Compare PM frameworks and metrics, with formulas, examples, and guidance on when to use each.
When you need to choose a scoring method for your backlog.
RICE includes Reach and divides by Effort. ICE uses an Ease score alongside Impact and Confidence. Compare the inputs each needs and the decisions they support.
Compare the inputs each framework uses, the output it produces, and the limits of the score.
When generic Reach/Impact/Confidence/Effort doesn't match how your team thinks about tradeoffs.
When you're choosing between a quick 2x2 sketch and a numeric framework for a real roadmap call.
When you're picking between a research-driven and a stakeholder-driven approach to scoping.
When you want to compare five frameworks and decide which fits your decision.
Two metrics that look similar usually answer different questions. These pages tell you which one to track for the decision you're making.
When you're trying to read your unit economics and not sure which side of the ratio is the binding constraint.
When teams use different names or calculation methods for customer lifetime value.
When you need to know which number to put in the board deck and which to track week-to-week.
When you need to separate revenue losses from the expansion that offsets them.
When expansion offsets cancellations and downgrades, and you need to see both.
When you need to relate churn and retention for the same customers and period.
When you need to pick a usage window that matches how often your product is supposed to get opened.
When you need to distinguish recommendation intent from satisfaction with an experience.
OKRs, KPIs, payback periods. The frameworks teams confuse with each other and the differences that matter in practice.
When the goal-setting framework and the metric you measure success with get treated as the same thing.
When the finance team and the growth team are using the same word for two different calculations.
When you need to explain the total market, serviceable segment, and expected share.
When you have two ways of measuring product-market fit and they're telling you different things.
A/B vs multivariate, statistical vs practical significance. Compare experimental designs and the evidence needed for a release decision.
When you want to test more than one variable at a time and aren't sure if you have the traffic for it.
When the test cleared p < 0.05 but the lift won't actually pay for itself.
When you need to understand delivery speed alongside the volume of completed work.
Compare pages help you choose. Learning Hub articles explain the concepts with worked examples.