Renewals are a retention forecast, not a copy-forward
Forecasting renewals means segmenting the base by health, applying realistic renewal rates to each segment, and accounting for expansion and contraction, not assuming last year repeats. The lazy version copies the current base forward at a flat renewal rate, which hides both the accounts about to churn and the ones about to grow. A real renewal forecast starts from the actual state of each customer and builds up, which is why it belongs alongside the new-business revenue forecast rather than buried inside it.Segment by health, then apply rates
The method that works has three steps:
1. Segment the base by health and risk using real signals, not gut feel. 2. Apply a renewal probability to each segment from history and current state. 3. Layer expansion and contraction so the forecast reflects net movement, not retention alone.
This produces a range, not a point, and it ties directly to gross revenue retention on the downside and net revenue retention once expansion is included.
Watch the leading signals
The accuracy of a renewal forecast lives in the risk signals feeding the segmentation. Declining product usage, a drop in engagement, the loss of an internal champion, and rising support escalations move months before the renewal decision surfaces. A base scored on those signals gives customer success time to intervene and gives the forecast a genuine read on at-risk revenue. The difference between a renewal forecast that helps and one that surprises you is whether it watches the renewal rate by health segment or treats the whole base as one flat number that reveals its problems only at the renewal date.
Frequently Asked Questions
How do you build a renewal forecast?
Segment the customer base by health and risk, then apply a realistic renewal probability to each segment based on history and current signals, and layer in expected expansion and contraction. Summing those gives a renewal forecast grounded in the actual state of the base, rather than assuming every account renews at last year's rate.
What signals predict whether a customer will renew?
Product usage trends, engagement levels, the presence or loss of an internal champion, support escalations, and whether the customer has reached the value they bought. Declining usage and a lost champion are among the strongest early warnings, often visible months before the renewal date.
Why not simply assume a flat renewal rate?
Because it hides risk and expansion both. A flat assumption treats a declining account and a thriving one identically, which misses the at-risk revenue you could save and the expansion you could plan for. Segmenting by health turns a guess into a forecast you can act on.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like how do you forecast renewals? into prescriptive action for your team.
Schedule a Demo