On the Internet you can find many definitions of CLV (and also its various other names, such as LTV or LCV), i.e. the value of customer life. Without going into details, we can assume that CLV is the sum of income that has been (or will be) generated during the relationship between your company and its customers. Of course, you are never sure how long this business interdependence will take, and how much it is affected by various factors, therefore normally we calculated CLV for a certain period of time. For example, you can specify that “monthly/quarterly/annual CLV of X client was Y EUR.”
How do you calculate CLV? Historical methods
A qualification of the value of CLV for each client or group of clients can be accomplished in two ways: historical and predictive. In this article I will focus on the historical approach, which is simpler in use. On the other hand, examples of advanced methods of calculating CLV can be: moving averages, regression analysis, Bayesian conclusion and model Pareto/NBD. Discussion of these advanced techniques will appear in the following text, in which we will focus only on the predictive methods of calculating CLV.
Let’s go back to the historical approach. There we can distinguish two common ways of calculating the CLV using the ARPU (average revenue per user) or by cohorts analysis, which we described in details in a previous article. I’ll start with the characteristics of the method of the ARPU, which involves the use of existing customer spending in determining his or her CLV. I’ll do this using a simple example to explain its mechanism. Let’s suppose that in the last six months, clients named John and Kate have shopped in the following way:
If in half of the calendar year you will want to calculate the average monthly CLV for John and Kate, then you can do the following:
- John: (50 EUR + 0 EUR + 0 EUR+ 50 EUR+ 50 EUR+ 200 EUR) / 6 = 58,33 EUR;
- Kate: (300 EUR+ 0 EUR+ 0 EUR+ 150 EUR+ 0 EUR+ 100 EUR) / 6 = 91,67 EUR.
The formula is very simple. You count the sum of all revenues received from a client and divide it by the length (number of months) of your relationship. Both Kate and John bought something in the last six months.
The obtained ARPU value is then multiplied:
- x 12 to calculate the annual CLV; one-year time perspective is assumed for clients who are interested in products with high seasonality of sales, for example shoes;
- x 24 to calculate a two-year CLV; two-year and longer CLV should be calculated for clients who are interested in long-term products or services, such as computers.
Thus the annual CLV for Kate will be in 1100 EUR, in the case of John it will be 699 EUR.
The ARPU method is simple to use, which is its unquestionable advantage. While using it you must remember not to do a simple conversion of a total revenue by a total number of clients, because the average obtained in this way says nothing about the actual CLV. You should also be aware that it is a method of calculating CLV which does not take into account many variables, such as the differences between the new and old clients, changes in the offer or seasonality. The characteristics of your clients and their behavior change over time.
Therefore you might be interested in the previously mentioned cohorts analysis, through which you will discover not only the level of spending of individual clients – for example, those who have made their first purchase in March – but also discover patterns of consumer behavior in the coming months. The disadvantage of the ARPU is the fact that it is a simple average. Therefore, the value calculated CLV can always be changed by the high value of purchases during the first months of the relationship between the company and its clients. For example, if you have a lot of new customers who make numerous purchases at the beginning, the CLV calculated for them may be overestimated. Generally, if you run a new company or one that is going through a period of very high growth or change, then you should approach the calculated CLV with caution. There is a high probability that the resulting value will not coincide with the actual behavior of your clients in the coming months.
Discrepancies between the calculated CLV and actual purchases of clients in the case of the ARPU method can reach values of 50-120%. If you want to minimize the risk of a revaluation – use the cohort analysis.
The easiest way to calculate the CLV using cohorts is to create a “medium cohort “, that is where the average of purchases from the first month of a customer’s relationship with you is stored. Then calculate the same for the second and subsequent months:
As you can see in the chart above, which is an example of the calculation of CLV using cohorts, the level of consumer spending falls in the following months. While the average value of purchases for the first month is 100 EUR, after a year of relation it falls by 90 EUR and stabilizes at around 10 EUR per month. The chart can be used to estimate the value of the CLV. For example, if you want to calculate the 12-month CLV for a new customer, you add up all the average values for 12 months. On the other hand, if you want to estimate how much clients who have made their purchase three months earlier will spend the next 9 months, add up the values of the last 9 points (months) on the chart.
Is it worth to use the CLV?
Indicators such as the CLV have a significant advantage over the other parameters that characterize your business. The number of clients, sales, growth rates – all this says a lot about the current situation but only to a small extent can we anticipate and respond to the question “how can this be?” The calculation of CLV allows us to estimate the future state of the business, so you can take appropriate early intervention. Moreover, the use of this indicator will enable you to identify the group of clients who spend most of their resources (in practice this means cash, but also time and interest) for your products or services.
Without this knowledge you will not be able to implement appropriate marketing strategies aimed at maintaining or increasing the involvement of a customer with, for example, previously described scoring. The 20% group mentioned at the beginning is a group of consumers that you should take special care of because they are most likely they are responsible for a large part of your income. Their loss can be very painful. Another key benefit of the CLV in business practice is to optimize the process of managing clients and their acquisition. You can calculate the CLV for the different sales channels (for example an online store, mobile application and profile auction site), and then decide to increase or decrease spending on any of them.