Quantitative Marketing

Calculating Lifetime Value (LTV)

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In recent years, Customer Lifetime Value (LTV) has been described in an array of guiding articles in general terms, calculating formulas, and often accompanied by a list of suggestions for improvement. More useful perhaps, is a reminder of specifically what LTV can do when calculated correctly, why it sometimes fails to provide useful information, and, finally, why it continues to hold value.


What is Customer Lifetime Value?

Why is Customer Lifetime Value, or LTV, a key business metric? For one, customer LTV represents how much the average customer spends during his or her entire relationship with a company–whether three years or (in cases of excellent brand loyalty) their literal lifetime. In other words, the LTV is the complete profit margin the company expects to achieve during the entire business-customer relationship. 

Knowing the future value of a company’s customers–individual, by segment, and in total–informs the leadership of both potential revenue and a limit.

Customer lifetime value (CLV, CLTV, or LTV) can be used as part of any business model focused on future profitability predictions–whether in judging investments, product lines, marketing, or even customer care costs.  



Why is Customer Lifetime Value Critical? 


The customer LTV functions as the maximum limit of what a company should be willing to pay to acquire a new customer. Spending beyond this limit offers the company no economic benefit.   The Customer Acquisition Cost (CAC) is the total cost of acquiring a new customer–in other words, all company costs until a first purchase is made and the prospect becomes a customer.  If the CAC is $500, and the LTV is $350, the business would lose money on the customer.  In this way, the CAC is an indicator of the lowest LTV needed to reach profitability. 

While the LTV is a key part of balancing the basic profit equation, it can also calculate comparative LTV for customers in different channels or even customer segments. A company might identify different marketing techniques used in higher-profit channels, and replicate them across other, lower-profit ones.  It could also validate different maintenance and support costs or even product offerings by channel or profit tier.  

Knowing the value of a customer is an asset.  It is a reference point that all other spending, decisions, and income can be measured against. Customer LTV can be monitored to measure the impact of ongoing marketing, customer engagement, promotions, communication.

Getting this number right (and maintaining it), and plugging it into any Business Intelligence or predictive models is essential to running a data-driven business. 


Customer Lifetime Value Calculations                          

Customer LTV formulae and calculations vary from the vastly oversimplified to a loaded formula value that strives to account for all cost variables entering the customer’s domain, potentially impacting a customer’s future value.  The simplest model uses purchase revenue and the most recent year, while the more complex includes live individual totals and counts, tempered with churn rate, discount rates, multi-margins, acquisition/marketing costs, retention costs, risks, all over a multi-period calculation.


Basic Customer LTV Formula

At its simplest, Customer Lifetime Value is customer profit over time:

(Average Annual Customer Profit) x (Average Duration of Customer Retention)

However, in order to calculate even this formula, a basic component breakdown is below:

(Average Value of Sale) x (Number of Transactions) x (Gross Margin) x (Retention Time Period) 

  • Average Value of Sale = (Total Annual Sales Revenue) / (Total Annual Number of Orders)
  • Average Number of Transactions = (Total Annual Number of Orders) / (Total Annual Number of Unique Customers)
  • Gross Margin = (Total Sales Revenue – Cost of Good Sold, or COGS) / (Total Sales Revenue)
  • Retention Time Period = (Average of all customers lifespans as a customer)

When using this formula, it is critical to remember that “Average” is neither a guess, range, nor assumption; instead, it is the result of a formula populated with real business data pulled from a system of record, such as the Enterprise Accounting System (EAS) or similar system, the Customer Data Platform (CDP) or Customer Relationship Management (CRM), and, in some cases, the Digital Advertising Platform.  

For Avg Value of Sale, the Total Annual Sales Revenue and the Total Annual Number of Orders should be based on the financial system record for a recent 12-month window.  If, however, one wanted to determine the LTV of a particular segment, the Total Annual Revenue and Number of Orders by targeted segment would be used for the calculation, and would require the financial system tag revenue and order with a field (label or metadata) indicating segment for the data comprising revenue and orders

The analyst would similarly compute Avg Number of Transactions:  Over the same 12-month period, the Total Annual Number of Orders is divided by the Total Annual Number of Unique Customers, data again coming from the financial system of record, and potentially the CDP/CRM depending on data structures and storage rules. Again, if the goal was for a subset of customers, only the values for the targeted customers would be compiled.

Gross Margin is typically used in this model and calculates the profit the product makes beyond production costs (COGS).  Operating Margin and Net Margin adds administrative costs, and taxes plus any other expenses, respectively.  Models not including margin and used as a comparison to other revenue can easily end up being compared to profit-only figures. Leads and Managers reviewing final LTV reports must ensure that the revenue/profit calculations are clearly indicated before making comparisons or decisions in planning and operations.

Retention Time Period or Customer Lifetime Period

Approach 1: With existing long-term customer data. Calculated by adding all customer “lifespans” of business with the company, then dividing by the total # of customers.


Total Lifespans / Total # Customers = Avg Lifespans 


Ex. Company has been in business for 4 years, with a total # of unique served customers of 750 in the CDP.  Also, in the CDP are records of the relationship length with each customer.  The total # of years tenure, when added together, is 1474 years (customer tenure ranged from 3.8 years to .08 years).

1474/750=1.96, or 2 years on average

Approach 2: With limited customer data. Another way to calculate Retention Time Period is using the Churn Rate.  To determine Churn Rate, track how many customers at the beginning and end of the time period (typically 1 year).  

Churn Rate = (# of Customers at End of Time Period – # of Customers at Beginning of Time Period) / # of Customers at Beginning of Time Period.

(80 Customers at End) – (100 Customers at Beginning) =  -20/100 or .2 or 20% Churn Rate

Retention Rate (80 Customers at End) / (100 Customers at Beginning) = 80/200 or .8 or 80%

Customer Lifetime Period = 1/Churn Rate

Customer Lifetime Period = 1/.2 or 5 years


Customer Lifetime Value Examples

E-commerce Specialty Cleaner Customer LTV

SwiftClean is a natural all-purpose cleanser popularized in social media campaigns 5 years ago. With an average sale of $38.95 with a 24% profit margin.  The average customer purchases 3.7 times per year over 3 years.

38.95 (avg sale) x 3.7 (transactions) x 3 (period) = $432.35 x .24 (margin) = $103.76 LTV

The CAC is relatively low due to minimal marketing costs = $15.73

(Note: Most examples of LTV calculations do not differentiate between gross, net, or operating margins. To gain an accurate LTV, comparisons should define the margin type, and ensure comparisons of value use the same margin type.)


Subscription or SaaS Customer LTV

In some cases, however, the CAC may be higher than the return from the first (several) customer transactions, but still within the limit of the LTV.  For example, a subscription service like Netflix may offer a low monthly rate of around $15, and marketing costs and promotions expenses are considerable, driving CAC to $200.  From their LTV (e.g., $250), Netflix knows the future value of the customer exceeds $200 and makes the transaction worthwhile. 


Mistakes, Misuse, and Limitations of LTV 

While inaccurate data will always be the easiest error to make when calculating LTV, several mistakes are common in calculation or using the data point beyond its applicability. 

  • Conflating customer profitability (CP) with LTV, the past profitability of the customer, which is less useful in forward-looking decisions by the business although easier to quantify.
  • Applying revenue instead of profits in the formula.  If the Lifetime Value of 10 purchases of $25 = $250.  Customer Acquisition Cost = $175, so Lifetime Value > Customer Acquisition Cost.  But if the margin is 50%, the Lifetime Value is $125 and losing on every new customer acquired. 
  • Setting unrealistic Lifetime (LT) when new businesses do not have enough history for an average customer LT.  Or a company with a fluid product line that precludes a singular history to average among customers makes a lofty prediction. 
  • NPV vs nominal prediction to determine future profit. Using the net present value (NPV) of future profit accounts for the diminished future value of money, and requires factoring in a discount rate.  Because such calculations are necessarily sophisticated, most organizations calculate LTV using nominal, non-discounted values.  These predictions naturally skew high and increase the farther out the prediction travels.
  • LTV is not a static model.  As the output of an equation, the LTV changes if any inputs change (e.g., custom communication, promotions could change retention rate, etc.). As products, channels, and strategy change, so too your customer profiles.  If everything in the business environment seems to be changing, the LTV should probably be recalculated often. 
  • Using one LTV for disparate customer segments.  Creating an LTV across all customers provides a practical value, but segmenting widely varying customers provides more accuracy in the output.  A true mathematical average will produce an average purchase amount, frequency, and duration of relationship for even wildly varying customers.  But the smaller the data pool and greater the variance, the less helpful the average. Conversely, segmentation of customer LTV is one of the benefits of calculating LTV.  
  • Overvaluing current customers and undervaluing potential customers. By the very marketing techniques that creators of LTV practice each day, low-value customers can be turned into high-value customers by marketing effectively.  Since there are many more “middle-value” customers than high-value, prioritizing the smaller segment at their expense is counter-productive. 

Many of the errors or limitations above result from failure to use actual business data when calculating the customer LTV. Without the data, the calculation no longer has significant value beyond aspirational guessing. With Arcalea's Galileo attribution platform, marketers can calculate CAC and LTV by channel, initiative, and marketing asset. As a result, low-performing resources can be eliminated or reallocated to higher performing ones.

Companies that are diligent with infrastructure, maintaining platforms and other data sources as a singular part of the business model, are more likely to make data-driven decisions and produce accurate outputs from business calculations, including customer LTV. 


Need to lower CAC and increase LTV?

Designed to provide unparalleled clarity into the customer journey, Galileo attribution identifies greatest opportunities and predicts return. Gain accurate CAC and LTV by channel and initiative, increasing ROI 20-40%.

Let us maximize your value.




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