CLV, CLTV or LTV is one of the most important metrics for growing companies which can help you understand a reasonable cost of customer acquisition. Roughly defined, customer lifetime value (CLTV) indicates the total revenue minus rlated cost a business can expect to generate from a customer in the course of business relationship. CLTV compares customer’s revenue value to the company’s predicted customer lifespan and allows businesses to identify significant customer segments that are the most valuable to them. Whether you have a traditional offline business, a fully digital business like SaaS or some combination of the two, calculating your CLV can help you make smarter decisions about your marketing and sales budgets and bring better returns on your investments.
In this article, we’ll briefly cover how to calculate CLTV and how to get started with CLTV analysis.
There is no single CLTV formula – they can differ from analyst to analyst and business to business. There are different approaches to measuring customer lifetime value – historical, predictive, and traditional and the choice of methods will depend on your business and on your resources. We will review 6 different methods to help you get a better understanding of different approaches.
There are two approaches to calculating CLV:
Historical methods are simple but, unfortunately, they can’t be used for predictions because they don’t consider changes in customer behavior that can affect the outcome.
Method 1 determines average revenue per user (ARPU) which is calculated by dividing the total revenue for a specific period (TR) by the number of customers (CQ).
ARPU = TR / CQ
Using this number, we can calculate the ARPU for one year.
Method 2 is based on cohort analysis (a cohort is a group of customers with similar characteristics). Using cohort method, we calculate ARPU for a specific cohort.
Method 3 is based on LTV report in Google Analytics which helps businesses measure the value of customers by engagement and calculates revenue metrics during 90-day acquisition period. Google Analytics LTV report can be a good starting point if you need only high-level insights, for example, data about the revenue per user from a particular short-term campaign, but you can’t rely on it if you need a sophisticated analysis for SaaS.
Predictive methods will require more efforts and in turn are more rewarding in terms of results accuracy.
Method 4 uses a rather complicated formula, but it is accurate. You need to calculate several metrics such as
After that, you need to multiply these metrics and divide the result by the number of clients for the specific period.
CLV = (T x AOV x AGM x ALT) / number of customers for the specific period
This method is better than a historical model but still we can only guess the customer lifespan analyzing monthly data so such predictions may be misleading.
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Method 5 is based on the formula that includes such metrics as average gross margin per lifespan of a single customer (GLM), retention rate per month (R), and discount rate per month (D).
CLV = GLM x (R / (1 + D – R))
Using this CLV formula, we can take into account all possible changes of revenue during a specific period of time.
Method 6 provides a more accurate view on CLV than the previous ones. The new way to calculate CLV is based on discounted cash flow analysis that takes into account all risks associated with future revenue streams. To model the simple version of this new CLV, you’ll need such data:
The revenue for each cohort of customers evolves over time due to a churn rate which grows exponentially. We can calculate the average customer lifetime (ALT) using a simple formula:
ALT = 1 / Churn Rate
To get customer lifetime value (CLV), we can simply multiply the average customer lifetime by the average gross profit a company makes from each customer every year:
CLV = ALT x Average Gross Profit per Account
Filling this out with formulae for gross profit per account and average lifetime value, we get the equation:
CLV = (ARPA x Gross Margin %) / Churn Rate
In this formula, we used the rate at which customers churn, but in fact, everything is more complicated, and we need to look at how revenue behaves when a company has churn. As we are modeling revenues as that will occur in the far future, we’ll need to apply a discount rate to account for the risks (market changes, new competition) and time value of money. Typically, we should apply 10% annual discount to future cash flows.
CLV calculated in this way can help businesses better understand and manage their future revenue streams. Besides, it more accurately reflects what investors would be willing to pay today for the future flow of cash.
Not all customers are created equal. For most businesses, only a small cohort of customers (about 20%) can generate 80% of revenue. Assessing CLV can help businesses dedicate more resources to acquire and retain high-value customers and increase the revenue. Except from identifying the highest-value customer segments, businesses can also use CLV to create cohort of users for testing purposes, to build matrices of purchase behavior by combining product purchase and customer segment data with CLV, etc. Keep in mind that you will need to perform a thorough CLV analysis to identify these data points.
As you see, there are multiple approaches to CLV calculation depending on the available data and the nature of your business. In very rare cases you will benefit from calculating CLV alone, using a CLV calculator. Usually it’s the part of holistic analysis or business model development. So, you need to make sure that all the calculations you are doing are aligned, easy to understand and present. That’s why you will benefit from using Lean Case, a toolset that simplifies the creation of business models and eases the presentation to the investors.
CEO and Founder Lean-Case - Eckhard is a Serial Entrepreneur co-founding cyber-security startup accells acquired by Ping Identity and m-payment startup paybox acquired by Sybase/SAP. As a Business Angel, VC Partner and Investment Advisor, he has realized that turning business models into numbers is a major challenge and must professionalize.
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