What is customer lifetime value (CLV)?
Definition: Customer Lifetime Value (CLV) is a metric used to estimate the total revenue or profit a business can expect from a single customer over the entire duration of their relationship.
It is important because it helps businesses understand the overall value of their customers, allocate marketing budget, determine the appropriate customer acquisition cost, and focus on retention.
Calculating customer lifetime value
CLV is calculated by multiplying the total customer value by the average customer lifetime.
CLV = Total Customer Value x Average Customer Lifetime
The calculation can be done using historical data, predictive modeling, or a traditional model.
Historical CLV uses past data to calculate CLV.
CLV = (Average Purchase Value) x (Average Purchase Frequency) x (Average Customer Lifespan)
- Average Purchase Value (APV): the total revenue divided by the number of purchases.
- Average Purchase Frequency (APF): the total number of purchases divided by the number of unique customers.
- Average Customer Lifespan (ACL): the average length of time a customer remains active with the business.
Traditional CLV (or Cohort-based CLV) is calculated using cohort analysis, which groups customers based on the time they made their first purchase.
CLV = (Average Revenue per Customer per Period) x (Retention Rate / (1 + Discount Rate - Retention Rate))
- Average Revenue per Customer per Period: the average revenue generated by a customer in a specific time period.
- Retention Rate: the percentage of customers who continue to make purchases in subsequent periods.
- Discount Rate: the rate used to discount future cash flows to present value, accounting for the time value of money.
Predictive modeling uses advanced statistical models and machine learning techniques to predict future customer behavior based on historical data.
This method accounts for various factors, such as customer demographics, purchase history, and engagement patterns, and provides a more accurate estimation of CLV.
What method you should use depends on the available data, industry, and your business needs.