You may be familiar with the Pareto principle, which states that 80% of turnover comes through 20% of customers.
Identifying high-value customers allows companies to find more of that type of customers, and to ensure that not too much is invested in customers with less potential.
How do you calculate the Customer Lifetime Value?
The calculation of Customer Lifetime Value may vary depending on the amount of available data. Often we look at the turnover during a certain period, but you can also calculate it perfectly on added value (margin).
In addition, it is crucial to take a time period that contains sufficient data, so that seasonality and other factors cannot give a distorted picture. Depending on the sector, we recommend looking back for one year or longer to calculate the Customer Lifetime Value.
Measuring CLV with Google Analytics
Google Analytics is a very powerful tool for measuring and analysing visitor behaviour on your platforms. With a few minor adjustments to the tracking setup you can use Google Analytics to calculate your Customer Lifetime Value.
The first step is to correctly identify unique users. In Google Analytics this can be done on the basis of the client ID or a user ID that is generated via the back-end of the web platform. An additional option is to import a unique ID from your CRM system
How do you use the Customer Lifetime Value?
The next step is to determine how you want to use the Customer Lifetime Value. Are you going to rely solely on historical data or will you predict the future value based on a statistical model?
Finally, you must determine how the value is calculated. In the case of a webshop, the revenue from e-commerce transactions can be used. In the case of a lead generation website, you should rather look at the conversion rate of the number of leads to paying customers and what the value of a paying customer is.
Which model do you use for the calculation?
There is no one-size-fits-all model for calculating the Customer Lifetime Value. That will differ per type of company. In the case of companies that generate their turnover on a contract or subscription basis (telecom companies, insurance companies, etc.), the calculation is generally easier to make compared to companies that focus on retail.
We always advise our clients to start on a small scale and then refine the CLV model in more depth. So be sure to look at what data is currently available within the company and think carefully about how the data set can be expanded later to work even more effectively in the future.
Use CLV for segmentation and as predictive metric
Once we have calculated the historical CLV, we can map customer value with behavioral variables and dimensions or RFM models. From this, different segments are distilled for targeting, to maximise customer value and to detect look-like profiles that initiate new high-value acquisition.
You can also use CLV to make predictions for future revenue and margin based on current acquisitions. An additional variable that can strongly refine these insights is the historical (and current) churn ratio.
Personalisation & bid management
Of course we must reach our most valuable customers in the best possible way with the right proposition.
