Customer lifetime value (LTV, or CLV) is the total revenue you expect to receive from a customer over the entire duration of their relationship with your business.
It’s the answer to: what is a customer actually worth? Not just their first order, but all their orders, renewals and upgrades over time. Whatever product or service you sell, LTV shapes decisions about how much you can afford to spend acquiring customers, which customers to prioritise for retention, and whether a pricing change will help or hurt in the long term.
Key takeaways
- LTV = average order value × purchase frequency × average customer lifespan
- LTV is most useful as a ratio against customer acquisition cost (CAC), the LTV:CAC ratio is a core health metric
- For subscription businesses, LTV is primarily determined by churn rate
- There’s no single customer lifetime value formula. Historic CLV looks backward at what happened, predictive CLV uses a prediction model to estimate what’s coming
- A small share of high value customers usually drives a disproportionate share of revenue, so segment before you average
- Improving LTV is usually cheaper than increasing acquisition volume
How customer lifetime value (CLV) is calculated
The customer lifetime value formula for ecommerce:
LTV = average order value × purchase frequency × average customer lifespan
Example: a WooCommerce store where customers spend €65 per order on average, buy 3 times per year and stay customers for 2.5 years on average.
LTV = €65 × 3 × 2.5 = €487.50
For subscription businesses, the customer lifetime value formula simplifies:
LTV = average monthly revenue per customer ÷ monthly churn rate
Example: customers pay €29/month and monthly churn is 3.5%. LTV = €29 ÷ 0.035 = €829
Both are approximations. Real CLV calculations often adjust for gross margin (so you’re measuring profit, not revenue) and apply a discount rate to account for the time value of future revenue. For most small businesses, the simpler formula is accurate enough to be directionally useful.
One thing I see store owners get wrong constantly: they run this calculation once, file it away and never touch it again. Purchase frequency and lifespan shift with your pricing, your product mix and the season. Recalculate quarterly, not once a year.
Historic CLV vs predictive CLV
The formulas above are historic CLV. They sum up what customers have already done and turn it into an average. Useful, but backward-looking. It tells you what a customer was worth.
Predictive CLV asks a different question: what will this customer be worth? Instead of averaging past transactions, a prediction model reads early signals (recency, purchase pace, category, first-order size) and estimates where a given customer or cohort is heading before that revenue actually arrives.
Both are legitimate CLV calculations. They answer different questions. Historic CLV is the number you report. Predictive CLV is the number you act on, since it lets you flag a likely high value customer in their first month rather than discovering it two years later.
Which CLV prediction model do you actually need?
CLV prediction ranges from a spreadsheet formula to full machine learning, and most businesses are reaching for far more sophistication than they need.
Heuristic formulas. The average order value × frequency × lifespan calculation above. Directionally accurate, easy to explain to anyone on your team, and genuinely good enough for most small to mid-sized WooCommerce or EDD stores.
Probabilistic models. Approaches like BG/NBD and Pareto/NBD model purchase timing and drop-off as probability distributions rather than a single average. They’re built for businesses with irregular, non-contractual purchase patterns, think a store where customers buy at unpredictable intervals, and they’re a meaningful step up in accuracy over a flat heuristic.
Machine learning models. Trained on transaction history, browsing behavior and engagement signals to predict individual-level value. This is where the “prediction model” conversation usually lands in enterprise contexts, and it’s genuinely powerful at scale. It also needs a lot of clean historical data to train on, which most stores under a few thousand customers simply don’t have yet.
In practice, jumping straight to a fancy model before your basic tracking is solid is a classic case of solving the wrong problem. If you don’t yet know your repeat purchase rate or your average order value by segment, fix that first. A sophisticated prediction model built on messy input data will hand you a confident, wrong number.
LTV vs CAC: the ratio that matters most
LTV on its own is just a number. Its real usefulness is in relation to your customer acquisition cost (CAC), how much you spent (in marketing, ads and sales) to acquire a customer.
The LTV:CAC ratio is one of the most-cited benchmarks in subscription and ecommerce:
| LTV:CAC ratio | What it suggests |
|---|---|
| Below 1:1 | You’re losing money on every customer |
| 1:1 to 2:1 | Breaking even or barely profitable, not sustainable |
| 3:1 | Generally considered healthy |
| 5:1+ | Strong unit economics, room to invest in growth |
A 3:1 ratio means you earn three times what you spent to acquire a customer. This leaves room for overhead, product development and reinvestment while remaining profitable.
If your LTV:CAC is below 2:1, you have two levers: increase LTV (retain customers longer, increase purchase value) or decrease CAC (improve targeting, conversion rates, organic channels).
What drives CLV
CLV is a product of three factors, any of which can be improved:
Average order or subscription value. Increasing what customers spend per transaction directly raises LTV. Upsells, bundles and premium tier conversions all move this number. See average order value for how to track and improve it.
Purchase frequency. For non-subscription ecommerce, how often customers come back matters as much as what they spend each time. Email marketing, loyalty programmes and relevant re-engagement campaigns drive repeat purchase behavior.
Customer lifespan (or inverse: churn rate). For subscription businesses, this is the biggest lever. A customer who stays for 3 years is worth roughly three times one who stays for 12 months. Reducing monthly churn by 1 percentage point can meaningfully increase LTV across your entire customer base.
Finding your high value customers
Averages hide the customers who matter most. A store-wide CLV of €200 might mean every customer is worth roughly €200, or it might mean most customers are worth €80 while a small group is worth €900. Those are completely different businesses to run.
This is where customer behavior becomes more useful than a single formula. A few signals worth segmenting on:
- Time between first and second purchase. Customers who come back quickly are far more likely to become high value customers than ones who take months.
- Order frequency relative to product type. A consumables buyer with a 6-week reorder cycle behaves nothing like a one-time, high-ticket buyer, and averaging them together buries both patterns.
- Category and channel. Customers acquired through organic search or referral often show different repeat behavior than customers who arrived via a discount-driven ad campaign.
- Response to past offers. Customers who’ve previously upgraded, added to an order or referred someone tend to keep doing so.
Once you can see which customers behave this way, you can treat them differently: earlier access to new products, a different email cadence, or simply not wasting a discount code on someone who was already going to buy at full price.
Burst Pro’s WooCommerce and EDD analytics show revenue, repeat purchases and your most profitable products side by side inside WordPress, so you can spot these behavior patterns without exporting data to a separate tool.
LTV for WooCommerce stores
In ecommerce, LTV calculations need to account for the irregular purchase patterns of different customer segments.
A customer who buys consumable products (coffee, skincare, supplements) on repeat has a very different LTV profile than one who buys a high-ticket item once. Calculating LTV by product category or customer segment is more useful than a single store-wide average.
Practical things to measure:
- Average first-order value vs subsequent order value (repeat buyers often spend more)
- Time between first and second purchase (the strongest predictor of whether a customer will buy again)
- Percentage of customers who make a second purchase within 60 days (a useful activation benchmark)
LTV for WooCommerce subscription products
For WooCommerce Subscriptions or Easy Digital Downloads, LTV is directly tied to how long subscribers stay active. At a churn rate of 3% per month, the average subscriber stays for roughly 33 months. At 6%, they stay about 17 months.
The formula LTV = monthly ARPU ÷ churn rate makes this concrete: if you’re charging €49/month and average churn is 4%, LTV = €49 ÷ 0.04 = €1,225. Cut churn to 2% and LTV nearly doubles to €2,450, without changing pricing at all.
Burst Pro’s subscription analytics show subscriber retention, cancellation trends and revenue metrics alongside each other inside WordPress. You can track how changes to onboarding, pricing or product affect the underlying retention numbers that determine LTV.
FAQs
Nothing, they’re the same metric, just abbreviated differently. LTV (lifetime value) and CLV (customer lifetime value) are used interchangeably. Some contexts use CLTV. All three mean the same thing.
For strategic decisions, profit-based LTV (which subtracts cost of goods sold and other direct costs) is more accurate. Revenue-based LTV is simpler to calculate and useful for directional comparisons. Be consistent when comparing LTV across time or segments, and be clear which version you’re reporting.
Yes, significantly. A subscription box, a one-time high-ticket purchase and a consumable product all produce distinct lifetime value profiles, even at the same average order value. If your catalog mixes product types, calculate CLV per product line or segment rather than one blended number. A single figure across a mixed product or service lineup usually hides more than it reveals.
Use your best estimates as inputs and revisit as data accumulates. For churn-based LTV, you can use early cohort retention data. If customers who signed up 3 months ago are still 85% active, extrapolate forward with some caution. Early LTV estimates are less accurate but still useful for setting initial acquisition spend limits and pricing decisions.
Focus on the biggest lever first. For subscription businesses, that’s almost always retention (reducing churn). For ecommerce, it’s usually driving the second purchase. Customers who buy twice are far more likely to buy a third time. Identify where in the customer journey you’re losing people and work backwards from there.
Know what your customers are actually worth
LTV tells you how much a customer relationship is worth over time. Burst Pro tracks subscription revenue, retention and purchase behavior inside WordPress, without sending your customer data anywhere.
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Related definitions: what is churn rate, what is MRR and what is average order value. For the bigger picture on tracking revenue in WooCommerce, see Burst for WooCommerce.
