When I work with early-stage founders, one of the first conversations I try to have is about metrics. It’s tempting to celebrate shiny growth numbers — signups, app installs, social followers — but those “vanity metrics” often mask the health of the business. I’ve seen teams chase headline figures that look great in investor decks but don’t tell you whether the product is solving a real problem, whether customers stick, or whether the model can scale profitably.
Picking the right KPIs early isn’t about picking as many as possible. It’s about choosing a small set of metrics that connect directly to your business model, can be influenced by the team, and tell you if you’re progressing toward sustainable growth. Below I share a practical, repeatable approach I use with startups, examples by stage, and simple rules to avoid common traps.
Start with your model: what actually creates value?
I always begin by asking: how does this business create value, and how does that value convert into revenue? Your KPIs should map to that flow. For example:
Once you’ve sketched the value path, pick one metric per stage that is both meaningful and actionable. That gives you a chain of KPIs you can optimize end-to-end.
Define a North Star metric and the supporting KPIs
The North Star metric is the single number that best captures long-term value creation for your users and your company. I favour North Stars that combine product value and revenue potential — for example, number of active paying users or revenue from customers who used core feature X in the last 30 days. Avoid pure volume metrics like “total signups.”
Under the North Star, choose 3–5 supporting KPIs that explain the drivers. Those should include:
Leading vs lagging: balance your dashboard
Early-stage teams need leading indicators to iterate quickly and lagging indicators to validate assumptions. Leading metrics let you test experiments in days or weeks — things like activation rate, onboarding completion, or time-to-first-value. Lagging metrics like revenue, churn, or LTV confirm whether your optimizations actually moved the needle over months.
My rule: include at least one leading metric that reflects customer behaviour and one lagging metric that reflects economic outcome.
Cohorts, unit economics and the timeframe that matters
Cohort analysis is one of the most underused tools. Look at how customers who joined in the same week or month behave over time. Cohorts uncover whether improvements are durable or simply front-loaded growth.
Unit economics matter earlier than you think. Even with small ARPU, if CAC payback is reasonable and gross margin supports scaling, you have options. Track:
Make KPIs actionable: tie them to teams and experiments
A KPI is useless if no one can influence it. For each metric, document:
This turns a dashboard into a roadmap. For example, if your activation rate is 20% but you think onboarding improvements can lift it to 40%, design small experiments (copy changes, progressive disclosure, tooltips), run A/B tests, and measure changes in the leading metric first.
Common KPI sets by stage (practical examples)
| Stage | North Star | Supporting KPIs |
|---|---|---|
| Pre-seed / Product-market fit | Proportion of users who reached first core value moment | Activation rate, NPS for early users, weekly retention (W1-W4), qualitative feedback volume |
| Seed / early growth | Active paying users | Trial-to-paid conversion, CAC by channel, 30-day retention, ARPU |
| Scale | Revenue from retained customers | LTV, CAC payback months, churn rate, expansion revenue %, gross margin |
These examples aren’t prescriptive; adapt based on whether you’re marketplace, SaaS, e-commerce, or services. I’ve used Google Analytics and Mixpanel for user funnels, Stripe reports for revenue and churn, and HubSpot/Salesforce to tie acquisition to deals. For reporting I often recommend a lightweight analytics layer like Looker Studio or a simple Snowflake + dbt stack if you’re ready to invest.
Data quality: measure what you trust
Bad data creates bad decisions. Before trusting a KPI, verify:
Set up a short checklist and sample audit (10–20 random users) every month until your tracking is stable. I’ve seen founders optimize the wrong funnel because their “purchase” event fired on an add-to-cart action rather than completed checkout — a costly mistake.
Benchmarks and expectations: be realistic
Benchmarks are useful but dangerous when applied blindly. Early-stage averages vary wildly by vertical and go-to-market. Instead of comparing to an industry headline, define your own targets based on two questions:
Set short-term measurable goals tied to experiments, and longer-term economic thresholds that must be achievable to scale.
Iterate your KPIs as the business matures
KPIs should evolve. Early on you might track “first value moment” and qualitative signals. Later you’ll add LTV, expansion revenue and unit economics. I recommend reviewing your KPI set every quarter and asking: does this metric still map to our value creation? If not, prune it.
When adding new KPIs, keep the dashboard lean — 6–8 metrics max — and ensure each has a clear owner and a playbook for improvement.
Practical checklist to choose your KPIs this week
If you want, send me the core funnel of your product (e.g., landing → signup → activation → trial → paid) and I’ll suggest a starter KPI set and 3 experiments you can run this month to move the needle. Practical metrics are the foundation of repeatable growth — choose them deliberately, not because they look good on a slide.
