Customer Lifetime Value (CLV) is a powerful metric that can help
businesses measure the total value that a customer brings to the company over
their entire lifetime. Understanding CLV is essential for companies that want to
optimize their customer acquisition and retention strategies, increase revenue,
and improve customer satisfaction. In this article, we’ll take a closer look at CLV,
why it’s important, how to calculate it, and how to predict it for a new customer.
CLV help you identify their most valuable customers!
What is Customer Lifetime Value?
Customer Lifetime Value (CLV) is a measure of the total value that a customer brings to a business over their entire lifetime.
CLV takes into account not only the revenue generated by the customer but also the costs associated with acquiring and retaining them.
CLV provides insights into the long-term profitability of a customer and helps businesses make data-driven decisions about marketing, sales, and customer support.
Why is Customer Lifetime Value Important?
Customer Lifetime Value is important for several reasons.
First, it helps businesses identify their most valuable customers, those who are likely to generate the most revenue over time.
By focusing on these high-value customers, businesses can improve customer satisfaction, increase revenue, and maximize profitability.
Second, CLV can help businesses make data-driven decisions about customer acquisition and retention strategies.
For example, if the CLV of a customer is high, it might be worth investing more in customer acquisition efforts to attract more customers like them.
Third, CLV helps businesses understand the true cost of acquiring and retaining customers.
By understanding the lifetime value of a customer, businesses can make informed decisions about
how much to spend on customer acquisition and retention efforts.
How to Calculate Customer Lifetime Value for Established Customers?
There are several methods for calculating CLV for old customers who have sufficient historical purchase data, but the most common method is to use the following formula:
CLV = (Average Value of a Sale) x (Number of Repeat Transactions) x (Average Customer Lifespan)
Here’s a breakdown of each component of the formula:
Average Value of a Sale: This is the average amount of revenue generated per sale.
Number of Repeat Transactions: This is the average number of times a customer makes a purchase during their lifetime.
Average Customer Lifespan: This is the average length of time that a customer continues to do business with a company.
Let’s say, for example, that a customer spends an average of $100 per purchase,
makes four purchases per year, and continues to do business with a company for five years.
Using the CLV formula, we can calculate the customer’s lifetime value as follows:
CLV = $100 x 4 x 5 = $2,000
This means that the customer is expected to generate a total of $2,000 in revenue over their lifetime.
Of course, this is just a simplified example, and in reality, calculating CLV is a more complex process that takes into
account factors such as customer acquisition costs, retention costs, and customer churn rates.
How to Predict Customer Lifetime Value for Prospective Customers?
There are several ways or methods to predict new customer lifetime value who have no purchase data at all. Here are some of the most common methods:
1. Aggregate Model
The Aggregate Model is probably the most common method out there.
It has been around the longest, and it is the most straightforward way to calculate CLV.
The aggregate model uses a constant spend rate and churn for all clients.
In this method, we have a single CLV, or in other words, a single group of customers rather than individuals.
Since this model creates one single CLV predicted value, there might be some drawbacks to using it.
So we have a better model to segement customers and predict the CLV seperatly considering the drawbacks of Aggregate Model.
2. Cohort Model
his method groups customers based on a specific characteristic or behavior, such as purchase frequency,
and then analyzes the lifetime value of each group over time.
This can help identify patterns and trends that can be used to predict future behavior.
3. FRM Analysis
This method analyzes a customer’s recency, frequency, and monetary value of purchases to predict their lifetime value.
By identifying customers who have made recent and frequent purchases and have spent a significant amount of money,
companies can predict their future behavior and estimate their lifetime value.
4. Machine Learning Model
Machine Learning (ML) is an essential Artificial Intelligence (AI) tool that can help predict CLV with great accuracy.
This is because the ML models use algorithms that find patterns in the data you’ve collected to more accurately forecast future customer behaviors –
which has enormous benefits, as we mentioned earlier in this article.
As part of the machine learning process, we will also estimate the Recency, Frequency, and Monetary Value (RFM) for the transactions.
This helps give you a clear overview of each user’s average purchase amount, lifetime duration, and their frequency of purchase.
For this type of model to work, you need to have previous data and prepare it so the algorithm can do its job.
This process involves removing duplicates and getting rid of empty fields or data that are incorrectly formatted.
An interesting CLV project
An online retailer that sells a variety of products, including clothing, electronics, and home goods. The company had been experiencing stagnant revenue growth and was looking for ways to increase sales. They decided to focus on improving customer retention by using customer lifetime value to identify their most valuable customers and target them with personalized marketing campaigns.
The first step in the project was to calculate the customer lifetime value for each customer. This was done by analyzing the customer’s purchase history, including the frequency, recency, and monetary value of their purchases. Using this information, the team was able to estimate the expected future value of each customer.
Next, the team used the RFM data to segment their customers into different groups based on their value. They identified a group of high-value customers who had a high lifetime value and were most likely to make repeat purchases. They then created targeted marketing campaigns to reach these customers, including personalized emails and promotions that were tailored to their preferences and purchase history. They sent personalized emails to each customer with promotions and discounts tailored to their specific interests and purchase history. For example, customers who had purchased clothing in the past would receive promotions for new clothing items, while customers who had purchased electronics would receive promotions for new tech products.
Also, there are many new custmomers with limited historical data, but has demographic data or some personal info data, we developed a regression model to predict
the customer lifetime value for those new customers and prioritize them by the predicted number, which will help us identify the most valuable prospective customers.
The result of the project was a significant increase in revenue. By focusing on high-value customers and targeting them with personalized marketing campaigns, the company was able to increase their repeat purchase rate and drive more revenue. In addition, by improving customer retention, the company was able to reduce the cost of customer acquisition and improve their overall profitability.
In conclusion, using customer lifetime value to identify high-value customers and target them with personalized marketing campaigns can be an effective way to lift revenue and improve customer retention. By understanding the lifetime value of each customer, companies can focus their marketing efforts on the customers who are most likely to make repeat purchases and have the greatest potential to drive revenue growth.