Metrics Monday 02: Customer Retention Rate
A short-and-sweet weekly(ish) post, because I like metrics and I like alliteration.
Whether you’re an analyst, a business owner, or an individual contributor in sales, marketing, or customer experience, it’s 🗝 key to familiarize yourself with the metric we’ll delve into today: customer retention rate (CRR). One very important reason is that retention and churn (covered in Metrics Monday 01) are negatively correlated (as one goes up, the other goes down).
Like churn, there are industry- and context-specific nuances, but customer retention rate boils down to measuring how well you’re doing at keeping existing customers over time. In an economic downturn, slump, recession, fucking-terrible-time-to-do-business, or whatever you want to call our current global economic situation, your customer retention rate requires your focus now more than ever.
It’s 6 to 7 times more expensive to acquire a new customer than it is to retain an existing customer.
We’ll use a definition from Salesforce to frame our discussion, as we did with churn:
Customer retention rate is the percentage of existing customers who remain customers after a given period.
Let’s translate that definition into a calculation. Our variables: the number of customers at the beginning of a given period (b), the number of customers at the end of a given period (e), and the number of new customers acquired during the given period (n). So we’ll subtract n from e and divide by b, then multiply by 100 to get a percentage.
Customer Retention Rate = [(Number of customers at the end of the period ➖ Number of new customers acquired during the period) / Number of customers at the beginning of the period] * 100
or
CRR = [(e➖n) / b] * 100
You may recall that my fictional noodles subscription service, Nicole’s Noodles, experienced 9.6% churn in June. In response, we surveyed a sample of subscribers and churned customers and found that those heavy buckwheat noodles really disappointed them, particularly during this brutally hot summer. We decided we must focus on keeping those customers who stuck with us despite our noodles no-no. Additionally, we offered churned customers two months free if they’d give us another shot.
Let’s look at the numbers from July. We started with 2,541 customers on July 1. 117 more customers canceled in July, but of the 271 we lost in June, we won back 82 with our two-months-free promo. 39 net-new customers subscribed. Let’s plug those numbers into our variables.
The number of customers at the beginning of the month, b, = 2,541
The number of customers at the end of the month, e = 2,545
New customers acquired, n = 39. Right? Or do we include the 82 customers we managed to salvage our relationships with as new customers? We could include them in n or recalculate e without them and get the same mathematical result, but we need to be able to measure how well (or not) our promo strategy has worked in September when their two free months have passed.
In this case, the best course of action is to create a new variable, adjusted number of customers at the end of the month (a) to substitute for the actual number of customers at the end of the month (e) so we can track this group separately, allowing us to measure our promo’s effectiveness and to avoid falsely inflating our retention rate.
So let’s modify our calculation to find a. We’ll need an additional new variable (p) to hold the number of customers receiving the promo. (Stick with me if you haven’t done much math in a while. It really is logical, I promise.)
Adjusted number of customers at the end of the period = Actual number of customers at the end of the period ➖ Customers receiving the promo
or
a = e➖p
Let’s plug in the numbers to calculate the value of a.
Actual number of customers at the end of the month, e= 2,545
Customers receiving two-months-free promo, p = 82
a = 2,545➖82
a = 2,463
Now that we know the value of a, we can use the following modified calculation which just substitutes a for e in our original calculation.
Our original calculation was CRR = [(e➖n) / b] * 100.
Our modified calculation is CRR = [(a➖n] / b] * 100
CRR = [(2,463–39) / 2,541] * 100
95.4% is our customer retention rate for July.
So, is that good? Retention rates vary across industries, so consider your benchmarks accordingly and define your “good” within the context of other metrics, like monthly recurring revenue (MRR), which we’ll explore next time. This is especially important if your customers belong to different subscription tiers or plans.
For fun (WHAT? YOU DON’T THINK THIS IS FUN?), calculate the churn rate for July as described in Metrics Monday 01. Now add the churn to the CRR. Look at the relationship between churn and retention. What do you notice about the relationship between these metrics?
Questions? Comment or message me.