How to calculate customer lifetime value (LTV)

A landscaper who lands a one-time job thinks about revenue differently than one who signs a neighborhood to annual service contracts. The one-time job has a fixed ceiling; the contract has a compounding one. That gap in thinking shows up in marketing, too. Brands that calculate success only based on what a customer spends on their first order are essentially taking one-time jobs, leaving the real long-term value of the relationship off the books entirely.

Customer lifetime value (LTV) fixes that. It gives marketing teams a way to understand what a customer is actually worth over time, which changes how you think about how much money it makes sense to spend to acquire them. For brands focused on long-term profitability—not just short-run efficiency—that shift in thinking has a direct impact on how budgets get built and defended internally.

Key takeaways

• Customer lifetime value (LTV) measures the revenue a business can expect from a single customer over the course of their relationship, typically time-boxed to a year rather than a literal lifetime.
• Several customer lifetime value formulas exist, but the differences between them are smaller than most teams think; what matters far more is that every department calculates LTV the same way, every time.
• A consistent LTV definition is what makes meaningful budget conversations between sales, marketing, and finance possible, especially when a company needs to acquire customers at scale.
• LTV is most useful when evaluated alongside customer acquisition cost (CAC); a 3:1 LTV to CAC ratio is a widely used benchmark for healthy acquisition economics.
• For subscription and SaaS businesses, LTV calculations need to account for monthly churn rate, since recurring revenue that doesn't stick changes the picture significantly.
• Customer satisfaction and retention are the most direct levers brands have for increasing customer lifetime value over time.
• The accuracy of any LTV calculation depends on the quality of your attribution data; incomplete attribution leads to an incomplete picture of what a customer is worth to your business.

What is customer lifetime value?

If you're newer to LTV as a concept and want a full breakdown before getting into the calculation, our primer on what lifetime value is is a good place to start. For the purposes of this article, we'll stay focused on the math and the measurement.

Customer lifetime value is the revenue a business can expect from a single customer across their entire relationship with that company. It's worth noting upfront that when most brands talk about LTV, they're not working with a literal lifetime; instead, they're using a time-boxed window, usually a year. That framing keeps the CLTV metric grounded and practical, because projecting customer behavior over an indefinite horizon gets speculative quickly.

LTV sits at the center of some of the most important decisions a company makes. It informs how much you can afford to spend to acquire a new customer, which channels are worth investing in for the long term, and whether your current approach to customer acquisition is actually building a profitable business or just generating revenue that won't stay.

The customer lifetime value formulas you'll actually use

There are a few different approaches to calculate customer lifetime value depending on your business model, and none of them require more than a handful of metrics you likely already have. Below are the three LTV formula variations that come up most in practice, along with when each one tends to be most useful.

Basic formula

The most commonly used approach to calculate customer lifetime value starts with three inputs: how much a customer spends on average, how often they buy, and how long an average customer stays. Put those together and you get:

Customer Lifetime Value = Average Purchase Value × Purchase Frequency × Average Customer Lifetime

To calculate each component:

• Average Purchase Value: Total sales revenue for a given period ÷ number of orders in that same period
• Purchase Frequency: Total orders in the period ÷ number of unique customers who purchased
• Average Customer Lifetime: The average length of time a customer continues to buy from your business, typically expressed in years

Here's an example: a company with an average order value of $80, a purchase frequency of 3 times per year, and an average customer lifetime of 2 years would calculate customer lifetime value as $80 × 3 × 2 = $480. If that company's customer acquisition cost is $150, the LTV to CAC ratio comes to 3.2:1, right around the benchmark most businesses aim for. That's a good LTV to CAC relationship, and it's a number both sales and finance can work with when evaluating channel performance.

This formula works well for most ecommerce and retail businesses because it relies on metrics that are easy to pull and straightforward to explain across teams.

Profit-adjusted formula

If gross margin varies significantly across your product catalog or customer segments, a formula that accounts for that will give you a more accurate picture of what a customer generates for the business. A profit-adjusted version of the customer lifetime value formula looks like this:

Customer Lifetime Value = (Average Purchase Value × Purchase Frequency × Gross Margin %) × Average Customer Lifetime

Other factors like discount rate can also be incorporated for a more precise present-value calculation; a discount rate adjusts future revenue to reflect what it's worth in today's terms. For most consumer brands, the gross margin adjustment alone adds meaningful accuracy without making the calculation unwieldy, but a discount rate becomes more relevant when you're modeling longer customer lifetimes. Existing customers with strong repeat purchase patterns and high average order values tend to show the clearest picture here, making cohort analysis a useful companion to this formula.

SaaS and subscription formula

For subscription-based businesses, the standard formula doesn't quite work because the relationship between revenue and time is structured differently. When a customer stays on a monthly plan, Purchase Frequency isn't a meaningful variable. What matters more is how long they remain customers before they churn. For a SaaS company or SaaS business, LTV is typically calculated as:

LTV = (Average Revenue Per Account × Gross Margin %) ÷ Monthly Churn Rate

Monthly churn rate is the percentage of customers who cancel in a given month, and it has an outsized effect on lifetime value for subscription models. If your monthly churn rate is 5%, your implied average customer lifetime is about 20 months. Reducing churn to 3% extends that average customer lifetime to 33 months, and customer lifetime value grows accordingly. Reducing churn is therefore one of the highest-leverage activities a SaaS business can focus on, since improvements in customer satisfaction and retention translate directly into stronger long-term value across the entire customer base.

The formula matters less than you think

Yes, some businesses are more suited to certain calculation styles than others, but the differences between these customer lifetime value formulas are not as significant as the consistency with which your company applies them. Whether a business uses the basic formula or the profit-adjusted version, the bigger risk isn't necessarily choosing the "wrong" one. Bigger issues come with having different parts of the company calculate customer lifetime value differently, or not agreeing on which costs and inputs belong in the calculation at all.

This becomes especially clear when a company needs to make a case to finance for budget. Say your team is planning an awareness campaign heading into a high-spend period and needs leadership to sign off. That conversation depends on both sides working from the same understanding of what a customer is worth. If sales and marketing are using one customer lifetime value figure while finance is using another—or if finance isn't working with an LTV number at all—the budget conversation stalls. Your team may be advocating for acquisition spend that looks completely reasonable against their LTV calculation, while finance sees a different value and can't follow the logic.

A shared, documented definition of customer lifetime value that's used consistently across the company is what makes cross-functional alignment on acquisition investment possible. The most valuable customers are the ones everyone agrees on, and that starts with agreeing on how to calculate customer lifetime value, and committing to measure it the same way every time.

How LTV should inform your marketing budget

Once you have a reliable LTV number, the most practical immediate application is putting it alongside your customer acquisition cost (CAC). A good rule of thumb for most business models is a 3:1 LTV to CAC ratio: for every dollar a company spends to acquire a new customer, that customer should generate roughly three dollars in lifetime value LTV. This ratio gives you a working boundary for how much it makes sense to spend on customer acquisition across different channels and sales efforts.

That ratio also gives context to channel-level decisions. If a particular channel is driving customers with strong repeat purchase behavior and high customer satisfaction scores, a higher customer acquisition cost on that channel may still fall within a healthy ratio. If another channel brings in customers who churn faster or buy less frequently, a lower CAC might still look problematic once long-term value is factored in. Cohort analysis is one of the most useful tools for surfacing these patterns as it lets you calculate customer lifetime value by acquisition cohort and see how the metrics develop over time for different groups of new customers. Tracking existing customers across multiple cohorts gives you the most reliable picture of which acquisition strategies are generating your most valuable customers.

What makes LTV hard to measure accurately

Even with a clean formula and consistent inputs, a few real-world factors make customer lifetime value projections imperfect. Average customer lifetime is inherently a forecast: you're making a reasonable estimate based on historical behavior, but individual customers don't follow averages. Customer churn can be hard to predict, especially when market conditions shift or when your customer base is still maturing. Average revenue per customer can also change over time as your product catalog grows or as a customer buys more frequently over the course of their relationship with the brand.

There's also a less obvious problem: most companies don't have a complete picture of the revenue a customer brings in across their entire relationship with the business. A customer might convert through paid search, but their first interaction was a brand awareness campaign that ran weeks earlier. If that top-of-funnel activity doesn't show up in your attribution, the revenue that customer generates gets credited to the wrong place and your understanding of which campaigns are actually acquiring your most valuable customers, with the strongest long-term value and the highest customer satisfaction, gets distorted as a result. When you calculate customer lifetime value based on incomplete attribution, you're working with metrics that understate what your best acquisition channels are actually delivering.

Where Prescient comes in

Prescient's marketing mix model gives brands a more complete picture of what their campaigns are actually driving, including the downstream revenue effects that most attribution tools miss. That means measuring halo effects (the impact awareness campaigns have on branded search, direct traffic, organic visits, and retail channels like Amazon) alongside direct revenue, so the data feeding your customer lifetime value calculations reflects what customers actually bring in over time, not just what a click-through window can capture.

When your attribution is more complete, every metric downstream—including lifetime value—is built on better inputs, and the decisions your company makes based on those numbers are on firmer ground. If you'd like to see how Prescient measures the full revenue impact of campaigns, book a demo with our team of experts.

\