The most feared metric among subscription businesses is churn. The fight against it seems endless, and it doesn’t help that most businesses measure churn by numbers of customers, not by revenue.
Many companies are able to assess churn behaviors, or indicators they think might lead customers to churn in any given month. That gives them an estimate of how many people they predict will churn that month. But what does that translate to in dollars?
To make smart business decisions, businesses must associate the amount of revenue that would exit with the churned customers. This means calculating Net Revenue Retention, or NRR, which can provide indicators as to the health of a business. NRR is the percentage of recurring revenue that you retain from existing customers over a discrete period of time.
NRR is a retrospective metric of how much revenue you retained across all users during a set time and is measured as a percentage. Anything over 100% indicates growth.
To calculate NRR, we look at four revenue values:
Here’s the formula:
(A + B) - (C + D) x 100% = NRR
NRR is a central metric in calculating whether your business revenue is growing or declining. It should also serve as a reminder and indicator that customer retention is a huge part of growth.
When a business experiences higher-than-comfortable churn, often the first instinct is to build a strategy around attracting more customers (aka growing acquisition). Instead of focusing on customers they’ve already paid to acquire, businesses look to spend yet more in CAC to stem the bleed of churn.
But what if they looked to their existing customers instead?
Many businesses track metrics around new customers or new purchases. They also may track behaviors they think may indicate churn, such as inactivity for a certain period or lack of purchases within a given cycle. If they have, say, 500 customers who meet churn behavior criteria, the common school of thought is to think that they’ll need 500 customers to replace all that lost revenue.
But that just isn’t the case.
If you effectively measure customer lifetime value (LTV), you’ll have the insight to know that to replace the revenue of 500 churned customers, you might only need to acquire 200 high-LTV customers.
This common school of thought also does not account for the increased revenue of retention. Imagine you could look at these 500 churning customers and understand not just what their net revenue is, but the revenue on the table.
If 500 customers translates to $1 million in revenue, but your customer insights demonstrate that $700,000 of that revenue sits with just 150 high-LTV customers, wouldn’t you want to prioritize retaining or re-engaging those 150 customers?
With potential lifetime value as a guiding metric, you can guide your retention efforts to capture the most money on the table without wasting resources.
If you understand the behaviors that drive your high-LTV customers, you can analyze their behavior and develop promotions that cater to their specific needs. From there, you could build out processes and automations in order to retain those 150 customers, not all 500.
It does take some testing to determine exactly what your high-LTV customers respond to, but with behavioral insights and predictive analytics, you’ll be starting out pretty close to your goal.
Catering your customer experience to high-LTV customers generates benefits across the board. Gearing your processes and promotions to the customers who generate the most revenue can also help you acquire more customers who meet your LTV criteria.
The first step to using customer retention to grow NRR is to measure what potential churned revenue you have sitting on the table. If your churn rate continues the same, how much does that translate to in lost revenue? Know your revenue risk so you can make informed decisions and test your actions.
To get an even clearer picture of revenue risk, use a predictive analytics solution to identify at-risk customers who also fit the profile of your ideal customer. By understanding who they are and what drives them, you can target and test campaigns for them and measure both the response and the amount of revenue you saved.
Then, continue to adjust your campaigns to optimize results and retention. But! Even better, this kind of testing doesn’t need to be limited just to retention efforts; once you have your ideal customer profile and the predictive analytics robust enough to help you build an effective lookalike audience, you can test offers and acquire more ideal customers who will stick with you for longer.