Who This Helps
Junior analysts who want to stop spinning wheels and start shipping analysis that actually gets used. You're tired of running experiments that don't matter. You want to prioritize the next experiment so your team sees you as the person who drives results.
Mini Case
Mei, a junior analyst at a fast-growing SaaS company, had 12 experiment ideas on her backlog. She picked the one that seemed most fun: a new onboarding flow. After 3 weeks of work, the experiment showed a 2% lift in activation — but the team needed a 10% lift to hit their quarterly goal. Mei's manager asked her to focus on the pricing page instead. That experiment moved the needle by 12% in 7 days. Mei learned: prioritize by impact, not by what's shiny.
Do This Now (5 Steps)
- List every experiment idea you have right now. No filtering. Just dump them all.
- Score each idea on two things: expected impact (1-10) and effort (1-10). Impact is how much it moves your key metric. Effort is time, people, and data needed.
- Calculate a priority score for each idea: impact divided by effort. Higher score = higher priority.
- Pick the top 3 experiments by priority score. These are your candidates for the next experiment.
- Run a quick sanity check on the top candidate: ask yourself, "If this works, does it get us closer to our team's goal?" If yes, start. If no, move to the next one.
Avoid These Traps
- Falling in love with your own idea. Just because you thought of it doesn't mean it's the best. Let the numbers decide.
- Ignoring effort. A huge-impact experiment that takes 6 months might be worse than a medium-impact one that takes 2 weeks.
- Analysis paralysis. Don't spend a week perfecting your priority scores. Use rough estimates and move fast.
- Forgetting the goal. Every experiment should tie back to a team or company objective. If it doesn't, it's a distraction.
Your Win by Friday
By Friday, you'll have a prioritized list of your next 3 experiments. You'll know exactly which one to start on Monday. Your manager will see you as someone who ships clean analysis with clear recommendations — not just someone who runs random tests. And hey, you might even have time for a coffee break on Friday afternoon.
This approach comes straight from the Data Reliability Leadership course, where analysts like Mei learn to focus effort on the highest-impact move. The mission "Reliability Baseline" teaches you how to define what matters and measure it — so you never waste time on experiments that don't move the needle.