Who This Helps
You're a Junior Analyst who wants to stop spinning and start shipping. You have data, but you're not sure which experiment to run next. This is for you.
Mini Case
Meet Ben. He runs a small SaaS company. Revenue is up 20% this quarter, but cash is flat. Ben has three possible experiments: cut ad spend on Channel A (which costs 12% of revenue), raise prices on the Pro plan, or hire a sales rep. He needs a clean analysis with a clear recommendation by Friday.
You pull the numbers. Channel A has a CAC payback of 8 months, while the company average is 5 months. Raising prices could increase revenue by 15%, but might churn 3% of customers. Hiring a sales rep costs $60k upfront with a 6-month ramp. Which one do you prioritize?
Do This Now (5 Steps)
- List all possible experiments. Write them down. No filtering yet.
- Score each on impact. Use a simple 1-5 scale for revenue potential, cost, and risk.
- Calculate the quick wins. Which experiment takes less than 7 days to test? That's your low-hanging fruit.
- Check your runway. If cash is tight, avoid experiments that burn more than 10% of your monthly budget.
- Pick one. The experiment with the highest impact score and lowest risk wins. Ship it.
Avoid These Traps
- Analysis paralysis. Don't wait for perfect data. Use 80% confidence and move.
- Shiny object syndrome. Just because an experiment is fun doesn't mean it's high-impact.
- Ignoring cash. Revenue is great, but if cash is flat, prioritize experiments that preserve runway.
- Overcomplicating. A one-page unit economics snapshot is better than a 20-slide deck.
- Skipping the recommendation. Your analysis is useless without a clear "do this next" statement.
Your Win by Friday
By Friday, you'll have one prioritized experiment with a one-paragraph rationale. Ben will know exactly which move to make. You'll ship clean analysis, build trust, and feel like a rockstar. And hey, you might even get to leave early.