The Customer-Lifetime-Value (CLV)
Segmentation and evaluation of customer potential - Part 2
The evaluation of customers and customer relationships has always been a challenge for marketing departments. At the same time, however, it is above all an important control element for a company’s communication strategy, media planning and budgeting. As in Part 1, in this series of articles we look at different approaches to customer evaluation and their advantages and disadvantages.
In this, the second part, we take a closer look at customer lifetime value (CLV). This is a key figure for a quantitative approach to customer segmentation.
How much value can a customer have for a company? To answer this question, the estimation of the financial value of long-lasting customer relationships is crucial. Customer lifetime value is an approximation of this value. It describes the net present value of the profit that a customer ultimately generates during the entire customer relationship with a company. On the one hand, purchases made in the past are included, but an estimate of the future potential of a customer is also taken into account.
The calculation of the CLV is not standardised and can be done in different ways. In this article we distinguish between a simple and a more complex method of calculating the CLV.
In the simple calculation method, the CLV is calculated within a time period as the difference between the sales generated in the customer relationship and the investments made (see margin box). For this, an estimate must be made for the expected duration of the customer relationship.
The calculation method can be extended to several possibilities, for example by including discounting factors or the probability of a premature end of the customer relationship (“churn”). However, the strength of the customer lifetime value is its low complexity, which is largely abandoned as of the inclusion of discounting factors. The simpler calculation is therefore more suitable for roughly estimating the customer value.
We would like to illustrate the calculation here with an example.
A restaurant wants to calculate the CLV of a regular customer. For this purpose, it is assumed that he regularly visits the restaurant twice a week for a total of T weeks (must be estimated) and consumes around €20. The profit margin of the restaurant is 10%. With this information, the CLV of this customer can be calculated, whereby investments in the customer relationship must be deducted from the result at this point.
The calculated value can be used in several ways:
- The CLV can be used to estimate the total value of a company’s customer base, or
to determine expenditure ceilings for new customer acquisition, or
to create customer segments that are used to guide marketing efforts.
- Depending on the data available to a company, the CLV can be calculated for the total customer base, customer segments or at the level of individual customers. Depending on the calculation, different application options are available, whereby the calculated CLV at customer level offers the most options.
- In this case, the CLV can be used to identify particularly attractive customer segments. As in the application of the RFM model, the identification of up- and cross-selling potentials (see box) is thus possible. However, the identification of customer segments also helps to identify customers who are on the verge of breaking off their business relationship with the company (churn) and who could possibly be prevented from doing so with targeted measures.
- The CLV has limitations in some areas. Some factors that may influence the actual value of a customer, such as whether they recommend the company to others, are not included in the calculation. Estimating the likely duration of the customer relationship is another source of uncertainty. The CLV is hardly useful in practice if no exact transaction data is available, as in this case only guideline values can be used, which may not be suitable for the specific application.
The basically simple structure of the CLV can be extended by several components if the data is available and well structured (keyword data management). We will briefly introduce two of them:
- Machine Learning: From the data of previous purchases, models can be created to predict future customer behaviour, which can include more factors in the calculation than the simple formula application. Based on historical customer data, patterns are recognised which are used to estimate future potential. The estimation of customer duration can also be made data-based here. The target variable in the modelling can be the “number of purchases until the end of the customer relationship with a customer”.
- Cohort analysis: Cohort analysis is used to include typical patterns in customer behaviour over time in the calculation. This is only possible to a limited extent in the formula calculation, as the variables change over time. For example, the probability of a second purchase (if a first has already taken place) is 27% and the probability of a third purchase (if two purchases have already taken place) is 54%, and so on. The probability of a customer terminating the business relationship is therefore significantly higher for new customers than for customers who have already made several purchases. These changing parameters can be included in a cohort analysis.
Identifying similar patterns in customer behaviour is one of the biggest applications of CLV. By grouping customers into segments, they can be targeted. The CLV can then be used to determine spending caps for retaining these customers. In practice, this is the much more common application, as although the calculation at the level of individual customers is interesting, hardly any advertising investments are made individually at the customer level. The information thus obtained is therefore difficult to utilise.
Customer lifetime value is a data-based indicator of the net present value of a customer relationship. It can be used for segmentation of target groups, evaluation of investments, determination of an expenditure ceiling for new customer acquisition, as well as for the classification of customers into meaningful groups. The CLV thus offers an easy-to-calculate parameter with many different areas of application. In the long run, this results in an increase in return on investment (ROI), as marketing measures can be used more efficiently and customers with potential can be better recognised.