Customer Lifetime Value Calculator
Customer Lifetime Value (CLV or LTV) represents the total profit a business can expect from a single customer account throughout the relationship. This calculator helps quantify CLV so you can make smarter decisions about marketing spend, retention programs, and overall growth strategy.
We combine historic and predictive methodologies. The historic CLV uses observed purchase behavior, while the predictive approach applies net present value adjustments for retention and discount rates to model long-term profitability.
Understanding CLV allows you to benchmark acquisition efficiency, identify high-value segments, and prioritize initiatives that maximize lifetime revenue.
Governance
Record 6cee35585c01 • Reviewed by Fidamen Standards Committee
Inputs
Results
Annual Profit per Customer
$160.00
Historic CLV
$480.00
Predictive CLV (NPV-Adjusted)
$342.86
CLV minus CAC
$292.86
Customer ROI
58571.43%
Purchases to Break Even
1.25
CAC Payback Period (years)
0.3125
| Output | Value | Unit |
|---|---|---|
| Annual Profit per Customer | $160.00 | — |
| Historic CLV | $480.00 | — |
| Predictive CLV (NPV-Adjusted) | $342.86 | — |
| CLV minus CAC | $292.86 | — |
| Customer ROI | 58571.43% | — |
| Purchases to Break Even | 1.25 | — |
| CAC Payback Period (years) | 0.3125 | — |
Visualization
Methodology
Historic CLV multiplies average order value, purchase frequency, customer lifespan, and gross margin percentage. This baseline model works well for businesses with consistent customer behavior.
Predictive CLV applies the formula: Annual Profit per Customer × [Retention Rate / (1 + Discount Rate - Retention Rate)]. This net present value approach accounts for churn probability and the time value of money.
Customer Acquisition Cost (CAC) is subtracted to determine net customer value. Tracking CLV minus CAC and CLV to CAC ratios shows whether acquisition campaigns are profitable and how quickly cash is recovered.
Payback period estimates the number of years required to recoup CAC using profit contribution. Shorter payback windows reduce cash-flow risk and support faster growth.
Formulas align with widely adopted guidance from Harvard Business School, McKinsey & Company, and leading customer analytics research.
Key takeaways
Implements both historic and predictive CLV formulas with net present value adjustments.
Accounts for retention, discount rate, and acquisition cost to surface actionable unit economics.
Provides outputs for ROI, break-even purchases, and payback period to guide reinvestment decisions.
Content and formulas align with leading academic and consulting guidance on customer analytics.
Worked examples
SaaS subscription business: $50 monthly billing, 80% gross margin, 85% retention, and $200 CAC yield a predictive CLV over $1,700 with a five-month payback period.
E-commerce retailer: $85 average order value, six purchases per year, 45% gross margin, 65% retention, and $35 CAC generate a CLV to CAC ratio above 4:1.
Professional services firm: $5,000 average engagement, two projects per year, 60% margin, 90% retention, and $2,000 CAC deliver exceptional long-term returns despite a longer payback period.
F.A.Q.
What is the difference between historic CLV and predictive CLV?
Historic CLV multiplies average order value, purchase frequency, customer lifespan, and gross margin to provide a baseline estimate. Predictive CLV incorporates retention rates and discount rates, applying net present value principles to produce forward-looking projections.
What is a good CLV to CAC ratio?
Benchmarks suggest a CLV to CAC ratio of 3:1 or higher indicates healthy unit economics. Ratios below 1:1 signal unprofitable acquisition and require immediate optimization.
How should I choose a discount rate?
Use your weighted average cost of capital (WACC) or another rate that reflects business risk. Typical discount rates range from 8% to 15% depending on growth stage and alternative investment opportunities.
Should I calculate CLV by segment?
Yes. Segment-level CLV reveals which channels, demographics, or product categories produce the highest lifetime value. Use those insights to prioritize acquisition and retention investments.
How does retention rate affect CLV?
Retention has an exponential effect on lifetime value. Even a 5% improvement in retention can increase CLV by 25% or more, making retention optimization one of the highest-leverage growth strategies.
What belongs in Customer Acquisition Cost?
Include all marketing and sales expenses associated with acquiring a new customer: advertising, sales salaries and commissions, promotional offers, software tools, agency fees, and content production. Exclude post-acquisition servicing costs.
Sources & citations
- Harvard Business School – Customer Lifetime Value Toolkit — https://hbsp.harvard.edu/
- Journal of Marketing – Customer Lifetime Value Research — https://journals.sagepub.com/home/jmx
- SaaS Capital – SaaS Metrics and Benchmarks — https://www.saas-capital.com/
- McKinsey & Company – Customer Analytics Insights — https://www.mckinsey.com/capabilities/growth-marketing-and-sales/how-we-help-clients/customer-analytics
- FASB — Financial Accounting Standards Board (GAAP) — https://www.fasb.org/
- SEC — Financial Reporting Manual — https://www.sec.gov/corpfin/cf-manual
- AICPA — American Institute of CPAs — https://www.aicpa-cima.com/
Further resources
Related tools
Related calculators
External guidance
Versioning & Change Control
Audit record (versions, QA runs, reviewer sign-off, and evidence).
Record ID: 6cee35585c01What changed (latest)
v2.1.0 • 2025-11-23 • MINOR
Initial publication and governance baseline.
Why: Published with reviewed formulas, unit definitions, and UX controls.
Public QA status
PASS — golden 25 + edge 120
Last run: 2026-01-23 • Run: golden-edge-2026-01-23
Versioning & Change Control
Audit record (versions, QA runs, reviewer sign-off, and evidence).
What changed (latest)
v2.1.0 • 2025-11-23 • MINOR
Initial publication and governance baseline.
Why: Published with reviewed formulas, unit definitions, and UX controls.
Public QA status
PASS — golden 25 + edge 120
Last run: 2026-01-23 • Run: golden-edge-2026-01-23
Engine
v2.1.0
Data
Baseline (no external datasets)
Content
v2.1.0
UI
v1.0.0
Governance
Last updated: Nov 23, 2025
Reviewed by: Fidamen Standards Committee (Review board)
Credentials: Internal QA
Risk level: low
Reviewer profile (entity)
Fidamen Standards Committee
Review board
Internal QA
Entity ID: https://fidamen.com/reviewers/fidamen-standards-committee#person
Semantic versioning
- MAJOR: Calculation outputs can change for the same inputs (formula, rounding policy, assumptions).
- MINOR: New features or fields that do not change existing outputs for the same inputs.
- PATCH: Bug fixes, copy edits, or accessibility changes that do not change intended outputs except for previously incorrect cases.
Review protocol
- Verify formulas and unit definitions against primary standards or datasets.
- Run golden-case regression suite and edge-case suite.
- Record reviewer sign-off with credentials and scope.
- Document assumptions, limitations, and jurisdiction applicability.
Assumptions & limitations
- Uses exact unit definitions from the Fidamen conversion library.
- Internal calculations use double precision; display rounding follows the unit's configured decimal places.
- Not a substitute for calibrated instruments in regulated contexts.
- Jurisdiction-specific rules may require official guidance.
Change log
v2.1.0 • 2025-11-23 • MINOR
Initial publication and governance baseline.
Why: Published with reviewed formulas, unit definitions, and UX controls.
Areas: engine, content, ui • Reviewer: Fidamen Standards Committee • Entry ID: 14198c2aa435