Mark Kok
Companies often invest heavily in loyalty programs because returning customers generate more net profit than new ones. To get the most out of these efforts, it’s essential to know what your customers are worth. That’s where the Customer Lifetime Value (CLV) model comes in.
CLV: understand your most valuable customers
In a previous blog, we explored the RFM model, which relies on historical customer activity. CLV, on the other hand, helps you predict future customer behavior. Simply put, Customer Lifetime Value measures the total value a customer brings to your business over the duration of their relationship. With CLV, you can quantify in euros what each customer is likely to contribute during the entirety of their customer journey.
Understanding CLV encourages companies to shift focus from short-term profit to sustainable, valuable relationships. When you know a customer’s expected lifetime value, you also know how much it makes sense to invest in them. This insight helps justify budget decisions for acquisition, retention, or personalized campaigns.
Invest in the right customers
With CLV, you can also determine how to invest in individual customers. Should a particular customer receive extra discounts, or is it wiser to enhance their service experience? Targeted investments like these help strengthen loyalty where it matters most.
The 80/20 rule is particularly relevant here: for many businesses, 80% of future revenue comes from just 20% of existing customers. Identifying these top-performing customers and understanding what motivates them is key. This insight allows your organization to allocate resources more effectively. In my next CLV blog, I’ll go deeper into segmenting these high-value customers.
Calculating CLV: from basic to advanced
The complexity of CLV calculations varies depending on your goals. Beginners often calculate CLV manually in spreadsheets, using a portion of their customer data to estimate historical value.
The simplest formula is:
CLV = Average purchase frequency x Average order value
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Average purchase frequency is calculated by dividing the number of orders by the number of unique customers per year.
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Average order value is the total annual revenue divided by the number of orders.
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 accurate results, tailor CLV calculations to your sales cycle (short vs long term) and purchase frequency (one-time vs repeat purchases). This ensures the model aligns with your customer data and improves predictive accuracy.
Leveraging technology for CLV
Manual calculations can be cumbersome, but modern Customer Data Platforms (CDPs) simplify the process. Many CDPs automatically calculate CLV, along with predictive metrics such as the probability of a future purchase and the expected number of transactions within a given period.
Moreover, capturing CLV in your CDP allows you to combine it with other customer attributes like preferences, interests, and engagement history. This enriches your customer profiles and enables data-driven, personalized marketing strategies immediately.
What’s next?
In the next blog, I’ll dive into how to act on CLV insights. You’ll learn how to segment your customers, prioritize high-value accounts, and tailor your marketing and retention efforts for maximum impact.
Ready to make the most of your CLV data? Contact us today to explore how your organization can integrate CLV into a CDP and start making smarter, more profitable decisions.
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