The Minimum Viable Analytics Setup for Solo Founders

Most founders are over-tracking and under-thinking

It is easy to spend a whole weekend wiring up GA4, GTM, Segment, Hotjar, Mixpanel and a Looker Studio dashboard for a product with eight users, and then never look at any of it, because there is too little data to read and too many bugs to fix.

At $0 to $500 of monthly revenue, you need to know three things. Where signups come from, what people do after they sign up, and who's about to churn. Everything else is a distraction dressed up as data. This guide gets you to a setup that takes an afternoon to install and answers those three questions.

Plausible vs PostHog vs GA4: pick one

Most small teams end up choosing between these tools. The tradeoffs:

• Plausible: clean interface, privacy-friendly, cookieless, paid plans only. Good for content sites and marketing pages, limited for product analytics.
• PostHog: a free tier large enough for a young product, with product analytics, session replay, feature flags and A/B tests. Steeper learning curve. A good default for SaaS.
• GA4: free, very complete, hard to use. Useful because Google Ads and Search Console connect to it. Best run alongside another tool rather than as the main one.
• Mixpanel or Amplitude: powerful, but their paid plans are aimed at larger teams. Wait until you have real revenue.
• Fathom or Simple Analytics: alternatives to Plausible, with the same tradeoffs.

A sensible default stack

For an early SaaS, a sensible stack is PostHog as the main product analytics tool, plus GA4 in the background because Google Ads relies on it. That's it. No Segment. No CDP. No data warehouse. You can add those later when you actually have data worth piping anywhere.

If you're running a pure content site or a marketplace where there's no real 'product analytics' to speak of, swap PostHog for Plausible. You skip event configuration you would not use.

The events that actually matter

Resist the urge to track everything. You will not look at most of it. Here is a minimum set of events to instrument on day one. Each one answers a question you will actually ask.

• signup_completed, with source and UTM parameters so you know which channels work
• activation_event, for whatever 'first real value' means in your product (first deck created, first email sent)
• upgrade_clicked and upgrade_completed, split so you can see the drop-off
• key_feature_used, one event per core feature rather than an event for every click
• support_message_sent, a proxy for friction: a spike is worth a look
• subscription_cancelled, with a reason field if you ask for one

What to actually measure

For a SaaS, the metrics that actually predict whether the business will work are unglamorous: weekly active users as a percentage of signups (activation), week-2 retention, MRR by source channel, and churn reason. If those four numbers are moving in the right direction, you're fine. If they're not, no dashboard in the world will save you.

Set a weekly recurring 30-minute calendar block to look at these. Write down one observation and one experiment to run next week. That's it. Living in the dashboard is time not spent shipping.

Where founders waste time

Three common time-sinks. First, building a Looker Studio dashboard with 14 widgets that you check obsessively for the first week and never open again. Second, debating attribution models when you have 40 signups a month: last-click is fine until you spend real money on ads. Third, A/B testing small copy changes on pages with a few hundred visits a month. You need traffic for statistical significance; without it you're just generating noise.

Spend your analytics hours on two things: getting cleaner inputs (UTM your campaigns, name your events consistently) and talking to actual users. A 20-minute call with someone who cancelled often teaches more than a month of staring at funnels.

When to graduate from this setup

You outgrow the minimum setup when you have enough volume for statistical tests to mean something and enough channels for attribution to matter. At that point, adding a warehouse (BigQuery or Postgres) and piping PostHog + Stripe + your app DB into it makes sense, because you can finally ask questions like 'do users from channel X have better LTV than channel Y?' and get a real answer.

Until then, the simple stack wins on every dimension: cheaper, faster to install, easier to maintain, and aligned with the actual decisions you can make at your scale. Boring analytics setup, interesting product. That's the trade you want.