
Signup started: User began the registration process.
Signup completed: User finished registration and can now use the product.
Activation: User completed the action that makes them a "real" user. Define this carefully—it's the moment where someone goes from trying to using.
Activation varies by product:
Booking app: Made their first booking
Social network: Added their first connection
Productivity tool: Created their first project
Marketplace: Listed their first item or made their first purchase
Activation is your most important metric. Users who activate retain; users who don't, churn. The gap between signup and activation shows where your onboarding fails.
Core Action Usage
What's the primary thing your product exists to do? Track it obsessively.
For a note-taking app: notes created, notes edited.
For a messaging product: messages sent, conversations started.
For an analytics tool: reports generated, queries run.
This is your "north star" metric—the single number that best represents value delivery. If this number grows, you're winning. If it stagnates, something's wrong.
Feature Engagement
For each major feature, track:
How many users use it at all
How often active users use it
Where users drop off within the feature
This reveals which features matter and which were wrong bets. After a month of data, you might discover a "critical" feature is used by 3% of users while a "nice to have" is used by 80%.
Retention
Are users coming back?
Track cohort retention: Of users who signed up in week 1, what percentage returned in week 2? Week 4? Week 8?
Early-stage retention numbers are often discouraging. That's normal. The goal is to see retention improve as you iterate. If it's getting worse, your changes aren't helping.
Basic Engagement
Keep it simple:
Daily/weekly/monthly active users: Users who performed any meaningful action in that period
Session frequency: How often do users return?
Session duration: How long do users spend?
These baseline metrics show whether engagement is improving over time.
What Not to Track
Every metric you add is maintenance burden. Skip these for MVP:
Vanity metrics: Total signups, total page views, social followers. These feel good but don't inform decisions. 10,000 signups means nothing if 9,900 never returned.
Micro-interactions: Tracking every button click creates noise. Track meaningful actions, not mouse movements.
Attribution (initially): Where users came from matters for marketing optimization, but you need marketing spend first. For MVP, knowing someone signed up matters more than knowing which ad they clicked.
A/B testing infrastructure: You need traffic for A/B tests to work. Focus on getting users before optimizing experiences.
Revenue analytics (if pre-revenue): You'll add this when you charge. Until then, don't build for it.
Setting Up Tracking
Implementation matters. Poorly structured tracking creates confusing data that's hard to query.
Funnel analysis shows where users abandon flows. If 70% start checkout and 20% complete it, something's wrong in those steps.
Combine with session recordings. The data shows where; recordings show why.
Validating Feature Investments
You built a feature based on user requests. Is it used?
Compare engagement before and after launch. Did active users increase? Did retention improve? If a requested feature doesn't move metrics, user requests weren't representative.
Prioritizing Next Features
Look at usage patterns:
Heavy use of feature A, minimal use of feature B: invest in A
Users abandoning at specific points: fix those points
Activated users vs. churned users: what do activated users do differently?
Data guides roadmap decisions. "Users want X" becomes "Data shows users need X."
Key Takeaways
Analytics for MVPs should be simple and decision-focused:
Use few tools: One product analytics tool, one error tracker. PostHog plus Sentry covers most needs.
Name events consistently: Establish conventions before first implementation.
Build one dashboard: Single source of truth for "is the product working?"
Act on data: Analytics without action is overhead.
The goal isn't comprehensive measurement. It's having enough visibility to make informed product decisions. Start minimal, add when specific questions need answers.
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