LTV Components
LTV is a composite metric. Decomposing it into average order value, purchase frequency, customer lifespan, and gross margin reveals which lever actually moves the number — and which ones just feel productive.
LTV Components
The input metrics that compose lifetime value: average order value, purchase frequency, customer lifespan, and gross margin.
LTV components are the underlying variables that, when multiplied together, produce a customer's lifetime value. The standard decomposition is AOV × purchase frequency × customer lifespan × gross margin, with repeat purchase rate sitting underneath frequency as a leading indicator.
Treating LTV as one number hides where the money actually comes from. Decomposing it lets you see whether your lifetime value is built on high basket sizes, loyal repeat buyers, long-tenured customers, or healthy unit economics — and which of those four levers will respond fastest to the work your team has time to do this quarter.
Most stores track LTV as a single rolled-up figure in a dashboard somewhere. That number tells you whether you're trending up or down, but it doesn't tell you what to do on Monday morning.
The four-component view fixes that. Each input — AOV, purchase frequency, customer lifespan, and gross margin — maps to a different team and a different set of experiments. Once you can see them side by side, the next investment usually becomes obvious.
The four inputs that compose LTV
Average order value is the revenue per transaction. It moves through merchandising work: bundles, upsells on the product page, free-shipping thresholds, and price tiering. AOV is the fastest lever to test because every checkout in the next 30 days will reflect the change.
Purchase frequency is how many times an existing customer buys per year. It's driven by lifecycle email, replenishment cadence, loyalty programs, and the underlying consumption rate of the product. Repeat purchase rate is the leading indicator — the share of customers who place a second order — and it predicts where frequency will land six months out.
The margin layer most operators skip
Customer lifespan is how long the relationship lasts before the customer churns or goes dormant. For subscription brands it's a contract length; for apparel and beauty it's an inferred window — typically 18 to 36 months — based on time-since-last-order distributions.
Gross margin (or better, contribution margin) is what converts revenue-LTV into profit-LTV. A €400 revenue customer at 30% margin is worth less than a €250 customer at 65% margin, and any LTV figure that ignores this is just a vanity number you can't divide CAC into.
Watch the cohort-vs-blended trap
Blended LTV across all customers will overstate the value of new acquisition cohorts when your repeat customers are old and loyal. If you're using LTV to set paid acquisition caps, compute it on the cohort you actually acquired in the last 90 days — not the lifetime average that includes 2019 buyers.
Which lever to pull first
Multiplicative metrics reward whichever component is furthest from its ceiling. If your AOV is €55 in a category where competitors sit at €85, a 20% AOV lift is realistic and compounds with every other input. If you're already at category-leading AOV, that same 20% is much harder to win.
For most apparel and beauty stores in the €1M-€15M band, the order of attack is: fix margin first (it's accounting work, not experimentation), then AOV (fastest experimental wins), then purchase frequency (slower but compounds), then lifespan (the longest feedback loop). Reverse that order for subscription or replenishment categories where frequency is the whole game.
Typical LTV uplift achievable per component in 12 months
Frequently asked questions
The most common decomposition is LTV = AOV × purchase frequency × customer lifespan × gross margin. AOV and frequency are observed directly from order data; lifespan is either contractual (subscription) or inferred from cohort retention curves; margin should be contribution margin, not just gross.
Repeat purchase rate is the share of customers who place a second order — a binary outcome per customer. Purchase frequency is the average number of orders per customer in a given window, including those who buy three, four, or ten times. Repeat purchase rate is the leading indicator; frequency is the lagging result.
Contribution margin, whenever you can compute it. Gross margin only nets out COGS, but LTV needs to net out the variable costs of serving that customer — payment processing, fulfilment, returns, and customer support. Contribution margin is typically 10-20 points lower than gross margin and gives you an LTV you can actually divide CAC into.
Fit a retention curve to a cohort of acquired customers and find the point where retention asymptotes near zero — usually 18-36 months for apparel and beauty, 24-48 for home goods. Alternatively, take 1 / (annual churn rate) as a quick approximation, where churn is the share of last-year buyers who didn't return this year.
AOV. Bundle and threshold tests show results within a single billing cycle, and they apply to every customer who checks out — not just repeat buyers. Purchase frequency takes a full purchase cycle to read out, and lifespan changes only show up after a year of cohort data.
Not necessarily. If higher AOV comes from heavier discounting on bundles, margin drops and net LTV can fall. And if the AOV lift comes from one-time gift purchases, it inflates the first order but doesn't compound through frequency. Always check the margin-adjusted LTV, not just the revenue figure.
LTV ÷ CAC is the unit economics ratio that determines whether your acquisition is profitable. A 3:1 ratio is the rough industry rule, but it only holds when LTV is calculated on contribution margin and on the same cohort you spent CAC to acquire. Blended LTV across legacy customers makes new-cohort CAC look better than it is.
Monthly for AOV and repeat purchase rate (they react quickly to merchandising and lifecycle changes). Quarterly for purchase frequency and contribution margin. Annually for customer lifespan, since the underlying cohort data takes a year to mature meaningfully.
Usually because the blended top-line LTV was carrying weight from a small group of high-loyalty customers. When you decompose by cohort or by acquisition channel, you see the median customer is closer to one to two purchases — and the long-tail of repeat buyers was hiding that. This is the right number to plan against.
Not in any reasonable timeframe — LTV is a 12-to-24-month outcome. Run experiments on the components instead: AOV tests on bundle pages, frequency tests on post-purchase flows, repeat purchase rate tests on the second-order email sequence. Each component test reads out in weeks and rolls up into LTV over quarters.
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