The Minimum Viable Analytics Setup for Solo Founders

Most founders are over-tracking and under-thinking

I've watched a lot of solo founders spend a full weekend wiring up GA4, GTM, Segment, Hotjar, Mixpanel, and a Looker Studio dashboard — for a product that has eight users. Then they never look at any of it because the signal-to-noise is awful and they're too busy fixing bugs.

The honest truth: at 0–500 MRR, 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. The goal of this guide is to get you to a setup that takes 90 minutes to install and answers those three questions.

Plausible vs PostHog vs GA4: pick one

These are the three tools 90% of indie founders end up choosing between. The honest tradeoffs:

• Plausible — cleanest UI, privacy-friendly, no cookie banner, ~$9/mo. Great for content sites and marketing pages. Limited for product analytics.
• PostHog — free up to ~1M events/mo, does product analytics + session replay + feature flags + A/B testing. Steeper learning curve. My default for SaaS.
• GA4 — free, the most data, the worst UX. Useful because Google Ads, Search Console, and other tools integrate with it. I run it alongside something else, not as primary.
• Mixpanel/Amplitude — powerful but priced for funded companies. Skip until you've got real revenue.
• Fathom/Simple Analytics — Plausible alternatives, same tradeoffs.

My default stack

For a SaaS founder under $10K MRR, the stack I'd install today: PostHog as the primary product analytics tool, plus GA4 quietly in the background because Search Console and Google Ads need 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. The five extra minutes you save not configuring events compounds across a year.

The events that actually matter

Resist the urge to track everything. You will not look at most of it. Here's the minimum set of events to instrument on day one — every one of these should answer a question you'd actually ask.

• signup_completed — with source/UTM so you know which channels work
• activation_event — whatever 'first real value' looks like (first deck created, first email sent, first audit run)
• upgrade_clicked and upgrade_completed — split so you can see drop-off
• key_feature_used — one event per core feature, not 40 events per click
• support_message_sent — proxy for friction; spikes here usually predict churn
• subscription_cancelled — with a reason field if you ask

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. The founders who 'live in the dashboard' usually aren't shipping anything else.

Where founders waste time

Three time-sinks I see constantly. 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're spending real money on ads. Third, A/B testing tiny copy changes on pages that get 200 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 single 20-minute call with someone who canceled will teach you more than a month of staring at funnels.

When to graduate from this setup

You outgrow the minimum setup somewhere around $20–30K MRR, when you have enough volume that statistical tests work and enough channels that attribution actually matters. 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.