Customer lifetime value (CLV) — or customer profitability, customer profitability analysis, or simply “figuring out who makes you the most money” — is an essential metric that can help optimize your business so that it makes you the most possible money while wasting the least possible time and resources. CLV simply calculates the net profit attributed to your relationships with customers and is a common consideration within large corporations that is often underutilized, or given no consideration, in small businesses and startups.
Why should I care?
A straightforward example of the relevance of customer lifetime value is to examine customer acquisition in an e-commerce company. When acquiring customers through digital channels like Google Adwords, Facebook Ads or Youtube Ads, the most basic, common sense approach to optimizing your spending would be to invest more where it is cheaper to acquire new customers. A scenario for example:
Based on simple analysis, it appears obvious that it would be wisest to spend money on Google Adwords because that is where new customers can be acquired the most cheaply.
But, if we also consider customer lifetime value in context, the equation changes:
With additional data, it’s clear that the initially “obvious” answer (considering only customer acquisition cost) would not have lead to the most ideal decision. For the same $100, you would ultimately make $500 more by initially spending more money on the expensive Youtube ads when customer lifetime value is factored in.
This example is a simplification of the concept but is representative of real-world scenarios. For any business that sells a product to customers, understanding the short and long-term costs and benefits in acquiring and maintaining client relationships is significantly important.
Customer acquisition costs and customer lifetime values are not just pieces of information that are only relevant in retrospect; there are steps that can be taken to make better prospective decisions and help determine who your best customers are and will be.
Calculate Customer Lifetime Value
Many customer lifetime value formulas are based on getting the average customer lifetime value for all of your customers. This can be helpful, but it does nothing to tell you about the types of customers that you should be targeting and acquiring, and most formulas require 5+ years of data and knowledge about the customer life cycle to predict what the CLV is going to be with the highest degree of accuracy. However, there are methods that startups and small businesses can use to achieve the same goal. You need to determine the following for each of your current customers:
Once you have determined those variables, a general formula to calculate customer lifetime value (CLV) is:
Imagine you own an e-commerce company that launched ~3 years ago, you’ve had a number of sales but you’re not sure how the underlying metrics look, so you want to determine your customer lifetime value. Here’s how to go about doing so in a spreadsheet. For purposes of this exercise I will use Shopify as a data source:
- Transactions & Line Items: Export a full list of transactions and line items from Shopify, place this into one tab. Make sure that email addresses are included.
- Customers (with source): For each email address, you should have an attributed source. In early days this can be done manually. Once your orders grow, you need an attribution algorithm to determine the source of each of your customers.
Then you’ll have the data needed to determine customer lifetime value to date and see what areas are working best for your company.
The categorized information should provide the following example data:
The goal is to be able to segment your customers and identify which are the most valuable:
The chart above demonstrates that not all types of customers are equal. Frequently, it is not the customers that account for the majority of revenue that generate most of the profit because of high costs incurred. At the other extreme of the spectrum are the small, infrequent customers that never purchase enough to cover the cost of acquisition and administering accounts. This graph is generated when net margins are plotted against customers according to their revenue size. It is critically important to understand the overall profitability of tranches of customers and consider how they interact with your business, which relationships are the most beneficial, as well as how relationships can be more successfully (profitably) managed as a result.
In our example company (download an example spreadsheet template here), on a set marketing budget, we can see that certain customers aren’t worth the expense (e.g. those gained through events) and effort, and therefore should not be actively pursued because on average they don’t spend as much, while we should attempt to find more customers online. This information allows us to direct our limited/fixed marketing budget wisely.
It may seem controversial, but all customers are not equal. And all potential customers are not equally worth your time or investment. Considering customer lifetime value will allow you to identify the customers that should be eradicated and which types you should seek.
You can further refine this by identifying customer lifetime value for each of the following segments:
- By the source of the customer.
- By the customer’s location.
- (B2B) By the size of the customer.
- (B2C) By the customer’s wealth, a few ways to approximate this:
- By Zipcode
- By Bank Identification Number
- Etc… (The variables you could use to determine metrics are virtually unlimited).
Of course, how to actually get the data and specifics for customer lifetime value can vary from business to business depending on what you sell and how, how long customers are likely to remain, and how long you’ve been in business.
With many variables and the potential for great complexity, it’s most useful to start with the basics and begin with simple calculations around CLV and adjusting your decision making as your knowledge of your business and customers grows.
Once you’re doing this analysis consistently, it may become worth automating the process in order to reduce the amount of manual work required. Tool like Tableau and PowerBI can help with this, and we also offer packages for dashboard automation.
To get a free excel template for doing the analysis shown above, click here.