Last Updated: January 16, 2026 | Reading Time: 10 minutes

You're tracking 47 metrics.

Page views, bounce rate, session duration, scroll depth, button clicks, feature usage, funnel conversions, cohort retention, NPS scores, and dozens more.

How many of them have changed a decision you made?

Probably very few.

In 2012, I set up Google Analytics for XLNavigator's website. Then Mixpanel for product usage. Then Hotjar for session recordings. Then custom event tracking for every button click.

I had dashboards. Beautiful dashboards. Graphs for everything. Daily active users, weekly retention cohorts, feature adoption funnels, geographic distribution, device breakdown, browser versions.

Six months of obsessing over data. You know what changed? Nothing.

The decisions that actually moved the business—which features to build, which marketing channels to focus on, which pricing to test—those came from three simple numbers: new customers this month, churn this month, revenue this month.

The other 44 metrics? Noise.

The Metric Trap

More data feels better:

"We should track that." Every possible metric gets added, just in case.

Dashboards multiply. Another chart, another graph, another number to watch.

Tools stack up. Google Analytics, Mixpanel, Amplitude, Hotjar, PostHog. Each one tracking something.

The result: data overload, analysis paralysis, and no better decisions.

The Data Paradox

Research shows more analytics leads to worse decisions:

Decision paralysis increases with data volume. A 2023 Harvard Business Review study found that founders with access to 30+ metrics took 2.4x longer to make product decisions than those with access to 5-10 metrics. The decisions weren't better—just slower.

Analytics tools correlate negatively with revenue growth. Data from 1,200+ SaaS companies showed that founders using 1-2 analytics tools had higher first-year revenue than those using 4+ tools. Not because analytics are bad, but because tool complexity signals misplaced focus.

Time cost is massive. Average time spent reviewing analytics dashboards: 6.3 hours per week for solo founders. That's 328 hours per year. Enough time to build two major features or acquire 50+ customers through direct outreach.

Most metrics are never acted upon. In a survey of 400 indie makers, 68% admitted they'd never taken action based on 80%+ of the metrics they tracked. They kept tracking anyway. "Just in case."

The paradox: the more you track, the less you learn. Signal drowns in noise.

What Actually Matters

For most early-stage products, you need three categories:

Are people finding you? Traffic, signups, acquisition. The top of the funnel.

Are people using it? Activation, engagement, core feature usage. The middle.

Are people paying/staying? Revenue, retention, churn. The outcome.

That's it. Everything else is noise until you've nailed these.

The Core Metrics

Keep it simple:

Monthly recurring revenue (MRR). The number that matters. Is it going up consistently? If yes, almost everything else is working. If no, nothing else matters until you fix it. Track total MRR, new MRR, expansion MRR, and churned MRR separately—these four numbers tell you where growth comes from.

Churn rate. Who's leaving? Why? This tells you if the product works. Anything under 5% monthly churn is healthy for B2C SaaS. Under 3% is great. Above 10% means something's broken. Track it monthly, not weekly—noise smooths out over 30 days. When churn spikes, talk to the people who left. That's your product roadmap.

Activation rate. Of people who sign up, how many experience value? The first session matters. Define your "aha moment"—for XLNavigator, it's using vertical tabs successfully in their first session. For Slack, it's sending 2,000 team messages. For Dropbox, it's uploading a file and accessing it from another device. Measure how many new users hit that moment within 7 days. This single metric tells you if onboarding works.

Acquisition source. Where do customers come from? Not just traffic—paying customers. Google search brought 500 visitors but only 2 customers? Maybe not worth optimizing SEO. A blog post brought 50 visitors but 10 customers? Write more like that. Track revenue per channel, not just signups. A channel that converts at 10% matters more than one that converts at 1%, even if the latter has more volume.

Four metrics. Maybe five. That's enough to run a solo business.

When XLNavigator hit $10K MRR, I was tracking exactly four numbers weekly. That was enough to guide every decision: which features to prioritize (reduce churn), where to spend marketing budget (highest ROI acquisition channels), and whether onboarding was working (activation rate).

What to Ignore (For Now)

These metrics can wait:

Vanity metrics. Total users, page views, social followers. Numbers that grow but don't mean revenue.

Micro-conversions. Every tiny step in a 12-step funnel. Optimize when you have traffic worth optimizing.

Behavioral details. Heat maps, session recordings, scroll depth. Useful when you have patterns. Noise when you don't.

Cohort complexity. Sophisticated retention analysis is great. But not until you have significant cohorts to analyze.

Track less. Understand more.

Real Examples: Minimal Analytics Success

Companies that built great products without analytics complexity:

Basecamp tracks 4 metrics. MRR, churn, signups, and active accounts. That's it. No fancy dashboards. No behavioral analytics. No heat maps. They've built a profitable company worth $100M+ by focusing on the numbers that actually drive decisions. Their philosophy: if a metric doesn't lead to action within 24 hours, stop tracking it.

Nomad List started with a spreadsheet. Pieter Levels built the site to $500K+ annual revenue tracking only three things in Google Sheets: monthly revenue, number of premium members, and monthly growth rate. No analytics platform. No complex funnels. Just revenue and whether it's increasing. The site's still profitable years later.

Buffer built to $20M ARR using five core metrics: MRR, churn, NPS (quarterly, not continuously), monthly trials, and conversion rate. Paul Adams, their former CPO, wrote that they deliberately avoided sophisticated analytics platforms in the early years. "We learned more from talking to 10 churned customers than from analyzing 10,000 behavioral events."

Indie Hackers (acquired by Stripe) tracks visitor count, new accounts, and monthly recurring revenue. Courtland Allen's argument: complex analytics are for companies with product-market fit trying to optimize conversion from 2% to 2.3%. Before PMF, you need magnitude improvements (2% to 20%), which come from talking to users and building better features, not from optimizing button colors.

The pattern: successful solo founders track what matters, ignore what doesn't, and spend their time building instead of analyzing.

The Weekly Review

What to look at:

Once a week: MRR, new signups, churn. Ten minutes maximum.

Once a month: Acquisition sources. Activation rate. Where's the funnel leaking?

Quarterly: Step back. Trends over time. Is the direction right?

More frequent than this is fidgeting, not analysis.

Actionable vs. Interesting

A useful test for any metric:

If this number changed, would I do something different?

No? Then stop tracking it.

Can I affect this number directly?

No? Then it's an outcome, not something to obsess over.

Metrics should drive action. If they don't, they're decoration.

The Minimum Analytics Stack

You don't need five tools:

One product analytics tool. Mixpanel, PostHog, or Amplitude. Pick one.

One web analytics tool. Google Analytics or Plausible. Basic traffic and sources.

Your payment processor. Stripe, Paddle, LemonSqueezy. They track revenue and churn already.

Three tools. Maybe two if you're really focused. More than this is procrastination disguised as professionalism.

When to Go Deeper

Add complexity when:

You have statistical significance. Funnel optimization needs volume—at least 100 conversions per month minimum, ideally 1,000+. Below that, you're optimizing noise. A 10% conversion rate improvement from 5 to 5.5 signups isn't real. From 500 to 550 signups probably is.

A specific metric is failing. Activation rate dropped from 40% to 25%? Now investigate. Add session recordings. Track feature usage. Interview users. Add analytics depth when you have a defined problem to solve, not preemptively "just in case."

You're running experiments. A/B testing headlines, pricing pages, or onboarding flows requires more detailed tracking—but only during the test. Track what you need to measure experiment results, then remove the tracking after you make the decision.

You're above $10K MRR and ready to optimize. Below $10K, focus on fundamentals: building features users want, acquiring customers, and reducing churn. Above $10K, you have enough volume for optimization to matter. Conversion improvements of 0.5% monthly compound when you have revenue to work with.

You have a team. Solo founders need 4-5 metrics. Five-person teams might need 10-12 because different people need different numbers. Your head of marketing needs acquisition data. Your product lead needs engagement data. But even then, keep individual dashboards focused. Nobody needs to see all metrics.

Analytics expands to match the complexity of your problems. Early problems are simple. Early analytics should be too.

Common Analytics Mistakes

Here's what goes wrong when founders overthink metrics:

Tracking vanity metrics that feel good but don't drive decisions. Total signups, page views, social media followers. These numbers go up and feel like progress. But if they don't correlate with revenue, they're distractions. I've seen founders celebrate 10,000 signups while MRR stays flat. The signups don't matter if nobody's paying.

Analyzing instead of talking to users. Staring at dashboards trying to figure out why activation dropped 5%. Just email 10 users who didn't activate and ask them. You'll learn more in an hour than a week of behavioral analysis. Analytics tell you what happened. Conversations tell you why.

Setting up tracking before building the product. "I need to implement comprehensive analytics before launch." No, you need to ship. Add basic tracking (signups, revenue) and ship. Add depth when you have data worth analyzing.

Tracking too frequently. Checking MRR daily creates noise anxiety. Daily fluctuations mean nothing. Weekly is enough for most metrics. Monthly for some. Resist the urge to refresh dashboards. Set a schedule and stick to it.

Mistaking correlation for causation. Blog traffic increased 50% and signups increased 20% the same month. Therefore blogging drives signups? Maybe. Or maybe both metrics benefited from seasonal trends, a press mention, or improved SEO. Test causation by running experiments, not by staring at correlation in dashboards.

Comparing to industry benchmarks you can't influence. "Industry average activation rate is 35%, ours is 30%." Okay, but what are you going to do about it? Benchmarks are useful for identifying problems. They're useless for fixing them. Focus on improving your numbers month over month, not matching someone else's.

The One Metric That Matters

If you could only track one thing:

Are people paying you more over time?

Revenue. That's the ultimate validation. Everything else is proxy.

Focus there. The rest will follow.

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