Pick one outcome that reflects delivered value, not internal activity. For a SaaS micro-product, this might be active weekly teams completing a key action. Tie it to leading inputs you control, like qualified signups or first-success rate, so progress becomes actionable on a founder’s timescale.
Leading metrics signal momentum; lagging metrics prove results. Use a compact stack: acquisition velocity, activation rate, weekly active users, new MRR, churn, and runway. Review them together, then decide one intervention. Avoid adding fields endlessly; add measurements only when they unlock a decision you’re willing to take.
Group metrics by decision, not database table. Place the primary outcome top left, supporting drivers beneath, and contextual notes beside trends. Keep sparklines, thresholds, and last-week deltas visible. If the page cannot answer a concrete question in thirty seconds, ruthlessly simplify until it can.
Start with a spreadsheet or Notion database, pipe events through a simple tracker, and graduate to Metabase or Apache Superset when queries grow. Favor tools you can debug at midnight. Document connections, owners, and refresh steps so nothing breaks when you ship three features at once.
Automate daily pulls for operational metrics and weekly summaries for strategy. Use thresholds with humane notifications, never blaring sirens. When a number crosses a line, the alert should propose the first check. Close the loop by logging resolutions, preventing the same confusion from repeating later.
Chart new MRR, expansion, contraction, and churn to see net movement. Compare ARPU by channel to detect underpriced audiences. Use payback period to judge acquisition bets. Document what you will do if a metric crosses a boundary, so pricing becomes a disciplined lever, not a panic button.
List fixed costs, variable costs, and best-case, base-case, worst-case revenue. Project runway each week and mark decision checkpoints in your calendar. When the forecast narrows, cut experiments with slow feedback loops. Protect customer support and product quality, because trust lost during cash stress is costly to rebuild.
Keep a tiny ledger of hypotheses, expected lift, timeframe, and actual impact. Tag each experiment with the metric it intends to move. Review win rate monthly, killing low-yield ideas early. This simple ritual prevents endless tinkering and turns learning into observable, compounding revenue improvements.

Write user stories first, then extract the minimal set of events that prove success or reveal friction. Include properties you will actually query. Version the spec, test in staging, and keep screenshots of expected flows. Small, boring discipline today prevents mysterious discrepancies when you need clarity most.

Collect only what you need, hash sensitive identifiers, and honor user deletions quickly. Lean on cookieless analytics or server-side events when appropriate. Explain your approach in human language within the product. Clear, respectful practices build trust and reduce legal risk, keeping your attention on delivery and learning.

Set guardrails around ingestion volume, error rates, and major metric swings. Visualize rolling baselines and use gentle alerts to prompt review. Keep a short runbook: first check source, then transformation, then visualization. When you fix something, note the cause so future you avoids repeating it.