"My executive team wants LTV in our monthly report. But there are like four different formulas online — how do I pick the right one?"
This is one of the most common questions we hear from EC operators. LTV (Customer Lifetime Value) is widely used as a metric, but at least five common calculation methods exist. Pick the wrong one, and your business decisions rest on a shaky foundation.
TL;DR
- There are five common LTV calculation methods: Simple, Gross Margin, Cohort, LTV/CAC, and DCF. Choose by business stage and product
- LTV alone cannot drive investment decisions. View it together with CAC, with LTV/CAC = 3:1 as a baseline
- The three prerequisite metrics are AOV, RPS, and purchase frequency. Without stable measurement of these, LTV figures lack foundation
Why "five methods" matters
LTV looks deceptively simple. The textbook formula is:
LTV = AOV × Purchase Frequency × Customer Lifespan
But ask any operator running a real Shopify store, and you'll get a different number depending on what they include — gross margin, cohort retention, CAC, future cash flow discount. These produce different LTVs by 2-3x for the same business. The question is not "which formula is correct" but "which formula fits this stage of the business."
The five methods at a glance
1. Simple LTV — early-stage EC
LTV = AOV × Purchase Frequency × Customer Lifespan
The simplest formula, also presented in Shopify's official documentation. AOV ¥5,000 × 3 orders/year × 2 years = LTV ¥30,000. Sufficient when gross margin and customer ID linkage aren't yet structured. Note: revenue-based, so margin differences are not reflected.
2. Gross Margin LTV — scale-up EC
LTV = (AOV × Gross Margin) × Orders × Years
Once you start serious ad investment, profit-based LTV is essential. Otherwise you hit the "LTV looks fine but no profit" trap. With 30% gross margin: ¥9,000 gross-margin LTV. Translation: keep CAC under ¥9,000.
3. Cohort LTV — repeat-rate driven
LTV = Cohort cumulative revenue ÷ Cohort customers
Highest accuracy because it's based on observed values. Customer ID linkage is mandatory. Bain & Company has long noted that retention improvements have outsized impact on profit, and cohort-level visibility directly drives continuous improvement.
4. LTV/CAC ratio — investment decision
LTV/CAC = LTV ÷ CAC (baseline: 3:1)
Strictly speaking, this is not a formula for calculating LTV — it's an investment-decision lens using the LTV/CAC ratio. The four formulas for LTV calculation proper are 1, 2, 3, and 5 here.
The "LTV/CAC = 3:1" baseline widely used in SaaS also applies to EC. Below 1 = unprofitable acquisition, above 3 = room to scale spending. The appropriate ratio varies by industry and product, so use your own channel-level actuals.
5. DCF LTV — high-AOV / subscription EC
LTV = Σ(Annual CF / (1 + r)^n)
Discounts future cash flows to present value. Used for subscription EC and high-AOV products in 3-5-year investment decisions. Discount rate (typically 5-10%) heavily influences output, so document your assumptions explicitly.
The three-tier framework — AOV → RPS → LTV
All five methods share a common assumption: that AOV and purchase frequency are already being measured stably. Without that foundation, LTV figures lack reliability.
The three prerequisite metrics:
| Metric | Unit | Role | Relationship to LTV |
|---|---|---|---|
| AOV | per order | Order efficiency | Starting point of LTV formula |
| RPS | per session | Revenue per visit | Acquisition efficiency that creates LTV |
| CAC | per customer | Acquisition cost | Denominator of LTV/CAC ratio |
In practice: build a state where AOV and RPS are measured monthly with stability, then compute LTV quarterly. There's no need to view LTV daily — but AOV and RPS should be visible every day.
LTV/CAC investment decision zones
| LTV/CAC ratio | State | Recommended action |
|---|---|---|
| Below 1 | New acquisition is loss-making | Pause ads, or improve product first |
| 1-2 | Recoverable but thin margin | Decompose by channel, cut high-CAC channels |
| 2-3 | Healthy range | Maintain + improve AOV/CVR to lift the ratio |
| Above 3 | Room to scale spending | Increase ad budget, open new channels |
Critical caveat: this baseline must be viewed at channel and cohort level. Even if total LTV/CAC = 3, if Paid sits at 0.8 and Organic at 5.0, the right call is to pause Paid acquisition. Averages mislead.
Four common measurement pitfalls
| Pitfall | What happens | Treatment |
|---|---|---|
| One-time customers | Single-purchase customers drag down the average | Split "first purchase only" vs "repeat" |
| Fixed measurement period | Fixing lifespan to 3 years undervalues new customers | Use observed lifespan months by cohort |
| Pre/post discount mixing | Heavy coupon usage inflates AOV | Match AOV practice — use post-discount |
| Channel allocation | Mixing ad-acquired with Organic customers | Split cohorts by initial-touch channel |
Operating LTV alongside its prerequisite metrics
The summary:
- LTV has five common calculation methods. Choose by business stage and product
- LTV alone does not enable investment decisions. Use LTV/CAC = 3:1 as the baseline
- The three prerequisite metrics are AOV / RPS / Purchase Frequency
- Realistic operation: LTV quarterly, AOV and RPS visible daily
- Decompose LTV/CAC by channel and cohort — averages mislead
I'm building RevenueScope, a tool that automatically expands the prerequisite metrics — AOV, RPS, CVR — by channel and device on the dashboard. It doesn't compute LTV directly, but it's designed to surface "the data foundation underneath LTV" — the kind of view that's missing when teams jump straight to LTV reporting.
Discussion
What LTV formula does your team currently use, and what tripped you up the first time? Curious to hear from other operators — especially the "looked good in the spreadsheet but didn't survive contact with reality" stories.