A customer’s propensity to renew indicates how likely it is that they will extend their agreement rather than cancel it. It is typically gathered via a survey conducted, coupled with other metrics that evaluate anticipated behavioral loyalty markers, such tendency to spend extra, the propensity to add additional offerings or services, and their tendency to suggest.
Propensity to renew may analyze the situation that may be hidden by depending on more conventional functional forecasters of churn, such as complaints from customers or delayed payments. This is because the information comes directly from the users and, in more complicated connections, from several touchpoints within a user’s account. For instance, a client may report a lesser inclination to renew due to ignorance that prevents them from effectively utilizing the platform.
When working to obtain renewals, account managers and customer success leaders assess it at the customer level. On a broad scale, it is a sign that executives examine to provide a prospective perspective of possible revenue risk and probable logo turnover. When the risk offered by the client exceeds expectations, a review of the measure done far enough in preparation for the renewal date may lead to successful intervention methods and the preservation of client relationships.
Propensity to Renew, which is regarded as an outcome measure, is a trailing signal of unsatisfactory user experiences during one or more of the regular contacts a client has with the business or the staff, even if it acts as a good predictor for risk. Low or deteriorating ratings should prompt a more thorough examination of the underlying experiences in order to find and address any weak points, either for a specific client or more generally.
The propensity to Renew can be calculated with the formula below:
Let’s understand how to use the formula better.
When the users of a SaaS business platform have a set expiry date, this measure is typically collected via periodic conduction of surveys. To define the extension plan and prepare for development, it is ideal for conducting the survey one quarter before the expiration deadline.
You can simply ask a question, such as, “What is the possibility for you to renew your subscription on the platform based on your experience in the past six months?”
Give straightforward choices for responses by using a rating scale with five labels of the following answers: Highly Probable, Quite Probable, Somewhat Probable, Not Very Probable, and Not At All Probable. This scale has been shown to have less diversity in the understanding of replies.
If you want a more definite response, modify the question to generate a Yes or No response. You may use a 7-point scale, ranging from 1 to 7, with seven being the most likely to renew and one being the least.
As a recommended strategy, make sure the scale you choose is consistent with other scales in your survey. This will make it simpler for consumers to reply and for staff to evaluate the data.
Let’s understand the calculations with an example.
Say a SaaS platform for music provided by a company named ABC has 70,000 subscribers that need renewal in June. ABC needs to send out a survey to calculate the propensity to renew sometime in April. After sending out forms to all the subscribers, they received 55,000 responses. In the survey, they asked:
“What is the possibility for you to renew your subscription with us based on your experience in the past six months?”
The clients had options as given below:
Of 55,000 responses, 35,000 chose options one and two, 17,000 chose options three and four, and the remaining 2,000 chose the last option.
As per the responses, the propensity to renew can be calculated with the formula:
Propensity to Renew = Number of answers in the top 2 categories of favorable responses / Number of Propensity to Renew replies that were valid x 100
Number of answers in the top 2 categories of favorable responses = 35,000
Number of Propensity to Renew replies that were valid = 55,000
Therefore, Propensity to Renew = 35,000 / 55,000 x 100 = 63.64%