How To Calculate Customer Lifetime Value (2025)

How do you measure your online store’s success? Do you typically focus on things like sales and revenue?

While these ecommerce metrics are useful for tracking the short-term performance of content and campaigns, they don’t always paint a complete picture of your business’s future. Even looking at your current sales numbers can sometimes leave you with just a fleeting glimpse of your business’s true financial situation.

Customer lifetime value (CLV) is one of the most important factors in determining your business’s present and future success. It gives you insight into how much money you should spend on acquiring your customers by telling you how much value they’ll bring to your business in the long run. 

By measuring the net profit that you’ll take in over the course of your entire relationship with a customer, you’ll be able to narrow down exactly how valuable they are to your business. 

Why is calculating customer lifetime value important?

The metric of customer lifetime value gives a clear look at the benefits of acquiring and keeping any given customer. Before we dive into how to calculate customer lifetime value, it’s important to understand the reasons why CLV is so helpful to the success of your business.

Here are the three main benefits to understanding your CLV:

1. Drive repeat sales and revenue. CLV uncovers the existing customers that spend more in your store. It helps you understand what products they enjoy and what products improve their lives. You can use CLV to find high value customers, improve customer satisfaction, and strategize ways to increase customer retention rates. 
2. Reduce your lifetime value (LTV) to customer acquisition costs (CAC) ratio. Our research shows that customer acquisition costs average between $127 and $462, depending on your industry. A good LTV/CAC ratio is 3:1, which signals the efficiency of your sales and marketing. By improving your customer lifetime value, you can benchmark how marketing impacts customer profitability. 
3. Boost customer loyalty. The tactics you use to increase CLV can improve customer support, products, pricing, referrals, and loyalty programs, which leads to a better customer experience. Retained customers buy more often and spend more than newer ones.

What you need to calculate customer lifetime value

Since the CLV formula relies on other metrics, there are a few sets of data you’ll need to gather before you can calculate customer lifetime value for your business

Customer value

Average order value (AOV), or average purchase value, represents the average amount of money that a customer spends every time they place an order. To get this number, take your total revenue and divide it by your total number of orders. 

Formula: AOV = Total Revenue / Total Number of Orders

Purchase frequency represents the average number of orders placed by each customer. Using the same time frame as your average order value calculations, to calculate purchase frequency you’ll need to divide your total number of orders by your total number of unique customers. 

Formula: Purchase Frequency = Total Number of Orders / Total Number of Unique Customers

Using the results from those AOV and purchase frequency formulas, you can then calculate customer value for a set time period. Knowing customer value will be a key factor in calculating their value over the total span of their time as your customer. 

Formula: Customer Value = Average Order Value x Purchase Frequency

Average customer lifespan

Average customer lifespan is the length of time that your relationship with a customer typically lasts before they become inactive and permanently stop making purchases. The nature of this customer relationship varies between contractual and non-contractual business.

Most online stores are non-contractual, meaning that once a purchase is made, the transaction is effectively over. The difficulty with these types of businesses is in identifying when an active customer (someone who makes purchases and will continue to make purchases) becomes an inactive customer (someone who will never make a purchase from your business again).

However, some online stores, like subscription-based businesses, fall into the “contractual” category. With a contractual business, you know exactly when a customer becomes inactive because they announce it when they end their contract or subscription. With a contractual business, it’s much easier to identify your average customer lifespan.

If your store is brand new or has only been around for a few years, you might not have access to enough data to determine the average lifespan length of your customers. But don’t worry—there’s a quick way to work around this and still get some actionable results from your calculations. 

Start by figuring out how long the relationship with each customer lasts between their first and final purchase. Then divide this figure by your total customer base to calculate your average lifespan.

💡Tip: If you own a Shopify store, you can find this information by heading to the Reports section of your Shopify admin. You’ll just need to divide your total sales by your order count for the past year.

How to calculate customer lifetime value

Once you’ve gathered that data and made those initial calculations, you’ll be ready to calculate CLV. Customer lifetime value represents the average monetary value that each customer brings to your business during their relationship with your brand.

Here’s the formula to calculate average CLV:

(Average Purchase Value x Purchase Frequency) x Average Customer Lifespan = Customer Lifetime Value

Let’s say, for example, that a clothing store has an average sale of $50. The typical customer buys from them three times per year and stays with them for two years.

Here’s how we’d use the CLV calculation: CLV = ($50) x (3 purchases) x (2 years) = $300.

Calculate CLV by individual customer

There are some scenarios where it helps to know each individual customer’s lifetime value. 

If you’re dealing with a complaint from a customer who is requesting a refund, for example, you might have different customer service approaches depending on the CLV. Those with a low CLV might be asked to ship the item back in exchange for a refund, whereas those who are more valuable are offered a returnless refund in a bid to continue their loyalty. 

You’ll need the average lifespan figure calculated earlier to work out each customer’s lifetime value. Multiply this by how much they spend with you each year. 

If a customer has spent $500 with you over the past two years, for example, their average annual revenue would be $250. When we multiply this by an average five-year lifespan, the individual customer’s estimated lifetime value would be $1,250.

Calculate CLV by segment using RFM

RFM analysis (recency, frequency, monetary) is a technique for organizing your customers from least valuable to most valuable. 

By segmenting your customers with RFM, you’ll be able to analyze each group individually and determine which set of customers has the highest CLV.

Here’s how RFM analysis breaks down: 

• Recency refers to the last time a customer made a purchase. A customer who has made a purchase recently is more likely to make a repeat purchase than a customer who hasn’t made a purchase in a long time.
• Frequency refers to how many times a customer has made a purchase within a given time frame. A customer who makes purchases often is more likely to continue to come back than a customer who rarely makes purchases.
• Monetary value refers to the amount of money a customer has spent within that same time frame. A customer who makes larger purchases is more likely to return than a customer who spends less.

💡If you own a Shopify store, you’ll be able to find all of this data in the Reports section of your Admin. Head to Reports and click Sales by Customer Name. You’ll be able to find data like order count and total sales for every customer.

Example RFM analysis

To run an RFM analysis, each of these variables needs to be given a scale. Assign a value of 1 to 3 for each of your customers’ recency, frequency, and monetary value. 

Think of these three values as categories: 1 being the least valuable, 2 being somewhat valuable, and 3 being the most valuable. So, when you sort your data, your least valuable one-third of customers will get assigned a score of 1, the third above that will get a 2, and so on.

To help you get a better idea of how this might work, let’s take a look at an example spreadsheet.

In this example, we’ve collected customer information and broken down each variable into three categories based on the relevant data. To do this, we’ve taken the range of data for each variable and divided it into three equal segments.

As an example, for recency, customers who have made a purchase within the past four months are given a 3. Customers who have made a purchase within the past four to eight months are given a 2. And customers who have made a purchase within the past eight to 12 months are given a 1.

Next, we’ll add up the score for each customer’s recency, frequency, and monetary value and list a total under the RFM Score column. From there, you’ll be able to sort your chart by RFM score and divide your results by highest (shown here in red), middle (orange), and lowest score (yellow).

Your highest scoring results will be your most valuable customer segment—be sure to dive into the data to try and find common threads between these customers that could indicate why they provide more value and how you can target them better.

Calculate predictive CLV

The traditional CLV calculation relies on historical data to give an estimate of how much the average customer will spend during their relationship with your brand.

But there’s an issue with this: Customer lifetime value is a metric that’s constantly changing. For example, different marketing strategies might impact how much some customers spend with your business, while supply chain disruptions could deter others from buying. 

Fortunately, there is a workaround to the traditional CLV calculation—which can only tell you what people have spent with the business historically. Instead, you could use a predictive customer analytics approach. This will help you better estimate how much you can anticipate customers will spend in the future. 

The formula to calculate predictive CLV is the same: (Average Purchase Value x Purchase Frequency) x Average Customer Lifespan.

However, the input metrics use supplementary data that looks forward, not just backward. This requires real-time data collection and machine learning algorithms within business intelligence tools to make accurate projections. For example, calculating predictive CLV could involve relevant data about market trends, shifts in consumer behavior, and changing customer acquisition costs.

How to use your CLV calculations

CLV is vital for building smarter, more efficient marketing campaigns to optimize your spending and target customers more accurately. 

Here’s how you can put your CLV calculations to work:

Maximize return on investment

When you know the total lifetime value of an average customer, you can identify which segments are the most profitable. Prioritize outreach to similar customers in your marketing campaigns to improve return on investment (ROI).

Identify your ROI by subtracting your CPA from your CLV. This result represents the net profit from each customer after accounting for the acquisition costs.

Set budget for paid ad campaigns

Understanding your CLV will allow you to determine how much you can afford to spend on paid ad campaigns, such as those on Google and social media platforms like Instagram or TikTok.

You can also determine your maximum bid for an advertising campaign based on your CLV calculation and conversion rate. For example, if your CLV is $100 and your campaign’s conversion rate is 10%, then your maximum bid should be 10% of $100. In this case, you could bid up to $10 per click without going over budget.

Optimize product prices and offers

CLV calculations help inform pricing decisions and maximize profitability. When you know how much customers tend to spend during their lifespan, you can determine a product’s perceived value and reverse-engineer promotions that keep them coming back.

Zarina Bahadur put this into practice for 123BabyBox’s children subscription boxes. In the past year, Zarina says the brand has increased its CLV by 40%, and credits that increase to a restructure of subscription tiers designed to keep customers engaged. 

“We analyzed our CLV and saw a major drop-off after three months,” Zarina says. “So, we reworked our pricing to reward commitment.” 

Now, a one-month subscription to 123BabyBox costs $59.99 per box, but those who commit to a longer subscription pay less. These discounts get bigger the longer the commitment—eventually dropping down to just $39.99 per box for an annual subscription. Long-term subscribers also get extra perks, like early access to limited edition products and priority shopping, as an incentive to stay subscribed.

“This simple shift boosted our average subscription length from five months to eight, adding nearly $150 in CLV per customer,” Zarina says. “Churn dropped by 18%, and referrals increased because customers felt like they were part of something special.”

Zarina gives one piece of advice for ecommerce brands that are calculating CLV: “Keep it simple. Track how long customers stay and what they spend, then find one friction point and fix it. The easiest wins often come from improving what’s already working.”

Identify upselling opportunities 

Customers with a high CLV are typically more engaged, more loyal, and more likely to respond positively to upsell initiatives. This means you can bump up your CLV figure even further and squeeze more revenue out of loyal customers.

To do this, identify high-value customers and focus on retaining them through targeted marketing and loyalty programs. Use the data that contributed to the CLV calculation—purchase history, preferences, or customer behavior—to tailor offers that are more likely to be appealing and relevant.

“You need to be mindful about what’s leading you to slightly better people [customers who stick with you longer and spend more money], and put a little bit more emphasis there and a little less emphasis on those people we know aren’t going to come back,” says Neil Hoyne, chief strategist for data and measurement at Google, in a Shopify Masters episode.

Finding loyal customers for your business

Whether you’re an ecommerce store or SaaS startup, success isn’t about finding customers—it’s about finding the right customers. 

Now that you can calculate the value of your customers, you’ll be able to start crafting campaigns that target and win over those customers that really make a difference to your bottom line.