Last updated: Aug 27, 2026
Propensity to Renew is a measure of the likelihood a customer will renew their contract instead of terminating their engagement with a company, most often captured through a customer survey. It signals revenue risk and potential logo churn.
Alternate names: Churn Risk, Likelihood to Choose AgainTwenty customers respond to a Propensity to Renew survey question on a 5-point labelled scale. Twelve say they are either "Extremely Likely" or "Very Likely" to renew.
12 / 20 × 100 = 60%
A 60% score means just over half of surveyed customers intend to renew. For an account manager, that leaves 8 at-risk accounts to prioritize before the renewal date.
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External benchmarks for this metric are unreliable as companies ask slightly different survey questions and use different scales. However, the percentage can be compared to internal and industry standards of actual churn.
To visualize your Propensity to Renew data, try using a bar chart segmented by customer name to quickly identify your best fit customers, or track your overall Propensity to Renew with a summary chart.
Propensity to Renew
Propensity to Renew
Propensity to Renew operates at two levels simultaneously.
At the business level, executives use the aggregate score as a forward-looking view of revenue risk and potential logo churn. At the account level, customer success managers and account managers use individual responses to prioritize intervention before a renewal window closes.
Because the signal comes directly from the customer, it can surface risk that standard operational indicators miss. A customer may report low renewal intent because of internal changes you have no visibility into: a supplier consolidation, a budget freeze, or a shift in executive direction. Catching that signal 60 to 90 days before renewal gives your team time to respond.
Propensity to Renew is an outcome measure. It acts as a leading indicator of churn risk, but it is a lagging indicator of the customer experiences that shaped it. A low or declining score should trigger a deeper investigation into which touchpoints, product experiences, or service interactions are driving dissatisfaction.
Business level: at least quarterly, as part of a regular review of account health and revenue risk.
Account level: 60 to 90 days before each renewal date. That window is wide enough to allow meaningful intervention and position for growth conversations.
This metric is collected through a regular voice-of-customer survey, timed to one quarter before the renewal date.
Keep the question simple:
Based on your experience over the last six months, how likely are you to renew your contract?
Choose a response scale and stick with it. Three common options:
| Scale | When to use it |
|---|---|
| 5-point labelled scale | Default recommendation; reduces variation in how respondents interpret options |
| 7-point Likert scale (1 to 7) | When you need finer gradation across a larger respondent base |
| Yes / No | When you want a binary, easily reportable signal |
Align your Propensity to Renew scale with the other scales in your survey. Consistency makes it easier for customers to respond and easier for your team to interpret results.
Sample question and response options:
Based on your experience over the last 6 months, how likely are you to renew your contract?
Extremely Likely
Very Likely
Somewhat Likely
Not Very Likely
Not At All Likely
Decline to Answer
In complex B2B relationships, collect responses from multiple contacts within a single account. A single respondent may not reflect the full picture, particularly when buying decisions involve procurement, finance, and end users.
Propensity to Renew rarely lives in isolation. It is typically collected alongside other behavioural loyalty indicators:
Propensity to increase spend: signals expansion revenue potential
Propensity to add products: indicates cross-sell opportunity or stickiness
Propensity to recommend: closely related to Net Promoter Score and referral risk
Track Propensity to Renew alongside actual renewal outcomes over time. As you accumulate time-series data, you can align survey scores with real churn events to build more predictive models of revenue risk, moving from a descriptive indicator to a genuine forecasting input.
