LTVLifetime Value

Lifetime Value (LTV) is a metric that reflects how much profit a company earns from a single customer over the entire period of their interaction with the brand (throughout their “lifetime” in your business).

This is a predictive metric built on historical averages, so it does not guarantee an exact result — but it serves as a benchmark. With it, a business can estimate the real value of its customer base and understand which acquisition channels bring in more valuable customers. This makes it possible to allocate marketing budgets more thoughtfully, plan retention strategies, and make data-driven growth decisions.

In other words, in marketing, LTV is used as a guideline: if you know how much a customer brings in on average, you can understand how much it’s worth investing in acquiring them.

How to calculate LTV correctly

Data required for the calculation

In the list below, you’ll see five metrics, but not all of them are used at the same time. That’s because LTV is an approximate metric, and digital marketers and analysts use different formulas depending on the type of business, available data, and the level of analytics resources. That’s why there are several approaches to calculating it.

In this article, we’ll look at two of the most common approaches: a simple formula (relevant for e-commerce and non-subscription services) and a classic formula (for subscription models such as SaaS or EdTech).

For the simple formula you need:

• AOV (Average Order Value) — the average value of one order. It shows how much, on average, a customer spends per purchase.
  It’s calculated as total revenue for a given period / the number of orders in that same period. For example, if a store earned $50,000 in revenue in a month and processed 1,000 orders, then AOV = 50,000 / 1,000 = $50.
• PF (Purchase Frequency) — the average purchase frequency per customer. It shows how many times, on average, the same customer places an order during a given period.
  It’s calculated as the total number of orders / the number of unique customers in that same period. For example, if a store fulfilled 4,000 orders for 2,000 customers in a year, then PF = 4,000 / 2,000 = 2 purchases per year.
• Lifetime — the length of a customer’s “lifetime,” meaning how long, on average, a customer remains active. It can be measured in months or years — the key is to keep the units consistent with the other metrics in the formula.
  How can you calculate customer “lifetime” (Lifetime)? In businesses without a subscription model, such as online stores, it’s not always clear when exactly a customer stopped being active. That’s why lifetime is often estimated based on data from inactive users. For example, you can consider “lost” those who haven’t purchased for more than 3 months. For those customers:
  • Find the date of the first and last purchase.
  • Calculate the interaction duration for each customer.
  • Find the average value across all customers in that group — that will be the Lifetime.

For the classic formula:

• ARPU (Average Revenue per User) — the average revenue a single user generates over a given period.
  Formula: total revenue for the period / the number of active users in that same period. For example, if you have 1,000 active subscribers and total monthly revenue is $12,000, then ARPU = 12,000 / 1,000 = $12 per month.
• Churn Rate — the share of users who stopped using the service during a given period.
  It’s calculated as the number of users who left during the period / the number of users at the beginning of that period. For example, if you had 1,000 users at the start of the month and 950 remained by the end, then churn = (1,000 – 950) / 1,000 = 0.05 or 5%.

Common mistakes in calculation

• Mixing different periods for metrics

If, for example, ARPU is calculated monthly and Churn Rate is calculated yearly, the formula will produce an incorrect result. All metrics in the formula must be within the same period (monthly, quarterly, or yearly).

• Different time windows for LTV

Sometimes LTV is compared using a customer’s current value regardless of how long they’ve already been with the company. But it’s more accurate to evaluate LTV within the same time frame from the first purchase (for example, 30, 90, or 365 days). This enables a fair comparison of new and long-term customers and helps avoid distortions.

• An unrealistically inflated customer lifetime

This problem occurs when Lifetime is calculated using limited or early data and then automatically projected into the future. This is especially risky in businesses with unstable churn: numbers from the first months may not reflect the real situation. To avoid this, it’s worth recalculating LTV from time to time using updated data.

• One LTV for the entire audience

An overall average across the entire customer base hides differences between segments. Customers from different channels or regions may have different value, so it’s better to calculate LTV separately for key segments.

• Ignoring changes over time

LTV is a dynamic metric. It changes along with pricing, the product, and customer behavior. It should be updated regularly (for example, quarterly) so decisions remain relevant.

• Using a single churn rate (Churn Rate) for all customers

Different customer segments may have different churn. For example, new users often leave faster than those who have been with the company for several months. If you use one averaged Churn Rate for everyone, LTV may be inaccurate.

• Not accounting for margin (relevant for more complex calculations)

Margin shows what portion of revenue remains for the business after covering direct costs of producing or delivering the product. Basic formulas don’t account for this, and for most marketing tasks that’s sufficient. But if the goal is more complex — for example, detailed financial analysis or calculations for investors — it’s worth adding a Gross Margin coefficient to estimate the customer’s actual profitability.

• Fully trusting predictive LTV

LTV is a useful metric for evaluating customer value and planning marketing investments, but it remains a forecast built on historical averages. Treating LTV as the only and error-free benchmark is a mistake.

LTV formula

Simple LTV formula

This calculation method works for non-subscription businesses where customers make purchases from time to time — for example, online stores, one-time order services, or offline retail. In such cases, it’s difficult to determine the exact moment a customer has permanently “left,” so a simplified approach is used without churn rate, focusing on average order value, purchase frequency, and an approximate interaction duration.

Or, simplified, the formula looks like this:

LTV = Average order value × Number of orders per year/x months × Customer lifetime in years/months

Imagine a home goods online store where the average order value is $40, customers make 2 purchases per year on average, and the average customer “lifecycle” is 3 years. In that case, the calculation looks like this: LTV = 40 × 2 × 3 = $240

In this example, $240 is the approximate total revenue one customer will bring to the business over the entire relationship. Knowing this value helps you make more informed marketing decisions: for example, how much you can spend at most to acquire one customer (CAC) to keep the investment profitable, or how increasing average order value or purchase frequency will affect long-term revenue.

Classic LTV formula

This method is more suitable for subscription-based businesses (SaaS, online services, EdTech, etc.), where it’s easier to track how long a customer “lives” in the system — because once they cancel their subscription, it becomes clear the interaction has ended.

Imagine you have an EdTech platform with a paid subscription. The average user brings you $10 per month — that’s ARPU (Average Revenue Per User). The monthly churn rate is 5%, or 0.05 in decimal form. Then LTV = $10 / 0.05 = $200

So, each user generates $200 in revenue on average over the entire period of interaction with the service. Understanding this number helps you determine the maximum customer acquisition cost (CAC) you can afford while keeping the investment profitable, and also forecast how much revenue a certain number of new subscribers will generate.

How do you know if LTV is “good”?

On its own, LTV does not tell you whether acquiring customers is profitable. Its value becomes clear when compared to customer acquisition cost (CAC — Customer Acquisition Cost).

There is a generally accepted rule of thumb:

For example, if the average LTV for a business is $300 and the cost to acquire one customer is $100, then the LTV/CAC ratio = 3:1, and the marketing model is considered healthy. If the ratio drops below 3:1, that’s a signal to either optimize acquisition costs or increase LTV (increase average order value, purchase frequency, or customer retention duration).

Practical use of LTV

How LTV helps optimize the marketing budget

• Defining the maximum acceptable customer acquisition cost (CAC)
  Knowing LTV, you can calculate how much you can spend at most to acquire a new customer while keeping the campaign profitable. In most cases, the rule of thumb is used: LTV should be about three times higher than CAC. This creates a financial safety buffer and reduces risks when scaling advertising.
• Comparing channels by long-term return
  LTV shows not only how many leads or sales a channel generates, but also their future value. This helps direct budget toward channels that generate higher revenue in the long run.
• Segmenting the audience by potential revenue
  Breaking customers down by LTV helps identify the highest-value groups and run separate ad campaigns for them. For example, an online store can build lookalike audiences based on its most profitable customers to attract people with similar characteristics and behavior.

Using the metric in retention strategy planning

• Personalization opportunities
  If you know a certain customer segment has a higher LTV, you can develop special offers to encourage their activity: exclusive discounts for loyal customers, bonuses for repeat purchases, gifts for referrals.
• Evaluating and adjusting loyalty programs
  LTV helps you understand how much a company can spend on customer incentives so those costs pay off. For example, if analysis shows that an additional purchase increases LTV by $100, then a $10 bonus or discount looks like a smart investment rather than a cost.
• Controlling churn risk
  If you look at LTV together with churn rate, you can not only estimate average customer value but also understand how quickly that value “disappears” due to customer loss. For example, if a segment has high LTV but also high churn, it’s a signal that potentially profitable customers are leaving too early.

How LTV affects company growth

• Assessing readiness to scale
  If LTV significantly exceeds CAC, it means acquisition costs pay back quickly and generate additional profit. In that case, you can increase marketing investment while maintaining positive unit economics and predictable growth.
• Forecasting future revenue
  Regular LTV analysis helps build more accurate business forecasts: estimate how much profit the existing customer base will generate and plan investment levels in marketing, sales, and product. For example, if average LTV grows year over year, it’s a sign current retention strategies are working and can be scaled.
• Increasing investment attractiveness
  High LTV combined with controlled acquisition costs is an important argument for investors that the business has stable and predictable economics. In investor decks, this metric is often shown alongside the LTV/CAC ratio to demonstrate that acquisition spending generates long-term returns.
• Improving service and product
  If LTV increases due to repeat purchases and high loyalty, it suggests investments in service quality, product usability, or faster delivery are justified. For example, an online store that reduced delivery time from 5 to 2 days may see repeat orders increase and, accordingly, LTV rise.

The difference between Lifetime Value (LTV) and Customer Lifetime Value (CLV)

These two metrics are often used as synonyms, although in fact there are nuances between them. So, let’s start with the simplest part — the difference in definitions:

LTV (Lifetime Value) is a broader concept. It shows how much a customer generates on average over the entire relationship with the business. This is an average metric across the whole customer base — it helps you understand whether the business model is profitable overall, forecast revenue, and determine how much you can spend to acquire new customers.

CLV (Customer Lifetime Value) is a more individual approach. It is calculated for a specific customer. CLV shows who brings in more and who brings in less, and makes it possible to act more precisely: offer special deals to valuable customers, run loyalty programs for them, or, conversely, limit spending on those who are unlikely to become profitable.

As for calculation, the formulas for LTV and CLV rely on the same logic but are applied in different situations. In this article, for LTV we have already reviewed two options:

• for non-subscription businesses — AOV × PF × Lifetime;
• for subscription models — ARPU / Churn Rate.

CLV is usually calculated like this:

CLV=(Average Purchase Value×Average Purchase Frequency)×Average Customer Lifespan

Given that Average Purchase Value = AOV and Average Purchase Frequency = PF (these are simply different names for the same metrics that came from different business contexts). The formulas for LTV and CLV essentially calculate the same thing — they’re just used for different cases: LTV for an average value across the entire base, and CLV for a specific customer or segment.