What differentiates leading and lagging indicators for subscriptions?

WHAT THIS TESTS: This question checks whether you can separate predictive signals from outcome confirmations when designing a growth model. Interviewers want to see that you know leading indicators are inputs that forecast future behavior, while lagging indicators are outputs that validate whether a goal was achieved. At the senior level, they also care if you understand that a leading indicator is only useful once you have proven it correlates with a lagging business outcome.

A GOOD ANSWER COVERS: First, a crisp definition of leading indicators as metrics that predict future performance and lagging indicators as metrics that measure past results, often revenue-related. Second, a subscription-specific example pair: a leading indicator like activation rate within the first seven days or weekly active usage of a core feature, and a lagging indicator like monthly recurring revenue, net revenue retention, or churn rate. Third, the relationship between them, explaining that the leading indicator should be a controllable input that teams can experiment on to move the lagging indicator over time. Fourth, a brief mention of why both matter, since leading indicators give early feedback and lagging indicators keep the business honest about actual financial performance.

COMMON WRONG ANSWERS: Treating any early-funnel metric as a leading indicator without proving it predicts revenue, such as calling total signups or app downloads leading indicators. Confusing the two by saying lagging indicators are just bad or slow metrics. Failing to give concrete subscription examples and instead staying in abstract definitions. Suggesting that revenue itself is a leading indicator. Ignoring the time dimension, which is central to the distinction.

LIKELY FOLLOW-UPS: How would you validate that a candidate leading indicator actually predicts your lagging indicator? If your leading indicator is up but your lagging indicator is flat, what would you investigate? How do you choose the right time window for a leading indicator in a subscription business? Can a metric be both leading and lagging depending on context?

ONE CONCRETE EXAMPLE: For a B2B SaaS subscription product, a leading indicator could be the percentage of new accounts that invite at least two teammates within the first week, because this behavior correlates with account expansion. The matching lagging indicator would be net revenue retention twelve months later. The growth model assumes that increasing the invite rate through onboarding experiments will eventually improve NRR, but the team tracks the invite rate weekly to get early signal while waiting for the lagging revenue outcome to mature.

Source: amplitude.com