Customer Lifetime Value
Companies typically invest a lot of money in loyalty programs. After all, returning customers generate net more profit than new customers. To be successful in this, it's important to know what your customers are worth. The Customer Lifetime Value model (CLV) helps you gain this insight.
In a previous blog, I discussed the RFM model, which is based on historical customer activity. With the CLV model, it's possible to predict future customer activity. Customer Lifetime Value is the value that a customer brings to your business over the duration of their relationship with you. In other words, with CLV, you quantify (in euros) what a customer will bring you during the entire duration of the customer relationship.
CLV encourages companies to shift their focus from short-term profit to sustainable and valuable customer relationships. When you know what a customer will bring you, you also know how much you want to invest in that customer. This way, as a company, you can better justify budget choices for acquisition and/or retention.
With CLV in hand, you also know how to invest in certain customers. You'll know whether it's wise to offer a certain customer extra discounts or whether it's wiser to boost customer service.
The 80/20 rule
For many companies, 80 percent of their future revenue comes from just 20 percent of existing customers. Investing in retaining that 20 percent is therefore extra relevant. But for that, you need to know exactly which customers are in that 20 percent and what drives them. I will delve deeper into this in my second blog about CLV.
The CLV model comes with different levels of complexity and accuracy. Many marketers who are new to CLV calculate it manually using spreadsheets, using a portion of the available customer data. This gives them a rough estimate of historical customer value.
The simplest CLV formula is a multiplication of the average purchase frequency by the average order value. The average purchase frequency is the number of orders divided by the number of unique customers per year. The average order value is the total annual revenue divided by the number of orders in the past year.
The traditional method
A more advanced calculation is this: CLV = WM x R / (1 + D – R). Are you still with me? WM stands for profit margin per customer lifetime. R is retention rate and D is the discount rate, a figure used to convert future revenues into present value.
For a proper use of the CLV model, it's advisable to tailor it to your own sales cycle (short versus long term) and your purchase frequency (one-time versus repeat purchases). This allows the model to better align with your customer data, increasing its predictive value.
You can imagine that this calculation is less suitable for manual spreadsheet calculations. Fortunately, you can use machine learning for this. In more advanced CDPs, you'll find functionality that automatically calculates CLV for you. Additionally, you'll obtain the probability score of a new purchase and the predicted number of purchases in the desired forecasting period.
And here's the kicker: because you've captured CLV in the CDP, you can directly combine it with all the other customer attributes you've already built up. For example, with preferences and interests. This makes CLV a huge enrichment of your customer data, which you can also use directly.
But how?
That's what my next CLV blog is about.
