Lifetime value analysis calculates the expected value a customer/user will generate over the lifetime of their relationship with the company. So, if you want to know what Lifetime Value Analysis is and how a Data Science professional can help businesses using this technique, this article is for you. This article will take you through a complete introduction to Lifetime Value Analysis that you should know as a Data Science professional.
What is Lifetime Value Analysis & When to Use it?
Lifetime value analysis is a concept used by Data Science professionals in real-time business problems to calculate the expected value a customer will generate over the lifetime of their relationship with the company. It helps businesses understand the long-term revenue potential of individual customers and make informed decisions about customer acquisition, retention, and marketing strategies.
Let’s understand more about Lifetime Value Analysis and when to use it by taking an example of a real-time business problem.
Suppose a subscription-based streaming service wants to determine the value of each subscriber over its lifetime to gauge the profitability of its customer base.
By analyzing historical customer behaviour data, such as subscription period, average revenue per user, and customer churn, the company can estimate the expected income each customer will generate over the lifetime of their subscription.
This is where analyzing lifetime value can help the streaming service focus on its marketing efforts for acquiring high-value customers and implement retention strategies to maximize its overall revenue.
Some Data Science project ideas you can work on to understand more about Lifetime value analysis are:
Some Valuable Terms You Should Know for Lifetime Value Analysis
Below are some valuable concepts and terms that every data science professional should know before they start analyzing the lifetime value of their users:
- Customer Lifetime Value (CLTV): The total expected value a customer will generate for a business throughout their relationship.
- Average revenue per user (ARPU): Average amount of revenue generated by each customer during a given time period.
- Customer Acquisition Cost (CAC): Cost incurred by a business to acquire a new customer.
- Churn Rate: The rate at which customers stop using or subscribing to a product or service.
- Cohort Analysis: Analysis of groups of customers who share a common characteristic or experience within a specific time frame.
Process of Lifetime Value Analysis
It doesn’t matter what tool you use for analyzing lifetime value, but here’s the process that you should follow while analyzing the lifetime value of your customers as a Data Science professional:
- Define the scope: Identify the specific time period or duration over which you want to calculate customer lifetime value.
- Collect relevant data: Collect historical data on customer behaviour, including purchase history, subscription duration, revenue generated, and churn rate. (You can find a dataset for analyzing lifetime value here).
- Calculate customer lifetime: Determine the average or estimated length of the customer’s relationship with the company.
- Calculate customer revenue: Analyze customer transactions and calculate the total revenue generated by each customer over their lifetime.
- Calculate customer costs: Determine each customer’s acquisition cost, maintenance cost, and other associated costs.
- Calculate Customer Lifetime Value (CLTV): Apply the appropriate formula or methodology to calculate CLTV, which typically involves subtracting costs from revenues and accounting for the time value of money.
- Segment analysis: Perform cohort analysis or segment customers based on different characteristics (e.g. demographics, behaviour) to identify variations in CLTV.
- Interpret and use insights: Analyze CLTV results to identify high-value customer segments, assess the profitability of different customer acquisition channels, and create marketing and retention strategies.
Analyzing lifetime value is a concept used by Data Science professionals in real-time business problems to calculate the expected value a customer will generate over the lifetime of their relationship with the company. It helps businesses understand the long-term revenue potential of individual customers and make informed decisions about customer acquisition, retention, and marketing strategies. I hope you liked this article on what is Lifetime Value Analysis in Data Science. Feel free to ask valuable questions in the comments section below.