When Maya launched her SaaS, she trusted her gut, something I’ve done more times than I can count.
But three months in, her “killer feature” landed with a thud: <5% adoption, zero impact on churn, and $200K gone.
So she shifted. Not to heavy analytics or a data science team, but to lightweight, practical experiments.
What helped:
- A fake-door test instead of a full build
- Google Sheets + SQLite for quick pattern checks
- Feature flags to test without risk
- User interviews + heatmaps to understand the “why”
What surprised her most? The simple dashboard, not the complex feature, was where users spent 70% of their time.
With small but thoughtful changes, she saw:
- Activation up 22%
- Adoption up 37%
- MRR up 18%
- Churn down 12%
At OrbiQ, we see this a lot. Founders (especially those of us from engineering backgrounds) often build what we would use. But data has a way of humbling us, and guiding us somewhere better.
The truth is:
- That “quick” feature can cost more than you think.
- Intuition is a starting point, not a strategy.
- You don’t need perfect data, just enough to learn.
Start simple:
Talk to five users. Track 3–5 key events. Pair numbers with conversations. And remember that sometimes, the most effective product move is removing complexity, not adding more.
Takeaway:
You don’t need a data stack to be data-informed. Just a clear hypothesis, a cheap way to test it, and the willingness to let results surprise you.
