Contribution LTV D90
$96
Revenue $157 · Gross profit $113
Unit economics & growth scenario model
LTV, CAC & Payback Period Calculator
Model contribution LTV, LTV-to-CAC, CAC payback, retention and required growth improvements through day 180 — before you commit budget or change the growth plan.
Example decision
Hold and optimize
CAC is recovered, but the selected growth targets are not reached.
Contribution LTV D180
$140
Discounted $137
Modeled payback
Day 135
Discounted: Day 139
LTV / CAC D180
1.17x
Target 3.0x
ROI D180
17%
D90 ROI -20%
D180 retention
36.0%
Cumulative churn 64.0%
Scenario contribution forecast
Cumulative contribution compared with the CAC threshold.
Targets and required improvements
Indicative requirements assuming all other inputs remain unchanged.
Maximum CAC for 3.0x LTV/CAC
$47
Based on D180 contribution LTV.
Maximum CAC for payback by D90
$96
Based on cumulative contribution by the selected day and every later checkpoint.
Minimum recurring revenue
$171
Current: $55
Minimum retention uplift
Not feasible
Applied to each retention checkpoint.
Combined maximum allowable CAC: $47. This is the stricter of the LTV/CAC and payback constraints.
Scenario comparison
Stress-test the economics before increasing acquisition spend.
Scenario assumptions
Base: entered values unchanged.
Conservative: revenue ×0.95, retention ×0.90, costs ×1.10.
Optimistic: revenue ×1.05, retention ×1.10 (capped at 100%), costs ×0.90.
Conservative
$122
Contribution LTV D180
LTV / CAC1.01x
Modeled paybackDay 175
ROI D1801%
Base
$140
Contribution LTV D180
LTV / CAC1.17x
Modeled paybackDay 135
ROI D18017%
Optimistic
$161
Contribution LTV D180
LTV / CAC1.34x
Modeled paybackDay 109
ROI D18034%
Base scenario breakdown
Expected values per originally acquired customer.
Swipe horizontally to view all model metrics.
| Period | Start retention | End retention | Avg. active | Revenue | Gross profit | One-time cost | Variable cost | Contribution | Discounted | Cumulative LTV |
|---|---|---|---|---|---|---|---|---|---|---|
| D30 | 100.0% | 78.0% | 89.0% | $85.00 | $61.20 | $4.00 | $5.34 | $51.86 | $51.46 | $51.86 |
| D60 | 78.0% | 64.0% | 71.0% | $39.05 | $28.12 | $0.00 | $4.26 | $23.86 | $23.49 | $75.72 |
| D90 | 64.0% | 54.0% | 59.0% | $32.45 | $23.36 | $0.00 | $3.54 | $19.82 | $19.36 | $95.54 |
| D120 | 54.0% | 47.0% | 50.5% | $27.78 | $20.00 | $0.00 | $3.03 | $16.97 | $16.44 | $112.51 |
| D150 | 47.0% | 41.5% | 44.3% | $24.34 | $17.52 | $0.00 | $2.66 | $14.87 | $14.30 | $127.38 |
| D180 | 41.5% | 36.0% | 38.7% | $21.31 | $15.34 | $0.00 | $2.32 | $13.02 | $12.42 | $140.40 |
AI Growth Analysis
Receive an executive interpretation of the model, risks, value drivers and recommended actions. Your assumptions are validated and all metrics are recalculated on the server before the report is generated.
Methodology
Revenue
D0–D30 revenue is entered directly. Later revenue equals recurring revenue multiplied by average active share.
Contribution
Gross profit minus one-time customer cost and expected active-customer variable costs.
Discounted LTV
Each period contribution is discounted using an annual rate converted to a 30-day equivalent.
Reverse calculation
Numerical solvers estimate the minimum recurring revenue or retention uplift needed to reach both selected targets.
This is a deterministic scenario-based model, not a trained machine-learning forecast. Its outputs become stronger when assumptions are derived from actual cohort data.
Disclaimer
Results are provided for informational and analytical purposes only. They are based on user-provided data and simplified assumptions and do not constitute financial, legal, tax, investment, or other professional advice. Forecasts and AI-generated recommendations do not guarantee future business performance. Read the full Disclaimer
Growth Tools ecosystem
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From the practice
Turn this model into a plan
The Predictive LTV & Payback Calculator gives you a structured read on unit economics. Turning it into a funded plan is what the Lazarevych growth-analytics practice does next.
Service
Explore Unit Economics & Growth Strategy
Convert CAC, LTV and payback outputs into a funded growth plan.
Case study
Read: CAC, LTV & Payback Optimization
See how payback and LTV improvements were delivered in practice.
Insight
Read: CAC, LTV and Payback Period
Deepen unit-economics literacy around the model outputs.
Need expert support? Turn the result into a scoped analytics, measurement, CRO or unit-economics workstream.
Discuss Implementation with MaksymCustomer unit economics guide
How to use an LTV, CAC and payback calculator for growth decisions
This customer lifetime value calculator connects three decisions that are often modeled separately: how much contribution a customer can generate, whether that value supports the current customer acquisition cost, and how quickly CAC is recovered. Use it to compare LTV-to-CAC, CAC payback period and retention scenarios before increasing acquisition spend, changing pricing or investing in retention.
Customer lifetime value (LTV)
Estimate contribution LTV from customer revenue, gross margin, retention and service costs. The model reports both D90 and D180 value so you can see how customer economics develop over time instead of relying on one lifetime-value number.
LTV-to-CAC ratio
Compare contribution LTV with customer acquisition cost and set a target LTV:CAC ratio that reflects your own margin, cash and growth constraints. The reverse calculation shows the maximum CAC supported by the selected target.
CAC payback period
Measure how many days it takes cumulative contribution to recover CAC. Payback complements LTV:CAC because a customer can look profitable over time while still consuming too much cash during the acquisition and recovery period.
How the customer LTV and CAC model works
Start with customer acquisition cost, first-period revenue, recurring revenue and gross margin. Then add the retention curve that best represents the share of the original cohort still active at D30, D60, D90, D120 and D180. The calculator converts those assumptions into period-level revenue, gross profit and contribution.
The base case is compared with conservative and optimistic scenarios so you can see whether the decision is robust to weaker or stronger customer behavior. Instead of stopping at a headline LTV number, the model also calculates LTV:CAC, predicted CAC payback, ROI, maximum allowable CAC and the recurring-revenue or retention improvement required to reach the targets you set.
This makes the tool useful for questions such as whether a SaaS acquisition channel can scale without extending payback, whether ecommerce repeat-purchase economics support a higher CAC, or whether improving retention would create more value than increasing top-of-funnel spend.
LTV:CAC ratio vs. CAC payback period
LTV:CAC describes the relationship between customer value and acquisition cost. CAC payback describes the speed at which that acquisition cost is recovered. The two metrics answer different questions, so using both provides a stronger view of unit economics than optimizing either metric in isolation.
A high LTV:CAC ratio can still be uncomfortable if payback is slow and working-capital requirements are high. A very fast payback period can also hide weak long-term retention. The calculator therefore evaluates value, recovery speed and retention together.
Revenue LTV vs. contribution LTV
Revenue-based lifetime value is useful for describing how much a customer buys, but it can exaggerate the amount available to recover acquisition cost. This model uses contribution LTV: revenue is adjusted for gross margin, one-time customer costs and ongoing variable costs before it is compared with CAC.
For acquisition and budget decisions, contribution LTV is usually the more conservative lens because it connects customer value to the economics that actually fund growth.
Related unit-economics resources
Use the calculator with the supporting methodology, service and case-study material when you need to move from a model to an acquisition or growth decision.
CAC, LTV & payback guide
Learn how CAC, customer lifetime value and payback period work together in growth planning.
Unit Economics & Growth Strategy
Turn LTV, CAC, margin and payback into acquisition and scaling decisions.
CAC, LTV & payback case study
See how measurement and customer economics can change growth allocation decisions.
LTV, CAC and payback calculator FAQ
What is customer lifetime value (LTV)?
Customer lifetime value estimates the economic value a customer generates over a defined relationship or forecast horizon. This calculator focuses on contribution LTV, so it accounts for gross margin, one-time customer costs, variable servicing costs and retention rather than using revenue alone.
How do you calculate the LTV-to-CAC ratio?
The LTV-to-CAC ratio compares customer lifetime value with customer acquisition cost. A 3.0x ratio means the modeled contribution LTV is three times CAC. The right target depends on margin, payback speed, retention, cash constraints and the maturity of the business, so the calculator lets you set your own target instead of assuming one universal benchmark.
What is CAC payback period?
CAC payback period is the time required for cumulative customer contribution to recover acquisition cost. A shorter payback period generally releases cash sooner and can make growth easier to finance, while a long payback period can create capital pressure even when lifetime value looks attractive.
Why use contribution LTV instead of revenue LTV?
Revenue LTV can overstate customer economics because not every dollar of revenue contributes equally to recovering CAC. Contribution LTV deducts the costs needed to serve and support the customer, which makes it more useful for acquisition, pricing and growth-budget decisions.
Can I use this LTV and CAC calculator for SaaS or ecommerce?
Yes. The model includes illustrative SaaS, ecommerce and FinTech presets, and every assumption can be replaced with your own data. The presets are starting points for scenario analysis, not market benchmarks.
Is this a machine-learning LTV prediction model?
No. It is a deterministic, scenario-based customer economics model. It projects contribution LTV, LTV-to-CAC and CAC payback from the assumptions you enter. The outputs become more useful when retention, revenue, margin and cost assumptions come from actual cohort data.