Picking your north star for your motion and stage
A north star metric (NSM) is the single number that, if it grows consistently, you believe the business will be healthy and growing. The important word is "believe": the NSM is a thesis about causality, not just a metric that goes up.
The most common NSM mistakes for solo founders:
Borrowing a PLG north star for a sales-led business. If your motion is enterprise SLG, Daily Active Users tells you very little about revenue health. The right NSM is something like "qualified pipeline created" or "expansion NRR" depending on stage.
Picking a vanity metric that is disconnected from value delivery. Monthly website visitors is not a north star unless visitors converting to something valuable is a tight and proven relationship.
Not changing the NSM as the stage changes. The correct NSM in the 0-to-10-customer phase ("does the product work and can we close?") is different from the NSM in the 10-to-100-customer phase ("can we repeat the first ten?") and different again in the 100-to-1,000-customer phase ("can we build a system that does this without me?").
NSM by motion and stage
| Motion | Stage | North star candidate |
|---|---|---|
| PLG | 0-10 | Activated users (hit aha moment) |
| PLG | 10-100 | Weekly active teams |
| PLG | 100+ | Paid seat expansion rate |
| SLG | 0-10 | Qualified discovery calls booked |
| SLG | 10-100 | Pipeline created per month |
| SLG | 100+ | Net Revenue Retention |
| Hybrid | 0-10 | PQL-to-demo conversion rate |
| Hybrid | 10-100 | Revenue from product-sourced deals |
| Hybrid | 100+ | Product-influenced NRR |
A solo founder I worked with was tracking MQL volume as a north star, hitting the number every month, and still missing revenue targets. The diagnosis: MQLs were converting to SQLs at 12%, well below the 25–30% benchmark for their segment (Forrester B2B Marketing Benchmark, 2024). The real constraint was not lead volume but lead quality and sales process, which MQL volume as an NSM completely obscured.
Once she switched the NSM to "qualified discovery calls booked" (a higher-bar, more causally connected metric), she saw immediately that 60% of her MQL-to-SQL gap was from a single channel producing cheap but low-intent leads. The channel was cut, the NSM improved, and revenue followed three months later.
For the full decision framework on choosing the right metric for your motion, see the Pick your north star metric playbook. AI agents are useful here for running sensitivity analysis: "if this metric improves by 20%, what does that imply for revenue in 12 months, under these assumptions?"