Customer Lifetime Value Calculator

Updated 6th October 2026

A customer’s first order only tells you part of their value to your store. Someone who spends $40 once brings in $40. Someone who returns and spends $40 four times brings in $160. Customer lifetime value helps you look beyond that first sale and estimate what an average customer could spend over the whole relationship.

Use the customer lifetime value calculator below to estimate lifetime revenue, then follow the examples and guidance to understand your result, compare customer groups and encourage repeat purchases.

What is customer lifetime value?

Customer lifetime value is an estimate of the value a customer brings to your business throughout their relationship with it. In a simple ecommerce calculation, that means the total revenue you expect from their purchases, from their first order to their last.

CLV is simply the abbreviation for customer lifetime value. They are not two different measures. You may also see LTV or CLTV used for the same idea. The useful distinction is how a business calculates it: some figures measure revenue, while others account for costs.

This page’s calculator estimates revenue per customer. A $300 result means an average customer could spend $300 with your store over the lifespan you entered. Supplier charges, shipping, payment fees, returns and marketing expenses still have to come out of that revenue.

For a dropshipping store, CLV helps put repeat business into perspective. A customer who returns for another purchase gives you another sale beyond the order that first brought them to your store. Whether those sales are profitable depends on your costs as well as the amount they spend.

How to calculate customer lifetime value

The calculator uses this basic formula:

Average order value × orders per customer per year × customer lifespan in years = estimated revenue CLV

This is the order-value and purchase-frequency approach described in Shopify’s guide to customer lifetime value. You need three figures:

1. Average order value: divide your store’s sales revenue by its number of orders over a chosen 12-month period. Use a consistent treatment of discounts and refunds, and exclude sales tax collected for the tax authority.
2. Orders per customer per year: divide the orders in that same period by the number of distinct customers who placed them. Include people who ordered only once; counting only returning customers would exaggerate the average.
3. Average customer lifespan: estimate how many years customers continue buying from your store. Use your customer history where possible. If your store is new, treat this as an assumption and try a few plausible values.

Your order value and frequency must describe the same customers and period. The calculator expects yearly purchase frequency: two orders per month would be 24 orders per year. The currency field changes the displayed currency; it does not convert amounts.

Customer lifespan needs particular care. An order history covering six months cannot establish that customers will keep buying for five years. Also, a recent last order does not tell you that the relationship has ended. Use this simple model as an estimate and revise it as your store builds a longer history.

Customer lifetime value examples

These hypothetical examples show how the calculation works. They are not industry averages or targets for your store.

Example 1: calculate CLV from your annual store figures

Imagine your store recorded $120,000 in sales from 2,400 orders placed by 800 customers during one year.

• Average order value: $120,000 ÷ 2,400 = $50.
• Annual purchase frequency: 2,400 ÷ 800 = 3 orders per customer.
• Estimated customer lifespan: 2 years.

Revenue CLV is $50 × 3 × 2 = $300 per customer. The customer spends an estimated $150 a year, or $300 over two years, if the buying pattern continues.

Example 2: a customer orders every three months

Suppose the average order is $40 and customers buy once every three months. That is four orders a year: 12 ÷ 3 = 4.

With an estimated customer lifespan of 2.5 years, revenue CLV is $40 × 4 × 2.5 = $400. Entering a frequency of “1” would give $100, because the calculator would interpret it as one order per year. Converting the buying pattern into the correct time unit makes a substantial difference.

How to carry out customer lifetime value analysis

Calculating a storewide average is a starting point. Customer lifetime value analysis asks which customers generate that value, how their spending develops and what might explain the differences.

Start with a specific question, such as whether customers who first buy clothing return more often than customers who first buy accessories. Group customers by their first purchase month, first product or acquisition channel—the source that brought them to your store. A group defined this way is often called a cohort.

Give each group the same amount of time to make purchases. Comparing one group’s first 12 months with another group’s first three months would favor the older group. Keep customers who never returned in the calculation.

Hypothetical examples

Measure	Clothing first purchase	Accessories first purchase
Customers in the original group	100	100
Revenue after discounts and refunds	$14,000	$9,000
Revenue per customer	$140	$90

The clothing group spent more per customer during that window. That is an observed 12-month result, not proof of their complete lifetime value or profitability. Check whether the difference came from larger orders, more repeat purchases or fewer refunds. Product costs and promotional spending could also differ.

Then investigate an action: perhaps accessories buyers need better follow-up recommendations, or the clothing group responded well to a seasonal range. Test that idea with a later group rather than assuming the first product caused the difference. Shopify’s customer cohort reports provide one way to examine repeat purchasing over time.

How customer lifetime value helps your marketing

CLV gives marketing decisions a longer view. A campaign that attracts many first orders may look successful immediately, while another could bring customers who return more often. Comparing their customer groups over a matching period helps you see what happens after the initial sale.

Use that information to make follow-up more relevant. A first-time buyer may need product guidance and a reason to return. A regular customer may appreciate early access to a suitable new range. A previously frequent buyer who has stopped ordering may respond to a reminder that reflects what they used to buy.

Customer acquisition cost, or CAC, answers a different question: how much you spend to acquire a new customer. Revenue CLV alone does not tell you what you can afford to spend on acquisition. If someone is expected to spend $300, spending $300 to win them would leave nothing for the products, delivery or other costs.

For your store, use CLV to identify promising customer groups and retention opportunities. Before increasing an advertising budget, check the costs and how quickly customers place their later orders. Revenue expected over two years is not cash available today.

Customer lifetime value prediction: past spending versus future estimates

Historical customer value describes what customers have already spent. Predicted customer lifetime value estimates what they may spend in the future. Some analytics tools combine past and predicted spending, so check the definition before comparing their results.

The calculator on this page makes a simple projection. It assumes the order value and yearly buying frequency you enter continue for the chosen lifespan. It does not examine individual customers or identify which ones are likely to return.

More advanced predictions can use purchase history, order frequency and gaps between purchases to estimate future spending. For example, Klaviyo distinguishes historic, predicted and total CLV and explains that its predictions are estimates rather than exact outcomes. Prediction windows also matter: expected spending over the next year is different from spending over an entire customer relationship.

You can explore uncertainty using this same calculator. At $50 per order and three orders a year, a two-year lifespan gives $300; three years gives $450; four years gives $600. The extra value comes entirely from the longer assumption. Choose a range your store history can support, then compare your expectations with what customers actually do.

How to increase customer lifetime value in a dropshipping store

The formula gives you three areas to work on: the amount customers spend per order, how often they return and how long they keep buying. Focus on changes that make returning worthwhile, then measure whether they improve repeat sales after costs.

Make the first order worth repeating

For a dropshipping business, start with the experience your supplier delivers. Order samples, check product descriptions against what arrives and set realistic delivery expectations. Review complaints about poor quality, sizing or late parcels. A discount on a second order is unlikely to solve a problem that made the first order disappointing.

Give customers a relevant next purchase

Build a range around the customer’s needs. A buyer of indoor planters might later want matching trays or plant-care accessories. Someone buying a one-off novelty item may have little reason to return for the same product. Choose useful complementary items rather than expanding into unrelated products simply to make the catalog larger.

Time follow-up around the product

Use helpful follow-up for customers who have agreed to receive marketing. Send care or setup advice first, then suggest a suitable next purchase. Replenishment reminders suit products that run out; seasonal recommendations may suit clothing. Check your own time-between-orders data before deciding when to send a reminder.

Make larger orders useful to the buyer

Try a relevant bundle or accessory suggestion, such as a bag with a matching organizer. Explain the benefit clearly. Check the extra supplier and shipping costs, especially when a bundle ships from different suppliers. A bigger basket is only useful to the business if the extra revenue justifies the extra cost.

Test repeat-purchase incentives carefully

A loyalty benefit or targeted offer may give customers a reason to return, but repeated discounts can increase sales while reducing what you keep. Test one change with a comparable customer group and allow enough time for another purchase. Review repeat orders, revenue per customer, refunds and the cost of the offer before rolling it out more widely.

Common questions about customer lifetime value

What is a good customer lifetime value?

There is no single dollar figure that suits every store. A business selling low-cost accessories has different order values and buying patterns from one selling furniture. Compare your own customer groups using the same method, and assess their costs alongside their revenue. A rising CLV is more useful when it reflects sustainable repeat business.

Can I calculate CLV for a new store?

You can build a scenario, but you cannot yet confirm long-term buying behavior. Use the sales data you have, label uncertain inputs as assumptions and try a shorter as well as a longer lifespan. Avoid treating a strong launch month as a normal year. Early repeat purchases and refunds give you evidence to improve the estimate.

How often should I review CLV?

Choose a schedule that matches your buying cycle. A monthly review can help you spot changes in the inputs, while a quarterly comparison may give repeat orders more time to develop. For seasonal stores, compare equivalent seasons. Revisit the estimate after a major change in products, suppliers, pricing or acquisition campaigns.

Put your CLV estimate to work

Start with a realistic estimate, then choose one question to investigate: which customers return, what they buy next or where the buying relationship breaks down. Use the answer to test one improvement. Return to the calculator when you have better inputs and compare the new result with your earlier estimate.

Link to this calculator: Copy the code below and paste it into your page.

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