Renewal forecast categories should be five: closed, commit, likely, at risk and lost. Each one needs an entry rule stated as evidence somebody outside the team could check, an exit rule that says what forces a move in either direction, and a limit on how long a renewal may sit there. Calibrate each category's close rate against your own last four quarters before weighting anything.
Renewal forecast categories are the first thing a customer success team is asked to produce and the last thing anyone defines properly. Somebody builds a spreadsheet with commit, likely and at risk across the top, the CSMs fill it in, and within two quarters the columns mean something different to each person filling them. The forecast then measures confidence instead of evidence, and the first time it is badly wrong the credibility of the whole function goes with it.
This page is for the Head of CS or CS Ops lead setting the categories up for the first time, or repairing a set that has stopped meaning anything. It gives five categories with entry evidence, exit rules and a time limit for each, the movement rules between them, the arithmetic for weighting them with your own close rates, and the weekly call that keeps the definitions honest. For measuring how wrong the forecast turned out to be, renewal forecast accuracy is the page that covers scoring and the fix.
- Five categories is enough: closed, commit, likely, at risk and lost. A sixth adds argument without adding information, and teams that run three cannot separate a renewal that is confirmed from one that is merely calm.
- Every category needs three rules, not one. Entry evidence decides what puts a renewal in. An exit rule decides what takes it out, in both directions. A time limit decides what happens when nothing changes.
- Weight the forecast with your own measured close rates, not with round numbers. A category is worth what it historically delivered on your accounts, which is rarely the 90% and 50% that get typed into spreadsheets.
- No trustworthy public benchmark for renewal forecast accuracy exists, so do not chase one. Measure your own variance in both directions and improve against last quarter.
- Auto-renewing accounts do not belong in commit by default. A contract that rolls without anyone confirming it is a forecast entry with no evidence behind it, which is the most common way a clean forecast turns out to be wrong.
Questions this page answers
- How do we define commit, likely and at risk for renewals?
- What should renewal forecast categories be in customer success?
- When should a renewal move to at risk?
- How do I weight a renewal forecast by category?
- Should auto-renewing contracts sit in commit?
- What do I ask a CSM in a weekly renewal forecast call?
- How many renewal forecast categories should we have?
- Which five renewal forecast categories should a customer success team use?
- What evidence puts a renewal into commit, likely or at risk?
- How does a renewal move between forecast categories?
- How do I set the close rate for each renewal forecast category?
- Do auto-renewing and multi-year contracts need different renewal forecast categories?
- How do I run the weekly renewal forecast call?
- How does GainTrace keep renewal forecast categories honest?
Which five renewal forecast categories should a customer success team use?
Renewal forecast categories work best as five: closed, commit, likely, at risk and lost. Five is the smallest set that separates the four states a renewal can be in, which are done, confirmed by a person with authority, unconfirmed but healthy, and blocked. Teams that run three columns lose the distinction between a renewal somebody has agreed to and one that is merely quiet, and quiet is where the damage lives.
“The renewals that burn me are not the angry accounts. Those I see coming. It's the ones where the relationship is warm, usage is fine, nobody escalates, and then they don't renew and I'm explaining to my boss why my forecast said safe.”
The stall clock is the maximum number of days a renewal may sit in one forecast category before it is demoted automatically. Commit has no clock, because commit records a fact that already happened. Every other category has one: a renewal that produces no new evidence inside its clock moves down a category without debate, which converts the argument about optimism into an argument about evidence.
The stall clock exists because forecast decay is silent. A renewal placed in likely in January on a good call is still in likely in April on the strength of that same call, and nobody has lied. Attaching a clock to every category except commit means the forecast degrades on its own unless somebody does the work, which is the correct default. Set the clock at 30 days for likely and at risk on a quarterly renewal cycle, and shorten it to 14 days in the final month of the quarter.
What evidence puts a renewal into commit, likely or at risk?
Entry evidence for a renewal forecast category has to be a fact a person outside the customer success team could verify. A written confirmation from the budget holder is evidence. A good call is not. The table sets out what each category requires to enter, what forces a renewal out of it in either direction, how long it may sit there, and who is allowed to make the move, which matters as much as the definition.
| Category | Entry evidence | Exit rule | Stall clock | Who may move it |
|---|---|---|---|---|
| Closed | Counter-signed order, or an invoice raised after the notice window shut with payment terms running | Leaves only to lost, and only on a credit note or a cancelled invoice | None | CS Ops, from the billing system, never by hand |
| Commit | The person who signs has confirmed this term and this value in writing, in the last 30 days | Drops to likely if the confirmer leaves, the value changes, or the date moves. Rises to closed on paperwork | None. Commit records a fact, and the fact does not decay, but the confirmation does | CSM proposes, manager accepts. A commit nobody else has seen is not a commit |
| Likely | Outcomes achieved in writing, an identified budget holder, no blocker, and no confirmation yet | Rises to commit on written confirmation. Falls to at risk on a named blocker or an expired clock | 30 days, or 14 in the last month of the quarter | CSM |
| At risk | A named blocker with an owner and a date. Budget cut, sponsor gone, competitor in, unresolved escalation, usage collapse | Rises to likely only when the blocker is closed in writing. Falls to lost on notice or an expired clock with no plan | 30 days to produce a dated plan, then the manager decides | CSM raises it, manager reviews it weekly. Anyone may flag, nobody may quietly unflag |
| Lost | Written notice, a signed contract elsewhere, or a decision confirmed by the budget holder | Leaves only on a signed reversal, which is rare enough to be an event | None | Manager, with the customer's own words attached |
Commit is the category that needs the most discipline, because it is the one leadership spends. Two tests keep it honest: the confirmation is in writing from whoever signs, and it is less than 30 days old. A practitioner in the corpus offers a third test that works well as an entry question for likely, and it is the best single probe we have seen for a renewal that looks calm.
“every so often I ask, if this customer had to defend this line item to their CFO next week, what would they point to? When the honest answer is weaker than it was a year ago, that's the risk, and it shows up long before any usage dip.”
How does a renewal move between forecast categories?
Movement rules are what turn renewal forecast categories from labels into a process. Upgrades need new evidence, downgrades need only the absence of it, and both need a date and a person attached. Write the asymmetry down, because the natural gravity of any forecast runs upward: a CSM who has had a warm conversation feels entitled to move a renewal up, and nobody feels entitled to move one down.
| Move | What it requires | What is not a reason |
|---|---|---|
| Likely to commit | Written confirmation from the budget holder, dated in the last 30 days | A champion saying it will be fine, or a verbal yes on a call |
| Likely to at risk | A named blocker with an owner and a date, or an expired stall clock | Nothing is needed beyond silence. Absence of evidence is the trigger |
| At risk to likely | The blocker closed in writing by the person who owns it | The escalation going quiet, or the customer becoming friendly again |
| Commit to likely | The confirmer leaving, the value or date changing, or the confirmation ageing past 30 days | A manager's reluctance to reduce the number in the same week it is reported |
| Anything to lost | Written notice, a signed contract elsewhere, or a confirmed decision | A CSM's despair. Lost is a fact, and a premature lost hides a saveable account |
| Lost to likely | A signed reversal or a new order form | A save conversation that has started but produced nothing |
One rule prevents most of the drift: no renewal may sit in the same category for two consecutive quarters without a new dated piece of evidence. The failure mode it kills is the perpetual likely, an account that has been forecast to renew for four quarters, has never been worked, and eventually leaves in the quarter nobody was watching. Where the underlying signals for a downgrade are hard to see, early warning signs of churn when data is scattered covers pulling them out of the tools you already have.
How do I set the close rate for each renewal forecast category?
Close rates for renewal forecast categories come from your own history, not from a published table. Take the last four quarters, look at where each renewal sat 60 days before its date, and calculate the share of that category's ARR that renewed. The numbers usually surprise people: commit comes in below 100%, at risk comes in well above zero, and the gap between likely and at risk is often narrower than anyone assumed, which is a finding about your definitions rather than your customers.
Category close rate = ARR that renewed from the category ÷ ARR sitting in the category at day 60 × 100
- Day 60
- 60 days before the renewal date, or before the notice deadline where one exists. Use one fixed point for every renewal so the categories are comparable
- ARR that renewed
- the value that renewed in the end, counting a downgrade as partial. A renewal at 70% of its old value contributes 0.7
- What good looks like
- commit above 92%, at risk below 45%, and a gap of at least 25 points between likely and at risk. A narrow gap means the two definitions are doing the same job
Weighted forecast = (Commit ARR × Commit close rate) + (Likely ARR × Likely close rate) + (At risk ARR × At risk close rate) + Closed ARR
- Category close rate
- your own measured rate from the formula above, refreshed every quarter, expressed as a decimal
- Closed ARR
- already signed or invoiced, counted at full value with no weighting
- What good looks like
- the weighted forecast lands within 5% of the actual outcome two quarters running. Publish the variance in both directions so sandbagging is as visible as optimism
Worked example
A team looks back at four quarters of renewals and finds that at day 60, $3.1M sat in commit and $2.94M of it renewed, a 95% close rate. Likely held $2.4M, of which $1.68M renewed: 70%. At risk held $900,000, of which $315,000 renewed: 35%. This quarter the forecast shows $1.2M closed, $2.0M commit, $1.4M likely and $600,000 at risk, so the weighted forecast is $1.2M + (2.0 × 0.95) + (1.4 × 0.70) + (0.6 × 0.35) = $4.29M against a $4.5M target, a gap of $210,000 that is visible eight weeks early. These figures are illustrative; run the calculation on your own four quarters.
No trustworthy public benchmark for renewal forecast accuracy exists, and that is worth saying plainly because plenty of pages assert one. Every source we could find quoting a variance range for elite teams published no survey, no sample and no method, and several assert the same range in identical wording. Measure your own variance instead and improve on last quarter. The nearest verified reference points are renewal and retention rates: a median annual renewal rate of about 91% across 132 tech companies in CJ Gustafson's January 2026 survey, and 91% median gross revenue retention across more than 1,000 private B2B SaaS companies in SaaS Capital's 2025 brief. Both are self-selected samples, and both describe outcomes rather than forecast quality.
Do auto-renewing and multi-year contracts need different renewal forecast categories?
Auto-renewing and multi-year contracts use the same five renewal forecast categories with different entry defaults, and getting those defaults wrong is the most common reason a clean forecast turns out to be wrong. A contract that rolls unless the customer objects has a high close rate and no evidence behind it, so parking it in commit inflates the number with silence. A multi-year contract in a locked year is not a forecast entry at all.
| Renewal type | Default entry | What moves it | Use this default when |
|---|---|---|---|
| Auto-renewing, no negotiation expected | Likely, never commit | Written confirmation moves it to commit. The notice deadline passing moves it to closed | The contract rolls by default and the clause is enforceable in that jurisdiction |
| Auto-renewing with an uplift the customer has not seen | At risk until the uplift is acknowledged | Acknowledgement of the new price in writing, from a budget holder | The increase is above about 5%, or procurement has to approve it |
| Annual, actively negotiated | Likely at day 90, commit on written confirmation | The standard rules. Evidence up, silence down | Always. This is the case the categories were designed for |
| Multi-year, inside the locked term | Outside the forecast entirely, tracked as future exposure | Entering the final 12 months moves it into the forecast at likely | The term has more than four quarters left and no mid-term break |
| Multi-year, final year of the term | At risk until the term-end conversation has happened | A dated restructure or renewal conversation moves it to likely | Always. A term-end renewal is a new purchase decision with three years of price movement attached |
The auto-renewing row is the one that causes trouble at scale, because the close rate on those renewals is high enough to make the default feel harmless. It is harmless for one cycle. Auto-renewal clauses covers what a renewal without a decision costs in the following year, and multi-year contracts covers why the final year of a long term deserves the at risk default.
How do I run the weekly renewal forecast call?
The weekly renewal forecast call has one job: to test category entries against evidence, in a fixed order, in under 45 minutes. Run it on the movements and the stalls, never on a full read-out of the portfolio, which is how these meetings turn into an hour of narration that nobody can act on. Forecast quality improves in the first month because everyone knows the questions are coming.
Open with the number and the change since last week
Weighted forecast, target, gap, and what moved. Thirty seconds. Everyone in the room should know the size of the problem before any account is discussed.
Walk every upgrade and ask for the evidence
Anything that moved up this week gets one question: what is the new fact and when was it dated. A move with no new evidence goes back to where it came from, in the meeting, without discussion.
Walk every expired stall clock
The system has already demoted them. The conversation is about what the next dated action is and who owns it, not about whether the demotion was fair.
Review at risk accounts against their dated plans
Each one needs a blocker, an owner and a date. An at risk account with no plan after 30 days is a decision for the manager, and the decision is usually to escalate or to forecast it lost.
Check the largest renewals regardless of category
Take the top five renewals by value in the next two quarters every week, whatever category they sit in. Concentration risk is not a category problem, and a single large commit can be worth more than the entire at risk column.
Close with the actions, not the summary
Who is doing what by when, written in the same place the forecast lives. A call that ends in a discussion ends in nothing.
Data quality decides whether the call is possible at all. Forecasting appears in 92 of 4,978 public G2 reviews of customer success platforms that we read (1.8%), and where reviewers describe it failing, the cause is almost always upstream: renewal dates entered by hand, a sync that lags the CRM, or risk updates that never reach the account record.
“Though many of our other problems with [the platform] is bad data from our Salesforce, it took more time to get buy ins, as we have had some hiccups due to sync issues which has caused some distrust with the CSMs that need to update renewal forecasts.”
“This inconsistency means I have to manually search for and update information like renewal updates or risk updates, which do not appear automatically on the account page.”
Before you publish the category definitions
- Each category has entry evidence a person outside the team could verify.
- Each category has an exit rule for both directions, written as a fact and not a feeling.
- Likely and at risk have a stall clock, and something automatic enforces it.
- Every category has a named role allowed to move a renewal into it.
- Close rates come from your own last four quarters and are refreshed quarterly.
- Auto-renewing contracts enter at likely, not commit, and multi-year deals in locked years sit outside the forecast.
- Variance is reported in both directions, so a conservative forecast is corrected as fast as an optimistic one.
- One page holds the definitions, and new CSMs read it in their first week.
Where the forecast is owned outside customer success, the definitions still have to be shared, because the CSM is the person supplying the evidence. A CS leader in the corpus describes ten years of never owning the number, which is common and leaves a team reading a dashboard somebody else built.
“At my current company, Finance has always owned NRR/GRR and renewal forecasting. I wasn't even included in those meetings or given access to a lot of the financial side ... I knew my customers, knew who was renewing, who was at risk, where we had expansion opportunities, etc., but I was basically looking at dashboards that someone else built rather than being the person responsible for building the forecast.”
How does GainTrace keep renewal forecast categories honest?
GainTrace enforces the evidence rather than collecting the opinion. It connects billing, CRM, product usage and support, so renewal dates and notice deadlines come from the contract and not from a field somebody retyped, and it flags a category entry whose supporting evidence has aged past its stall clock. Renewal forecasting shows the weighted number with the accounts and signals behind each category, and customer health shows the usage and sponsor changes that should move a renewal down before the quarter does it for you.
Frequently asked questions
How do we define commit, likely and at risk for renewals?
How many renewal forecast categories should we have?
When should a renewal move to at risk?
Should auto-renewing contracts sit in commit?
What close rate should we use to weight the renewal forecast?
Is there a benchmark for renewal forecast accuracy?
How this was researched
The category definitions, the stall clock, the movement rules and the entry defaults by contract type are ours, built from how practitioners describe forecasts failing. Practitioner evidence comes from 4,978 public G2 reviews of customer success platforms, of which 92 (1.8%) mention forecasting, and from a corpus of 33,600 posts in r/CustomerSuccess, r/SaaS, r/sales and r/startups collected between May 2024 and September 2026. Renewal and retention reference points come from CJ Gustafson's Mostly Metrics survey of 132 tech companies (January 2026, renewal-rate figures stated as approximate in the source) and SaaS Capital's 2025 B2B SaaS Retention Benchmarks (more than 1,000 private B2B SaaS respondents, medians, self-selected). We searched for a published benchmark for renewal forecast accuracy and found none with a disclosed sample or method, which is why this page gives a measurement procedure instead. The worked example uses illustrative figures.
- r/CustomerSuccess: How do you catch an account that is perfectly happy but has quietly stopped needing you?
- r/CustomerSuccess: 10 years in CS and somehow I have never owned the renewal forecast
- Mostly Metrics (CJ Gustafson): The Customer Success benchmarks you have been waiting for
- SaaS Capital: 2025 B2B SaaS Retention Benchmarks (Research Brief 32)
- r/CustomerSuccess: Looking for a CS coach (on what formal CS structure includes)
Write the five definitions on one page this week, then calibrate the close rates against your last four quarters before the next forecast call. Start free or book a demo.
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