The Ultimate Guide to Predicting Your Customer’s Lifetime Value.
Learn advanced Customer Lifetime Value (CLV) strategies for predicting customer value and using data-driven insights to optimize business strategies.
Welcome to the first lesson of the fifth chapter! In this chapter we’ll learn to predict Customer Lifetime Value (CLV) and quarterly revenue of a business.
Customer lifetime value (CLV) is a measure of the total amount of money that a customer is expected to spend on a business’s products or services throughout their relationship with the business. CLV is an important metric for businesses, as it helps businesses to understand the value of their customer base and to make decisions about how to allocate marketing resources and other investments.
Businesses that focus on a long-term relationship with their customers and sell products or services that require repeat purchases typically get the most value out of CLV analysis. For example, a subscription-based company like Netflix can use CLV analysis to predict how much revenue they will generate from a customer over their lifetime and how much they should be willing to spend to acquire that customer. Similarly, an e-commerce business can use CLV analysis to determine which customers are most valuable to them and prioritize their retention efforts accordingly.
Businesses typically use a combination of historical data and statistical modeling techniques to calculate CLV. This involves analyzing factors such as the amount of time a customer interacted with the business, the frequency of their purchases, and the average amount of money that a customer spends per transaction. By combining these factors, businesses can estimate the total amount of money a customer is expected to spend over their lifetime and better allocate their marketing budget.
Customer lifetime value calculation
Customer lifetime value (CLV) is a measure of the total value that a customer will bring to a business over their lifetime as a customer. There are several approaches to calculating CLV, including:
- The basic approach: This approach estimates CLV by multiplying the average purchase value by the number of purchases per year and the average customer lifespan.
- The cohort approach: This approach estimates CLV by analyzing data from a specific group of customers (called a cohort) and projecting their future value based on
past purchase behavior. - The discount cash flow approach: This approach estimates CLV by forecasting future cash flows and discounting them back to the present using a discount rate.
- The machine learning approach: This approach is also known as the Predictive method. One common way for building a predictive CLV model is through a linear regression model.
Other approaches include decision trees, random forests, or even neural networks.
In this lesson, we’ll discuss the basic approach and the machine learning approach.
Business context
CLV calculation method depends on the business context. In relevance to CLV calculation, we can divide the business context mostly into two parts.
- Contractual: Customers are bound by a contract with the business. It’s mostly monthly or yearly subscription basis.
- Non-contractual: Customers are not bound to the business. They can interact whenever they want without any obligation.
Benefits
There are several benefits of conducting customer lifetime value (CLV) analysis, including (but not limited to):
- Customer segmentation to identify high-value customers.
- Develop targeted marketing strategies.
- Improve customer relations and customer retention.
- Budget customer acquisition costs.
Usually, there is an upfront cost to acquire new customers. That makes customer retention cheaper than acquisition. Because of that, successful marketers focus more on retaining existing customers than on acquiring new customers. Customer lifetime value (CLV) is a major factor when setting up strategies for customer satisfaction and retention.
In the diagram, we see the effect of the acquisition cost of a customer. It takes time to bring that customer’s net present value to a positive.
Overall, CLV is a valuable metric that can help businesses make better decisions about how to allocate their resources and invest in their customer relationships. By understanding the lifetime value of their customers, businesses can focus on retaining and nurturing their most valuable customers, which can lead to increased profits and long-term success.
What’s next?
- Lesson 5.2: Data Exploration — Monthly Revenue Analysis: We’ll analyze monthly revenue trends, identify seasonality, and uncover patterns in customer purchasing behavior over time.
- Lesson 5.3: Data Exploration — Customer Revenue Analysis: This lesson will focus on individual customer revenue, segmenting customers based on their purchase behavior, and understanding the revenue contribution of different customer segments.
- Lesson 5.4: Predict Quarterly Sales with RFM Model: We’ll introduce the RFM (Recency, Frequency, Monetary) model to predict future sales and revenue. This will include building and validating a predictive model to forecast quarterly sales.
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