What is Lifetime Value (LTV)?

Lifetime value (LTV), also called customer lifetime value (CLV or CLTV), is the total value a customer generates for a business over the whole relationship, ideally measured as gross margin rather than revenue. LTV can be measured historically, from what customers have already bought, or predicted for the future, which is called predicted lifetime value (pLTV).

Historical LTV vs. predicted LTV

The term LTV is used for two different numbers, and mixing them up is a common source of confusion:

• Historical LTV is backward-looking: it sums the revenue or margin a customer, or a cohort of customers, has already generated up to today.
• Predicted LTV (pLTV) is forward-looking: it estimates the revenue or margin a customer will generate in the future, usually over a fixed horizon such as 12 or 24 months.

Historical LTV is a fact about the past and is useful for reporting and for comparing cohorts once enough time has passed. Decisions about acquisition spending need the forward-looking estimate, because they have to be made before the customer's future purchases are known. Marketing research on customer value focuses on this forward-looking definition: the expected sum of discounted future margins from a customer (Gupta et al., 2006).

How to calculate lifetime value

Simple historical LTV for a cohort

LTV = Average order value × Number of orders per customer × Gross margin %

Example: customers acquired in January 2025 have placed, on average, 2.4 orders by January 2026 with an average order value (AOV) of 60 EUR and a gross margin of 40%. Their 12-month historical LTV is 2.4 × 60 EUR × 0.40 = 57.60 EUR in gross margin per customer.

Formula-based LTV

A common textbook formula discounts the expected margin from each future period by the probability that the customer is still active (Gupta et al., 2006):

CLV = Σ_t (p_t − c_t) × r_t / (1 + i)^t − AC

where p_t − c_t is the margin in period t, r_t is the probability that the customer is still active, i is the discount rate and AC is the acquisition cost. Some definitions report LTV before subtracting AC, so that it can be compared with CAC.

Subscription vs. ecommerce

In subscription businesses, customers cancel explicitly, so a simplified LTV of average margin per period ÷ churn rate per period is often used. In ecommerce and retail the relationship is non-contractual: customers do not announce when they stop buying, and a customer who has not bought for six months may or may not return. Probabilistic models such as BG/NBD estimate how many future purchases each customer is likely to make from their purchase history, based on recency, frequency and customer age (Fader, Hardie & Lee, 2005). These models are a common basis for pLTV in ecommerce.

Components of LTV

• Average order value: spend per order, affected by assortment, pricing and cross-selling.
• Purchase frequency: how often customers buy, driven by category, seasonality and retention marketing.
• Customer lifespan or retention: how long customers keep buying.
• Gross margin: what is left after product, shipping and returns costs. Revenue-based LTV overstates value in categories with high return rates or heavy discounting.

How LTV is used in ecommerce and retail

• Setting acquisition targets: comparing LTV with CAC shows how much it is worth paying for a new customer. A 3:1 LTV:CAC ratio is a widely repeated rule of thumb from SaaS practice, not a research finding. Ecommerce businesses should set their own target based on margin-based LTV over a stated horizon and their cash-flow needs.
• Comparing cohorts and channels: customers acquired in different periods, through different channels or with different first-order discounts can have very different LTVs.
• Retention and CRM investment: LTV helps prioritize which customer segments to invest in. Customer value has two linked sides: the value a firm delivers to customers and the value customers deliver back to the firm (Kumar & Reinartz, 2016).

LTV in marketing mix modeling

A standard marketing mix model (MMM) measures the incremental sales each channel causes within the model's time window. It does not by itself capture the value of future repeat purchases from customers acquired by a campaign. LTV can be brought into MMM in two ways:

• New vs. returning customers: modeling new-customer sales separately from returning-customer sales shows which channels bring in new customers, and incremental CAC can then be compared with LTV. See how a new vs. returning customer split in MMM results helps to drive both growth and efficiency.
• LTV-weighted KPIs: valuing incremental new customers by their historical or predicted LTV gives a longer-term view of each channel's incremental ROAS (iROAS) than first-order revenue alone.

Both approaches depend on measuring incremental new customers correctly. Attribution-based LTV by channel credits customers who would have bought anyway, so it tends to overstate the value created by channels close to the purchase.

Common challenges

• Using revenue instead of margin: inflates LTV, especially with high return rates.
• Undefined horizon: "lifetime" should be stated as, for example, 12, 24 or 36 months.
• Immature cohorts: historical LTV of recent cohorts is incomplete; compare cohorts at the same age.
• Identity gaps: guest checkouts and offline purchases can split one customer into several, which lowers measured LTV.

Key takeaways

• Historical LTV is backward-looking; predicted LTV (pLTV) is forward-looking. Acquisition decisions need the forward-looking estimate.
• In ecommerce, measure LTV as gross margin over a stated horizon and use models built for non-contractual purchasing.
• Combine LTV with incremental new-customer estimates from MMM or experiments, not with attributed customers.

Related terms

• Predicted lifetime value (pLTV)
• Customer acquisition cost (CAC)
• Average order value (AOV)
• Return on advertising spend (ROAS)
• Incrementality

Further reading on Sellforte

• How a new vs. returning customer split in MMM results helps to drive both growth and efficiency
• How can MMM help optimize ecommerce revenue metrics?
• Why optimize ecommerce profitability

References

1. Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). "Counting your customers" the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2), 275–284. https://doi.org/10.1287/mksc.1040.0098
2. Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139–155. https://doi.org/10.1177/1094670506293810
3. Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36–68. https://doi.org/10.1509/jm.15.0414

Authors

Lauri Potka is the Chief Operating Officer at Sellforte, with over 15 years of experience in Marketing Mix Modeling, marketing measurement, and media spend optimization. Before joining Sellforte, he worked as a management consultant at the Boston Consulting Group, advising some of the world’s largest advertisers on data-driven marketing optimization. Follow Lauri in LinkedIn, where he is one of the leading voices in MMM and marketing measurement.