Lifetime value calculation: How to measure and optimize LTV

Imagine your loyal customers are like the high rollers in a casino. 

They might not buy all the time. But when they do, they tend to spend more, and more regularly. Casinos want to treat those high rollers well and keep them happy. 

Customer lifetime value works similarly: The more users you can turn into happy, loyal customers, the more they will be worth to your business in the long run. 

Customer lifetime value, or the total value a customer brings to a business, is an important metric to understand how profitable a company can be, and how much it can spend to acquire new customers. 

LTV is a critical metric for growth and product teams. Understanding the factors that influence LTV can help organizations make data-driven, strategic decisions that prioritize both user experience and profitability. 

Let’s talk about LTV, how to calculate it, and how to use it.

Customer lifetime value (LTV) is the average revenue a customer generates throughout their entire relationship with your company. It’s used to understand if your customer relationships are profitable. 

Product, marketing, advertising, and sales teams often use LTV to make strategic decisions about things like marketing spend and product development. It also helps them understand how much money they can spend on acquiring, engaging, and retaining customers while still being profitable. Knowing and understanding LTV will also show you which customer segments are most valuable (the ones with the highest LTV). 

Higher feature adoption rates often correlate with higher LTV, and time-to-value in onboarding directly impacts LTV—the more value your customers get from your product, the likelier they are to stick around for longer. Product metrics and product performance have a direct impact on customer lifetime value. 

LTV is an important metric for both transaction-based and subscription business models, though measuring it can look a little different (for example, for SaaS vs. ecommerce companies). 

To calculate LTV, you must first know the value of a few building block metrics. There are a few different ways to calculate LTV, which we’ll get into below. 

Before we dive into those, let’s look at a quick breakdown of the key components of an LTV calculation: 

Customer acquisition cost, often called CAC, is the average amount you spend on acquiring a customer. It includes everything from marketing and advertising to sign-up incentives, (for example, a $10 Amazon gift card) to salaries (or % of salaries) in the sales and marketing departments. 

CAC is particularly important because your LTV should always be higher than your customer acquisition cost—otherwise, you’re spending more to acquire customers than the revenue you are generating from them. For this reason, the LTV/CAC ratio should always be at least greater than one. As a general benchmark, an LTV/CAC ratio higher than three (3:1) is considered good, though the specifics depend on the industry and type of company. 

Retention rate is the percentage of customers who stay with your company over a given timeframe. Churn rate is the opposite: how many customers leave your product, or churn.  

This is the length of a typical customer relationship. To make calculations easier, this is generally measured in multiples of the same period as the purchase frequency. For example, a business that retains its customers well may have a customer lifespan of five years, while one that isn’t good at retaining its customers may have a lifespan of six months, or 0.5 years. Improving customer lifespan is often a very effective way to improve your customer lifetime value.

Note that customer lifespan calculations vary for different types of businesses. For a SaaS product that relies on fixed contracts, the lifespan ends when a customer fails to renew their contract. For a consumer app or ecommerce business, the product team will have to figure out when they consider a customer to have churned (for example, after two weeks of no recorded activity or X months without a repeat purchase).

Engaging customers at every stage of their customer lifecycle can also help boost LTV. 

ARPU or average revenue per user, is a metric that captures the smoothed-out revenue that a company generates per user, usually calculated on a monthly or yearly basis. 

Different industries and business models will have different ARPUs. There is no universally “good” ARPU score, but ARPU is one of the metrics that companies can use to benchmark against their competitors. Investors and VCs also use ARPU to gauge the health of a company and decide whether to invest. 

The formula for ARPU is simple: total revenue divided by total number of active users in a specific time period = ARPU 

This is the average number of transactions a customer makes over a given period (usually a year). Purchase frequency can be calculated by dividing the average number of purchases by the average number of customers. For example, for a monthly subscription service, the number of purchases made over a year is 12.

This is the average value of a customer transaction. For example, for an ecommerce company, this could be the average value of each cart, while for a subscription service this could be the cost of the subscription.

One of the easiest ways to improve LTV is to increase APV, usually by selling add-on items or increasing prices.

This tells you what part of each customer purchase is profit and what part is cost. Average gross margin can be calculated with the following formula:

Gross Margin = (Total Revenue – Cost of Sales) ÷ (Total Revenue)

Many companies use lifetime value (LTV) and customer lifetime value (CLV) interchangeably, and some even combine them as CLVT. As long as everyone agrees on the nomenclature, there are no issues. 

But we also wanted to note that some companies do distinguish between LTV and CLV: They use LTV for average lifetime value across their customer base (or cohort, or segment), and CLV when talking about individual users or accounts. 

Both methods are correct, as long as everyone is on the same page. 

There are multiple ways of calculating customer lifetime value. We’ve included a few different options below. However, LTV can vary based on a variety of factors, like product SKUs/plans (free vs. paid), user types (consumer vs. business), and degree of user engagement (power users vs. casual users).

The simplest LTV formula is: 

LTV = ARPU × Average Customer Lifespan 

Or, alternatively:

LTV = ARPU / User Churn

These formulas are very similar. Since user churn is often a metric you’re more likely to have on hand, it can be the easier option to calculate.

These are the simplest methods to calculate LTV, but they don't give you a complete picture: You’ll notice that they don’t take into account the cost of acquiring a customer, for example. 

A slightly more complex calculation for LTV (that will yield more granular results), is:

CLV = (Average Purchase Value × Gross Margin × Purchase Frequency × Customer Lifespan) – CAC

For example, if your product is a $10/month subscription service with an average gross margin of 70% and you spend $20 to acquire a customer with a lifespan of 60 months (or five years), your customer lifetime value calculation would look like this:

CLV= ($10/month × 0.7 × 12 months/year × 5 years) – $20 = $400

Cohort analytics helps you break down your user base into groups of users based on common characteristics or experiences, allowing you to better identify their behavior across the user lifecycle. Breaking down your LTV by cohort can help you understand which users or product offerings to target.

For example, you can create cohorts of users on each plan you offer, and compare their LTV to find out which users are most profitable. Amazon did just that and found that Amazon Prime members had a much higher LTV, and hence deserved more focus.

Mixpanel’s revenue analytics features make it easy to build cohorts based on defined behavior or attributes. Product teams can use those insights to refine retention strategies and determine which features or users to focus on. 

LTV calculations begin with the assumption that your customers generate an average amount of revenue—and therefore profit—each month or year for a certain amount of time. But the revenue and profits you receive in the future are less valuable than they would be if you received them today. 

Discounting future revenue and profits ties your LTV to the current cost of your investment, as well as your opportunity cost. The discount rate varies from company to company, but once you have a fixed discount rate, you can calculate a net present value (NPV) of your LTV by separately discounting profits for each period, or by using an online NPV calculator (or even Excel).

Instead of looking at historical data (how customers have behaved in the past), predictive analytics uses machine learning and data analytics to forecast future behavior and use that information to determine LTV. 

The key to predictive modeling is using a tool with powerful predictive analytics capabilities, gathering sufficient data to track long-term user behavior trends, and finally, using that information to predict (and ideally, influence) user behavior. 

Understanding LTV on a granular level using cohort analytics and predictive analysis can help guide product growth strategies. Data-driven product teams can A/B test product updates and new features and measure the impact of those changes on LTV. Even incremental product improvements can compound to significantly increase LTV. 

LTV data can also give you a deeper understanding of retention and churn, which will help product teams make smarter roadmap decisions and prioritize the most valuable customer segments. Once you have this information, you can allocate marketing and product resources based on customer profitability, and nudge customers towards behavior that is more likely to increase their LTV.

Hopefully, by now there’s no doubt that LTV is a valuable metric for product and growth teams. But knowing LTV is useful doesn’t make it easy to measure. 

From tracking issues to evolving user behavior, here are a few common challenges product teams encounter when trying to understand LTV, and some tips for overcoming them:

Many companies miscalculate LTV due to inconsistent or incomplete data. If you don’t have a complete understanding of your user base and how they behave, you will miss valuable information and opportunities for improvement—or worse, make business decisions based on faulty data, wasting both time and resources. 

Solution: Use Mixpanel’s event tracking and retention analytics for cleaner data.

LTV is not static—it fluctuates based on product updates, pricing changes, and market shifts. It’s important to have an updated and accurate understanding of LTV and the different factors that influence it, or you’ll be working with outdated information. 

Solution: make sure your data is accurate and current with Warehouse Connectors. 

Many companies struggle with churn impacting LTV calculations, especially if you are using a simpler formula or if your churn rates change unexpectedly. 

Solution: Combine churn analysis with LTV modeling using Mixpanel’s Funnel and Retention reports.

Customer lifetime value can shed light on a lot of key business drivers and opportunities, allowing you to make more informed decisions. 

Some examples are:

Though there are many ways to break down and apply LTV, viewing it only as a tool for acquiring as many customers as possible as cheaply as possible is a recipe for failure. It’s true that deeply analyzing your LTV can help you prioritize segmentation, retention, and monetization to improve future customer profitability. But, as Harvard Business Review states, LTV should be used to see customers as value-creating partners rather than as value-extraction targets.

Remember, LTV is just one piece of the puzzle. But combined with other metrics, it is a powerful way to understand your customers and their impact on your business.