Have you ever heard of the term Customer Lifetime Value? In English term can be abbreviated to CLV, CLTV and LTV. To simplify, I will use the abbreviation CLV from here on.
What is Customer Lifetime Value and why it is important?
The CLV is a piece of information that shows how much our customer is worth. Put simply – how much profit we have earned or can earn with individual customer. We should bear in mind that this data is calculated for the entire cooperation period and/or for all purchases that a customer has made or will make with us at any time (what is then the meaning of the term Lifetime Value J).
There are two types of CLV:
- historical CLV: a simple sum total of profit from all purchases of a customer,
- predictive CLV: a forecast of CLV based on past purchases and different indicators of the user’s behaviour.
Why are these metrics relevant? Knowing CLV is of strategic importance. By calculating CLV the company can focus on the profitability of the individual customer and thus ensure long-term profitability and stability of the entire company.
It can happen that a new customer and their first purchase result in a loss for us. We can do this consciously so that the customer returns to us and makes another purchase. In this way, the company strives to cover the initial loss with subsequent purchases by the same customer and, through a higher number of purchases, achieve a positive CLV and profitability. It is sad that many companies do not measure CLV at all and have no idea whether they have covered the initial loss and, particularly, if their business is profitable or not. At the same time, they are caught up in the process of acquiring more and more new customers, where they incur losses.
This can be illustrated with a very common scenario in marketing of a company that wants momentary effects and immediate profit, while it has no idea how much an individual customer is worth in reality (can it afford a loss with the first purchase?) and how much it can spend on customer acquisition etc. What I want to say is that these metrics should be used more often and should also be more important than individual metrics that are used to calculate CLV (e.g. average transaction profit).
The predictive CLV is important as the comparison between it and the real CLV shows us whether profitability is declining in the long run or not.
Some benefits of CLV
Correct calculation of return on investment related to the Customer Acquisition Cost
Using the CLV metrics, we can calculate which digital channels bring us the most profitable customers. By optimising digital channels, so that they attract maximally profitable customers, we can boost total profit. When optimising, it is important to know the Customer Acquisition Cost, as our aim is to increase the CLV/CAC ratio. The higher the ratio, the higher the ROI (Return on Investment).
As a consequence of correct calculation of the CLV, CAC and ROI metrics, the perspective on the way we are prepared to attract a new customer and at “what cost” also changes. All of a sudden we are ready to invest more money in new customers because we know we will get it back sooner or later.
Another important thing: once we find the segment of the most profitable customers, we can study their characteristics. And then search for new customers that are like them.
More effective advertising – pitch, targeting…
CLV can serve as a segmentation criterion (it may be the only one, but most often it’s an extra one), but then the marketing mix should be prepared separately for each segment. Different communication should be used for each segment, different ads etc. The entire advertising campaign can be even further personalised.
Identification of behavioural patterns
With the help of the CLV metrics, data can be organised in clusters and different behavioural patterns identified. This can be done for those customers who have made their first purchase, but more often for the most profitable customers. When we identify their typical characteristics and particularly the information about what prompted them to make a purchase, we can try and prompt other users who have never made a purchase with us to do it.
More effective marketing to existing customers
When doing marketing to existing customers, we should focus on how different marketing campaigns affected the average CLV rather than on the immediate revenue generated from the advertising campaigns – has the CLV increased, decreased, which campaigns affected it and how. It is especially interesting to compare CLV with the predictive CLV and investigate the differences. We also know that retaining an existing customer is cheaper than acquiring a new one. That is why marketing to the existing customers is even more important.
More effective customer support
Once we know our most profitable customers, we know who to offer our best support and who to focus on. Certainly, this will help us increase their CLV, as they will prefer even more to purchase from us. At the same time, we can learn a lot from them, collect feedback on products and/or services and thus improve them.
How to calculate CLV?
It is simple to calculate historical CLV (CLVH) for an individual customer. It is a sum total of the profit from all purchases made by the customer so far. The following formula is used:
CLVH = (Purchase 1 + Purchase 2 + … + Purchase N) × AGM,
where Purchase N is the purchase value and AGM (Average Gross Margin).
Let me give an example of a customer who has made 3 purchases worth €100, €200 and €300 so far, whereby the AGM was 40%. Their CLVH is:
CLVH = (€100 + €200 + €300) × 0.4 = €240
The formula includes gross margin (at the beginning, the difference between the selling price and purchase cost of product can be used), but often net margin is used. If net margin is used, the calculation is more accurate but much more complex, as a number of other parameters must be taken into account (advertising cost, refunds, taxes…). The calculation based on gross margin usually gives us completely satisfactory information on customer profitability.
The calculation of predictive CLV is important as it enables forecasting of the profit earned with one customer throughout their lifetime. This means we can predict the average value of our customer. Once we know this piece of information, we can better plan our marketing, sales and all other activities, as we know how much we can spend to acquire a new customer.
There are many formulas available. One of the simplest formulas to calculate predictive CLV (CLVP) is the following:
CLVP = (P × AOV) × AGM) × ALT
where P is the average number of monthly Purchases, AOV Average Order Value, AGM Average Gross Margin) and ALT Average LifeTime of a customer in months.
If we use the example of the same customer above (who has made 3 purchases worth €100, €200 and €300 so far, with average gross margin of 40%), we only add two pieces of information, namely that the customer has been with us for 6 months and that the average lifetime of our customers is 20 months. The calculation of CLVP is then simple:
CLVP = (3/6 × €200) × 0.4) × 20 = €100 × 0.4 × 20 = €40 × 20 = €800
It tells us that this customer will presumably bring us €800 of profit in 20 months’ time. Now it depends on us how much of this amount we are prepared to invest in different activities (read: spend forward ;)) and customer acquisition as well as stimulate purchases and at the same time care for profit.
How to calculate the Average LifeTime (ALT) of a customer?
The average lifetime of a customer is calculated relatively simply (even if much more complex models exist that we are not going to explore here at this time). The calculation is based on churn. This is the share of customers who cancel the service or do not make another purchase in a given period.
Let me illustrate how we reached a figure of 20 months in the previous example. It’s simple: If we assume or calculate that we lose 5% of our customers every month, the churn rate is of course 5%. The expected average lifetime of a customer is calculated using this formula:
ALT = 1 / Churn.
In our case it is: ALT = 1 / 5% = 1 / 0.05 = 20 As the average lifetime of a customer is calculated using the churn rate over a period of one month, the average lifetime is also expressed with the same units, i.e. 20 months.
There are several models to calculate CLV. Above are two simple models (without considering the costs of customer retention or the cost of customer acquisition etc.). More complex models are more accurate but at the same time more difficult to calculate.
CLV enables us to get to know our customers better, to segment them better, to focus on those who are more important and to plan our marketing activities more effectively. CLV is a measure that is very important and all companies should be familiar with it and its value. Have you calculated it yet in your company?