There is no universal retention metric.
Subscription, ecommerce, marketplace and B2B contracts have different lifecycles. Monthly churn is critical for subscription businesses but may not be the primary measure for categories with irregular repurchase cycles.
Metrics must reflect real customer behavior and the revenue model.
Cohorts separate growth from quality.
Aggregate repeat rate can increase simply because the mix contains more existing customers. Cohorts by first-purchase month, acquisition source or product make customer quality easier to compare.
This also connects retention back to acquisition and shows which sources create stronger customer value.
LTV is only useful when assumptions are explicit.
An LTV model can look precise while being highly wrong if churn, margin or discount assumptions are optimistic. Ranges and sensitivity are often more useful than one number presented as fact.
When the business lacks enough history, realized value and payback can be more reliable than long-range forecasts.
Good metrics lead to action.
If repeat rate declines, the team needs to know which segment changed, when, on which product and which intervention is likely to matter. The dashboard is the start of the question—not the answer.