Who This Helps
Product Managers staring at a sudden dip in a key metric. You need to move from 'What happened?' to 'Here's why, and here's what we do next.' The Finance Basics for Operators course gives you the simple frameworks to ask the right questions.
Mini Case
Your weekly active user growth dropped from a steady 8% to 2% last month. The team is pointing at a recent app update, but you're not convinced. By applying a basic unit economics lens, you break down the problem. You discover the cost to acquire a user spiked 40% in the same period, while retention held steady. The real issue wasn't the product—it was a change in your ad campaign targeting. You just saved the engineering team a two-week investigation sprint.
Do This Now (5 Steps)
- Isolate the Metric: Pick one KPI that dropped. Write it down. Is it revenue, conversion, or active users?
- Check the Calendar: Note any major events from 4 weeks before the drop—launches, marketing campaigns, or pricing changes.
- Break It Into Parts: If revenue fell, separate it into its pieces: number of customers and average spend per customer. Which piece moved?
- Follow the Money Flow: Trace the problem backward. If customer count is down, look at your acquisition channels. Did a cost-per-click change? Did a key partner stop sending traffic?
- Call the 30-Minute Huddle: Share your one-page breakdown with the core team. Use it to focus the discussion on facts, not opinions.
Avoid These Traps
- Don't jump to the most obvious technical cause first. Take a breath.
- Avoid mixing multiple KPI drops into one diagnosis. Solve one at a time.
- Don't skip the simple financial check. A change in cost or price often explains a volume change.
- Stop the blame game before the meeting starts. This is a puzzle, not a trial.
Your Win by Friday
By Friday, you'll have a single, clear hypothesis for your KPI drop, backed by a simple financial breakdown. You'll walk into your team sync with a one-page doc that turns a chaotic debate into a measurable decision. You'll look like the calm, data-driven leader in the room. And you might just save everyone a lot of wasted time—time better spent building the next great feature.