Customer lifetime value (CLV) is the amount of money a customer is expected to spend with your company over their lifetime with your brand. All businesses survive and thrive on having customers, and a lot of effort goes into acquiring and retaining them. But how do you know the value of your customers in terms of profitability over their lifetime with your company? By calculating the customer lifetime value.
What Is Customer Lifetime Value?
Customer lifetime value is the amount of money a customer is expected to spend with your company (buying products or services) over their lifetime with your company. CLV is also known as CLTV and sometimes LTV. Essentially, it is an educated estimate of the amount of profit you earn from a customer relationship. The CLV works best for companies that have multi-year relationships.
You can measure CLV for all your customers. However, this approach works best if you only have a couple of products close in value or customer segments. A better approach is to determine lifetime value for individual personas, customer segments, or cohorts. Measuring CLV by customer segments gives you deeper insights into which customer segments are most profitable.
Measuring customer lifetime value is essential for a few reasons, including:
- How much do you need to spend to acquire a customer and still make a profit?
- Budgeting spends on marketing, sales, customer support, and customer service.
- Determining your most profitable customers.
- Determining your most profitable products and services.
You typically calculate CLTV using historical data, but you can also use a predictive analytics model that considers customer churn.
How to Calculate Customer Lifetime Value
There are a few formulas out there that help you calculate customer lifetime value, some basic, others more in-depth, but they all follow the same general formula:
CLV = Average Order Value (AOV) X Average Purchase Frequency Rate (AFR)
- Average Purchase Value is the average revenue you make for each purchase in a year (Total Revenues / Total purchases).
- Average Purchase Frequency Rate is total purchases divided by the number of unique customers in a year.
For example, let’s say your business sells high-end women’s shoes. In a single year, you sell 5,000 pairs of shoes to 3,500 customers earning revenues of $1,050,000. Using the formula above:
- CLV = (1050000/5000) X (5000/3500) = $210 X 1.43 = $300
- So your CLV is $300 per customer per year.
- Let’s take this a step further and factor in the costs to make those shoes.
- CLV = AOV x AFR X Gross Margin
- Gross Margin is the amount of profit you earn after the costs of making the product are removed, Revenues - Cost of Goods Sold (COGS).
For our shoe example, let’s assume it costs an average of $50 to make a pair of shoes ($250,000 to produce 5,000 pairs of shoes).
- Gross Margin = (1050000 - 250000)/1050000 = .76 or 76%
- CLV = $210 X 1.43 X .76 = $228
Not everyone agrees that you should include Gross Margin in the CLV calculation because costs to make products fluctuate, but if your costs don’t fluctuate greatly, then it’s good to include it.
Calculate Churn Rate
Another aspect that you can bring into the CLV calculation is Churn Rate (the percentage of customers that leave you over a defined period). By adding Churn Rate into the calculation, you are performing predictive analysis.
Churn Rate = (Lost Customers ÷ Total Customers at the Start of Time Period) x 100.
In our example, 250 people only make one purchase over their lifetime, so Churn Rate = (250/5000) X 100 = 5%
Adding Churn Rate into the calculation we get:
- CLV = AOV x AFR X Gross Margin X Churn Rate
- CLV = $210 X 1.43 X .76 X (1/.2) = $1.141.14
Calculate Customer Acquisition Costs
The last thing to consider when calculating customer lifetime value is customer acquisition costs (CAC). CAC is the amount of money you spend to acquire a customer. Adding CAC to your CLV calculation gives you a more accurate view of your profit from each customer.
CAC = Costs associated with converting a customer / the number of customers acquired. Costs include spending by marketing and sales.
For our example, let’s assume marketing spends $650,000 per year, making CAC equal to $130 (650000/5000).
- CLV = (AOV x AFR X Gross Margin X Churn Rate) - CAC
- CLV = ($210 X 1.43 X .76 X (1/.2)) - $130 = $1.141.14 - $130 = $1011.14
The above calculations are done using an overall average of revenues and don’t consider that each type of shoe sold costs a different amount with different gross margins. It also doesn’t look at the types of customers that purchase from you (segments that buy lower-end shoes versus customer segments that purchase higher-end shoes).
Consider that someone who buys lower-end shoes may purchase several pairs of shoes a year, whereas someone who buys high-end shoes might only purchase a single pair over several years. Which type of customer is more profitable for you? The only way to know is to calculate CLV separately for each segment.
How to Improve Customer Lifetime Value
Knowing customer lifetime value is half the challenge. Putting programs and activities in place to improve it is the second part. Improved CLV directly affects revenue and reduces customer retention costs.
There are a lot of ways you can improve CLV. Some relate to acquisition, and some relate to retention. The following are a few ways you can improve CLV.
If you are an ecommerce company, you can:
- Make it easier to buy from you by finding ways to streamline the checkout process.
- Offer bundles at a discounted rate to increase the amount purchased during a single order.
- Provide additional related offers as a customer goes through the checkout process (usually at a discount).
- Send follow-up emails with new offers or special deals during certain times of the year.
- Create a loyalty program that includes discounts, early notice of sales, or special deals.
- Create a customer engagement program that regularly surveys your customers to get their opinions on your products.
- Make it easy to do returns or exchanges.
If you are a SaaS company, some ways to improve CLV include:
- Offer free trials or subscription levels based on usage.
- Optimize the onboarding process, making it easier for a customer to get set up (provide virtual training, guided tours, templates, easy access to support through email or phone).
- Create a regular communications program that sends customers information to help them use the service (monthly customer newsletter, how-to blogs or videos, etc.).
- Conduct regular customer satisfaction surveys to understand what’s working and what’s not, including how you can improve your service to keep your most profitable customers happy.
- Collect customer feedback on most-wanted features so you can focus on the enhancements that keep customers happy or upgrade them to the next level.
- Offer personalized content based on where a customer is in their journey with your application (onboarding, using for a month, using for a year, subscribed but not actively using).
- Offer add-on services or upgrades to the next version at a discount.
Measuring customer lifetime value is key to your company’s success. While you can calculate it for your business overall, the best approach is to separate your calculation by some unique identifier, such as persona, segment/cohort.
Read More: How to Improve Customer Lifetime Value With a CDP
Historical, cohort, or predictive CLV: choosing an approach
The formula stays the same no matter which approach feeds it — what changes is how much of the estimate comes from observed behavior and how much comes from a model. Pick the approach your data can support today, and name the limitation you are accepting.
| Approach | What it establishes | Why it matters | Failure mode |
|---|---|---|---|
| Historical | Total revenue to date divided by the customers who produced it | Needs no modeling, so it is the fastest way to validate that your formula and data plumbing are sound | Survivorship bias: averaging only customers who are still active drops everyone who churned and inflates what a new customer appears to be worth |
| Cohort-based | Revenue tracked by acquisition period, so each group of customers builds its own value curve | Turns CLV from a single number into a trend you can compare across pricing, product, and channel changes | Recent cohorts are incomplete, so a 2-month-old cohort always looks worse than a mature one — an apparent decline that is really early data |
| Predictive | Modeled future spend per customer, built from early signals such as recency, frequency, and product mix | Lets you budget acquisition and retention against expected value while relationships are still young | The model degrades when pricing, assortment, or customer mix shifts, so it needs scheduled retraining, not one-time calibration |
Work through them in order. If you cannot yet tie every order to an acquisition date, fix that gap first, because every later approach depends on it. Once cohort curves are clean, they usually replace the blended average — a curve shows whether value per customer is rising or falling instead of guessing from a total. Add a predictive layer only when you have enough completed cohorts to train against, and treat its output as a hypothesis that realized numbers later confirm or correct. A prediction that feeds automated decisioning, including agentic AI systems acting with minimal human review, carries these limitations into every downstream action, so keep a plainly calculated historical figure next to it as the check.
Common CLV calculation mistakes and how to fix them
Most CLV errors are not math errors — they are definition and data errors that survive every recalculation until someone changes the inputs. Four worth checking before you trust the number:
Revenue counted, margin ignored. A segment that spends heavily on low-margin products can look more valuable than a smaller segment buying high-margin ones. Fix: apply gross margin at the product-line or segment level wherever the mix differs, and keep the blended margin as a company-wide sanity check.
Mismatched time windows. Pairing 12 months of spend with churn measured over 24 makes one segment look better or worse than it is, and the error repeats in every report built on it. Fix: settle on one observation window and label it on every number you publish.
Gross figures left unnetted. Refunds, returns, and chargebacks sit in the revenue numerator but never reach the bank, so CLV drifts upward each period. Fix: net them out before averaging, then reconcile the result against the revenue figure finance reports.
Broken identity resolution. Duplicate customer records split one buyer into several, which understates purchase frequency, while over-merged records inflate it. Fix: reconcile the customer count and order count behind the calculation against a trusted source before comparing one period to another.
How often to recalculate customer lifetime value, and what invalidates the number
A CLV figure has a shelf life. Recalculate on a schedule matched to purchase tempo — monthly or quarterly for ecommerce, quarterly or semiannually for subscription and other multi-year relationships — and re-derive out of cycle when a trigger event changes the inputs: a price change, a new product line, a shift in acquisition channel mix, or a churn spike after a service incident. Publish the window and its date next to the number so two teams quoting CLV in the same meeting are quoting the same vintage.
The refreshed figure matters because it feeds the systems that act on it. Lifecycle messaging, value-tiered service levels, and agentic personalization programs all consume the score, and a stale input bends every downstream decision. A calculation that lives in a spreadsheet ages fastest of all — connecting it to the platform that activates segments, a packaged CDP or an agentic CDP where agents are the primary consumers, keeps the number where decisions happen.
FAQ
What is a good customer lifetime value?
A good customer lifetime value depends on your industry and business model, but generally CLV should be at least 3 times your customer acquisition cost (CAC) to ensure profitability. The key is to calculate CLV by customer segments or cohorts rather than overall averages, as this reveals which customer types are most profitable. Multi-year subscription businesses and high-margin product companies typically see higher CLV ratios.
How can I increase customer lifetime value?
You can increase CLV by improving retention (reducing churn through better onboarding, customer service, and loyalty programs), increasing purchase frequency (through targeted campaigns, subscriptions, and reminders), raising average order value (via upsells, cross-sells, bundles, and premium offerings), and reducing customer acquisition costs. The most effective approach is to focus on your highest-value segments and optimize their experience first.
Should I include customer acquisition cost in CLV calculations?
Including customer acquisition cost (CAC) in your CLV calculation provides a more accurate view of actual profit per customer and helps determine if your acquisition spending is sustainable. The formula becomes: CLV = (AOV × AFR × Gross Margin × Churn Rate) - CAC. While some marketers calculate CLV and CAC separately, subtracting CAC gives you the true net value of a customer relationship.
What data do I need to calculate customer lifetime value?
You need complete order or invoice history with amounts and dates, a reliable first-purchase or signup date for each customer, gross-margin data, and refund and cancellation records. Records should tie to a single customer identifier, and segment attributes such as acquisition channel or plan tier let you split the calculation where it matters most. If purchase history is fragmented across systems, consolidate it first — averages built on partial history quietly understate value and hide your best segments.
Can I calculate customer lifetime value for a new business with little history?
Yes — state the observation window and treat the result as provisional rather than a benchmark. With less than a year of data, calculate value to date for the customers you already have instead of projecting a lifetime figure, and watch how each new cohort’s curve develops rather than extrapolating the first few months. Absolute numbers stabilize once cohorts mature; the trend across cohorts tells you more than any single figure.
Related Terms
- Propensity Modeling — Predicts which customers are likely to purchase, churn, or upgrade, directly informing CLV estimates
- Customer 360 — Unified customer profile that provides the data foundation for accurate CLV calculations
- Revenue Operations — Cross-functional discipline that uses CLV to align marketing, sales, and customer success
- Marketing Analytics — Broader analytical practice that incorporates CLV as a key performance metric
- Customer Health Score — Leading indicator that helps predict future CLV changes
- Customer Experience Management — Customer experience management (CEM) is the practice of designing personalized interactions across every touchpoint.
- Customer Experience Strategy — A customer experience (CX) strategy defines how a company delivers positive interactions at every touchpoint.