Have you ever read that the way to calculate your customer lifetime value (CLV) is ARPA x margin/churn rate? Or some other variation, where your churn rate appears somewhere in the formula? It's tempting to follow this seemingly simple approach, as CLV is such an important metric.
You know your average revenue per customer, you know your margin, you know your churn rate. So you know your CLV, just like that. Voila! But don’t believe it!
The truth is, using churn rate in your CLV calculations will only lead to inaccurate and misleading results. Let's dig a bit deeper into why that is and explore a better way to think about CLV.
First, let's clarify what ‘churn’ is. Churn rate tells you the proportion of customers you lost in a given period, typically a month. Revenue churn rate, on the other hand, informs you about the proportion of revenue lost from your existing customers. What churn rate does NOT tell you is how long your customers are likely to stay. That's where the problems begin.
Using churn rate to calculate customer lifetime can be misleading because it assumes:
In the SaaS world, it's common to sign up many new customers but lose a significant portion of them within the first few months. Customers don’t leave at a constant rate every month. What actually occurs is typically more like what’s shown in this graph. This graph shows attrition in the 4% to 6% range early on. But after the first year the monthly attrition is below 2%. This example is from the small business segment which has naturally high attrition. Your churn numbers may be higher or lower, but the point is the pattern over time. In other words, after the initial period, attrition rates typically drop, and customers who stay beyond the first year tend to remain loyal for quite some time.
Utilizing churn rate in your calculation of CLV can result in counterintuitive conclusions. Things that just don’t seem to make sense. For instance, if your business is growing rapidly, your recent churn rate might be higher due to the influx of new customers, a proportion of whom typically don’t stay. The effect of the churn-based CLV calculation would have you believe that your CLV has decreased, even though long-term customers remain loyal. Conversely, during periods of low growth, your churn rate might drop due to the relative lack of short-stay new signups. In that scenario you will see the churn-based CLV calculation falsely implying an increase in CLV.
Relying on churn rate to determine customer lifetime can cause inconsistent and fluctuating customer lifetime values. For instance, if your churn rate jumps from 3% last month to 5% this month, your churn-based average customer lifetime – which includes long-term loyal customers – will fall from 33 months to 20 months. A churn-based customer lifetime calculation varies with the churn rate you use, resulting in wildly fluctuating values that are illogical. Why should a fluctuation in last month’s churn rate affect your long term customer lifetime so dramatically? It shouldn’t, and it doesn’t.
If you're using net revenue churn rate, as many experts recommend, and your business experiences "negative churn" (where existing customers’ increased spending exceeds lost revenue), the churn-based calculation fails altogether, as it yields a nonsensical negative customer lifetime number. An arithmetic failure such as this is an indication of a flawed approach.
Undeniably, an accurate calculation of customer lifetime and CLV is an essential component of the ultimate SaaS metric, the CLV:CAC ratio. The CLV:CAC ratio is arguably the best indicator of the unit economics of your recurring revenue business. Ultimately it helps answer the question: Is my business viable?
Using churn rate to calculate the customer lifetime component of customer lifetime value is a flawed method, yielding nonsensical results. It seems that in the case of customer lifetime and CLV expert recommendations don't always guarantee accuracy. Instead of relying on what people write and say, it’s better to trust in the logic of mathematics. Math is indifferent to popularity or perception and provides only the truth – which is what we want for making informed business decisions.
By all means calculate your churn rate to monitor recent losses of customers and revenue as a proportion of your customer base. But leave churn rate there. Don’t let churn rate near your CLV.
If you can’t use churn rate to calculate customer lifetime, how do you do it? We need a more accurate approach to customer lifetime and CLV. Something that is sound, mathematically, that we can rely upon.
Understanding the value of CLV and the CLV:CAC ratio to inform important business decisions, 19metrics has set out to solve the problem.
Instead of being deceived by the alluring, fatally flawed simplicity of the 1/churn shortcut to customer lifetime, the 19metrics method is to apply advanced statistical techniques to analyze what actual happens with your subscription customers, techniques that take into account that a proportion of new customers may not stay long, that some of those customers reactivate, that loyal customers may stay a very long time. This mathematical approach evaluates your actual expected customer lifetime - and it’s almost never going to be the reciprocal of your last month’s churn rate.
Armed with a customer lifetime number you can count on, you can calculate your CLV and your CLV:CAC ratio and you'll be better equipped to make informed decisions about your business's recurring revenue model.
The fundamental flaws of using churn rate in calculating CLV can lead to misleading results and poor decision-making. It's crucial to explore reliable and mathematically sound alternatives, like the 19metrics method, rather than depending on popular but flawed formulas.
By focusing on accurate and detailed insights derived from advanced statistical techniques, you'll be better equipped to make informed decisions about your business's recurring revenue model.
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