How valuable are my customers?
Customer Lifetime Value (CLV/CLTV) is a business management indicator and describes the value that a customer represents for a company over the entire period of his or her customer base. With the help of CLV, companies are able to better assess the composition of their customer segments as well as individual customers and estimate their past and future purchasing behavior.
What is the Customer Lifetime Value Analysis?
Customer lifetime value analysis is therefore the process of analyzing how valuable a customer is and will be to the company over the lifetime of the business relationship. Rather than looking solely at the profitability of transactions, in this process companies try to determine how long a customer is likely to remain a customer. In addition, a forecast is made as to how often he or she will buy something during this period and consequently how valuable he or she is within the given period.
For some companies, a customer can be profitable from the very first purchase. For others, this only happens after several transactions. It is therefore important to know which phase customers are in.
When do I use Customer Lifetime Value Analysis?
The analysis and evaluation of customer lifetime value (CLV) serves to recognize and predict the value of each individual customer for the company. The CLV enables companies to assign a certain value to all customers so that they can recognize which ones are the most valuable and therefore the most important. This insight can enable companies to focus their marketing efforts on the customers who are most likely to make a purchase.
How often companies should engage in customer lifetime value analysis depends on the industry in question. In industries such as telecommunications, the lifetime of a customer relationship may only be a few years or even months (often the minimum term of the contract). However, companies should carry out a customer lifetime value analysis at least once a year to determine whether the duration of the relationship, the purchasing behavior and/or the profitability of customers have changed.
How do you determine customer value?
The customer lifetime value is made up of historical sales and the forecast customer value. Past sales are taken into account by adding up all sales since the initial order with a time-related weighting, i.e. the further back in time a sale is, the lower the weighting. Subsequently, predictive modeling approaches are used to forecast the future sales of each customer.
Multivariate forecasting methods are used to predict customer value, in which historical customer characteristics such as socio-demographic data, order values, activities, order frequency, product preferences and returns are taken into account in the model. The aim of this analytical approach is to provide the most accurate and meaningful sales forecast possible for each individual customer, on the basis of which concrete decisions can be derived.
Which findings of the Customer Lifetime Value analysis are promoted?
The Customer Lifetime Value analysis helps to answer questions such as:
- How much should we spend to acquire a customer?
- What should we invest in order to win back or retain a customer?
- How long do our customers stay with us?
- Are our offers suitable for our best customers?
- How long do our customer relationships last on average?
- What is the average value of our customer relationships?
According to Gartner, 80% of a company's future profits are generated by only 20% of existing customers. It is therefore worth investing in the relationship with existing customers.
According to a rule of thumb, it is assumed that acquiring a new customer costs five times as much as retaining an existing customer. In this respect too, it is worth analyzing and aiming for customer segmentation based on customer lifetime value.
How do I use the Customer Lifetime Value Analysis?
Various factors must be taken into account when calculating or analyzing the CLV. These include
- Duration of the business relationship with a customer (customer lifespan)
- Customer retention rate
- Customer churn rate
- Average profit margins (per customer)
In addition, a distinction is made between a historical value, i.e. a past CLV, and an expected value, also known as a predictive CLV. In principle, the historical CLV is the sum of all of a customer's previous gross profits in relation to the individual costs. Predictive CLV is calculated on the basis of past transactions and customer actions. Algorithms are used to attempt to make a more accurate prediction of the value that a customer can generate.
Once companies know the CLV of a customer, they can carry out further analysis and procedures (such as a regression analysis). For example, to determine which factors influence the duration of the customer relationship or the overall value of this relationship. The aim here is to identify measures to increase both the duration of the relationship and its value. If the CLV of a customer is very high, this means that higher budgets can be released for customer relationship management. Conversely, a lower CLV also means lower financial expenditure for companies.
Nowadays, companies are able to merge different data sets for each customer to get a more holistic picture. In the past, companies with different product lines stored their customer information separately, so a customer could appear three or four times on different systems within the company. Based on the different products or services he or she has purchased. Today, big data analytics and increased storage and processing capabilities make it possible to collect all this disparate data in one place to profile customers more effectively and make better decisions based on that information.
Tips and pitfalls
The biggest challenge in customer lifetime value analytics is to find the right formula for the respective company. Companies also need to understand their customer base properly. For many, this can be a challenge if internal systems do not record how many of the company's products a customer uses or how often a transaction occurs.
When predicting future CLV, companies must bear in mind that these calculations are partly based on estimates and assumptions and therefore cannot be guaranteed to be 100% accurate. Furthermore, even a high CLV cannot guarantee that this customer relationship will remain constant to the same extent. Depending on the service and product segment, the specific issues and industry knowledge must be taken into account.
