How to Set Up Analytics for a Cursor-Built App
A developer's guide to setting up analytics for your Cursor-built SaaS. Covers tool selection, event tracking, conversion funnels, and common mistakes — with code examples.
You built your app with Cursor. It works. People are signing up. But you have no idea where they came from, what they do inside your product, or why they leave.
This is the analytics gap, and almost every Cursor-built product has it. Developers are meticulous about error logging and system monitoring — they would never ship an app without proper logging. But they ship products without analytics every day, flying blind on the questions that actually determine whether the business survives.
Where are my visitors coming from? Which pages convert best? Where do users drop off in onboarding? What feature engagement predicts retention?
These questions are not nice-to-have. They are the difference between guessing and knowing. And when you are a solo founder with limited time, you cannot afford to invest in marketing channels that do not work or product features that nobody uses.
This guide gives you a practical, code-included approach to setting up analytics for your Cursor-built app. We will cover the minimum viable analytics setup, the metrics that actually matter at your stage, and the common mistakes that waste your time.
The Two Types of Analytics You Need
Most analytics guides conflate two different things:
Marketing Analytics (Where do users come from?)
This tells you which channels drive traffic and sign-ups. It answers:
- How many people visit my site?
- Where did they come from? (Google, Twitter, Reddit, direct)
- Which pages do they visit?
- How many convert (sign up, start trial, purchase)?
Tool: Google Analytics 4 or Plausible
Product Analytics (What do users do inside the app?)
This tells you how people use your product. It answers:
- Which features do users engage with?
- Where do they get stuck in onboarding?
- What usage patterns predict retention?
- Which users are at risk of churning?
Tool: PostHog (free, self-hosted option) or Mixpanel (free tier)
At your stage, marketing analytics is more important. You need to understand acquisition before you optimize engagement. Start there.
Setting Up Marketing Analytics
Option A: Plausible (Recommended for Simplicity)
Step 1: Create an account
Sign up at plausible.io. Add your domain.
Step 2: Add the tracking script
For a Next.js app (the most common Cursor-built framework), add to your app/layout.tsx:
// app/layout.tsx
import Script from 'next/script'
export default function RootLayout({ children }) {
return (
<html>
<head>
<Script
defer
data-domain="yourdomain.com"
src="https://plausible.io/js/script.js"
/>
</head>
<body>{children}</body>
</html>
)
}
Step 3: Set up a conversion goal
In Plausible dashboard, go to Settings > Goals. Add a pageview goal for your thank-you or dashboard page (the page users see after signing up).
Alternatively, add a custom event goal and trigger it from your sign-up flow:
// After successful sign-up
window.plausible('Signup', {
props: { plan: 'free', source: 'landing-page' }
})
Step 4: Add UTM parameters to your links
When you share links on social media, email, or communities, add UTM parameters so you can track the source:
https://yourdomain.com?utm_source=reddit&utm_medium=community&utm_campaign=launch
Plausible automatically reads these and shows them in your traffic sources report.
That is it. You now have marketing analytics. Total setup time: 15 minutes.
Option B: Google Analytics 4 (More Powerful, More Complex)
Step 1: Create a GA4 property
Go to analytics.google.com. Create a new property. Get your Measurement ID (starts with G-).
Step 2: Add the tracking code
For Next.js:
// app/layout.tsx
import Script from 'next/script'
const GA_MEASUREMENT_ID = 'G-XXXXXXXXXX'
export default function RootLayout({ children }) {
return (
<html>
<head>
<Script
src={`https://www.googletagmanager.com/gtag/js?id=${GA_MEASUREMENT_ID}`}
strategy="afterInteractive"
/>
<Script id="ga4" strategy="afterInteractive">
{`
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', '${GA_MEASUREMENT_ID}');
`}
</Script>
</head>
<body>{children}</body>
</html>
)
}
Step 3: Set up conversion tracking
In GA4, go to Admin > Events. Create a new event for sign-ups:
// After successful sign-up
gtag('event', 'sign_up', {
method: 'email',
plan: 'free'
})
Then mark this event as a conversion in GA4 Admin > Events > toggle "Mark as conversion."
Step 4: Connect Google Search Console
In GA4, go to Admin > Product Links > Search Console. This gives you keyword data directly in your analytics dashboard.
Which Should You Choose?
| Factor | Plausible | GA4 | |---|---|---| | Setup time | 15 min | 30 min | | Learning curve | None | Steep | | Privacy compliance | Built-in | Needs cookie consent | | Cost | $9/mo | Free | | Search keyword data | No (use GSC separately) | Yes (with GSC link) | | Custom reports | Limited | Extensive |
My recommendation: Start with Plausible for simplicity. If you need deeper analysis later, add GA4 alongside it.
Setting Up Product Analytics
Once you have 50+ active users, add product analytics to understand usage patterns.
PostHog (Recommended)
PostHog is open-source and has a generous free tier (1 million events/month). You can self-host it or use their cloud version.
Step 1: Install
npm install posthog-js
Step 2: Initialize
// lib/posthog.ts
import posthog from 'posthog-js'
if (typeof window !== 'undefined') {
posthog.init('your-project-api-key', {
api_host: 'https://app.posthog.com',
capture_pageview: true,
capture_pageleave: true,
})
}
export default posthog
Step 3: Track key events
Identify the 5-7 actions that represent value in your product. Track only these. Do not track everything.
// User creates their first project
posthog.capture('project_created', {
project_type: 'personal',
is_first_project: true,
})
// User invites a team member
posthog.capture('team_member_invited', {
team_size: 3,
})
// User completes onboarding
posthog.capture('onboarding_completed', {
time_to_complete_minutes: 12,
})
Step 4: Identify users
// After login
posthog.identify(user.id, {
email: user.email,
plan: user.plan,
created_at: user.createdAt,
})
This lets you segment your analytics by user properties.
The Metrics That Matter at Each Stage
Do not measure everything. Measure the right things for your stage.
Stage 1: Pre-Product-Market Fit (0-50 users)
Focus on:
- Sign-up conversion rate: Visitors who sign up / total visitors. Benchmark: 2-5%.
- Activation rate: Users who complete the core action / total sign-ups. Benchmark: 20-40%.
- Traffic sources: Where your sign-ups come from.
Ignore:
- Retention (not enough data)
- Revenue metrics (too early)
- Vanity metrics (page views, session duration)
Stage 2: Finding Product-Market Fit (50-200 users)
Focus on:
- Weekly retention: % of users active this week who were active last week. Benchmark: 40-60%.
- Core feature usage: How many users engage with your primary value feature?
- NPS or satisfaction signals: Are users recommending you?
Add:
- Onboarding funnel analysis (where do users drop off?)
- Feature usage matrix (which features correlate with retention?)
Stage 3: Scaling (200+ users)
Focus on:
- Monthly Recurring Revenue (MRR): The number that matters most.
- Customer Acquisition Cost (CAC): How much does it cost to get one customer?
- Lifetime Value (LTV): How much revenue does an average customer generate?
- Churn rate: % of customers who cancel per month. Benchmark: <5% for SMB SaaS.
For more context on how analytics fits into your marketing strategy, see Best Marketing Stack for Solo Developers.
Building Your Analytics Dashboard
You do not need a fancy BI tool. Create a simple weekly dashboard that you check every Monday morning.
The Monday Morning Dashboard
| Metric | This Week | Last Week | Trend | |---|---|---|---| | Unique visitors | | | | | Sign-ups | | | | | Conversion rate | | | | | Active users | | | | | MRR | | | | | Top traffic source | | | |
Update this manually in a spreadsheet. Yes, manually. At your stage, the act of looking at these numbers weekly matters more than the precision of the tracking.
When to Automate
Automate your dashboard when:
- You are consistently checking it weekly (the habit is established)
- You have 200+ users (enough data for the dashboard to be meaningful)
- Manual tracking takes more than 15 minutes per week
At that point, PostHog dashboards, Plausible API, or a simple script that pulls from your database will save time.
Common Analytics Mistakes
Mistake 1: Tracking Everything
More events is not better. Every event you track is one you need to maintain, and one that clutters your analysis. Track the 5-7 events that represent your core value loop, and ignore everything else.
Mistake 2: Optimizing Before You Have Data
You need at least 100 conversions before any A/B test is statistically meaningful. If you have 10 sign-ups per week, running an A/B test will take 10 weeks to yield usable results. Focus on increasing volume first.
Mistake 3: Ignoring Qualitative Data
Numbers tell you what is happening. Conversations tell you why. At your stage, one 15-minute call with a churned user is worth more than a week of staring at dashboards.
Mistake 4: Vanity Metrics
Page views, session duration, and bounce rate feel important but rarely correlate with business success. A page with a 90% bounce rate might be your best converter if the 10% who stay all sign up.
Mistake 5: No UTM Discipline
If you do not tag your links with UTM parameters, you will not know which channels drive sign-ups. Be religious about this. Every link you share should have utm_source, utm_medium, and utm_campaign.
Privacy and Compliance
A quick note on privacy, because this matters more than most developers realize.
GDPR (Europe)
If you have European visitors (you probably do), you need:
- A cookie consent banner (if using GA4 or any cookie-based analytics)
- A privacy policy explaining what you track and why
- The ability for users to request data deletion
Or: Use Plausible, which is GDPR-compliant without a cookie banner because it does not use cookies or collect personal data.
CCPA (California)
Similar to GDPR but for California residents. Requires a "Do Not Sell My Personal Information" link if you share analytics data with third parties.
The Simple Approach
Use Plausible for marketing analytics (no cookies, GDPR-compliant out of the box). Use PostHog with their privacy controls for product analytics. Add a privacy policy to your site. This covers 95% of compliance for early-stage products.
Cross-Cluster Resources
For more context on measuring marketing effectiveness:
What to Do Next
- Set up Plausible or GA4 today. It takes 15-30 minutes.
- Add one conversion event for your primary CTA.
- Set up Google Search Console.
- Create your Monday Morning Dashboard spreadsheet.
- After you hit 50 users, add PostHog for product analytics.
- Return to the Cursor Startup Marketing Guide for the complete GTM framework.
You cannot improve what you do not measure. But you also should not measure everything just because you can. Start simple, stay consistent, and let the data guide your decisions.
Marketing your Cursor-built product gets much easier once you know what is actually working. For help scaling the marketing efforts that your analytics reveal are effective, tools like Any can automate content creation and SEO optimization across the channels that drive results.
Ready to put your GTM on autopilot?
50+ AI specialists working around the clock. One subscription, zero hiring.