LTV:CAC Ratio 2025: Avoiding False Positive...

LTV:CAC ratio is the most celebrated metric in SaaS—and arguably the most dangerous when calculated incorrectly. A 2024 OpenView survey found that 55% of SaaS companies report LTV:CAC ratios above 3:1, but when audited using consistent methodology, only 35% actually achieve that threshold. The remaining 20% have "false positive" ratios inflated by calculation errors that make unit economics appear healthier than reality. These false positives aren't just embarrassing when sophisticated investors dig into the numbers—they lead to catastrophic capital allocation decisions. A company believing they have 5:1 LTV:CAC when the true ratio is 2:1 will massively overspend on customer acquisition, burning runway on a growth strategy that actually destroys value. False positives typically stem from four sources: overestimated LTV (using overly optimistic retention, expansion, or gross margin assumptions), underestimated CAC (excluding costs that should be included), timing mismatches (comparing LTV from mature cohorts to CAC from recent spend), and cohort selection bias (cherry-picking best customers). This comprehensive guide identifies the most common false positive errors, explains why they occur, shows you how to detect them in your own calculations, and provides correct methodology for accurate LTV:CAC that reflects actual business economics. Whether you're preparing for fundraising or making go-to-market investment decisions, this guide ensures your LTV:CAC tells the truth.

The Correct LTV:CAC Formula

Before identifying false positives, establish the correct LTV:CAC calculation methodology that serves as the standard against which errors can be recognized.

The Standard LTV Formula

Lifetime Value measures the total gross profit a customer generates over their entire relationship. Standard formula: LTV = ARPA × Gross Margin × Customer Lifetime. Where: ARPA (Average Revenue Per Account) = total recurring revenue / number of customers. Gross Margin = (Revenue - COGS) / Revenue, expressed as a decimal. Customer Lifetime = 1 / Churn Rate (for monthly churn, this gives lifetime in months). Example: ARPA: $500/month. Gross Margin: 80%. Monthly Churn Rate: 2%. Customer Lifetime = 1 / 0.02 = 50 months. LTV = $500 × 0.80 × 50 = $20,000. Critical constraint: All components must be consistent—if using monthly ARPA and monthly churn, lifetime is in months. Annual metrics require annual churn. Mixing monthly and annual figures produces nonsense.

The Standard CAC Formula

Customer Acquisition Cost measures the total cost to acquire a new customer. Standard formula: CAC = Total Sales & Marketing Costs / Number of New Customers Acquired. Fully-loaded CAC includes: Sales team compensation (base, commission, bonus, benefits). Marketing spend (paid, content, events, tools). Sales operations and enablement costs. Marketing operations costs. Allocated overhead (office space, equipment for S&M teams). Example: Total S&M spend: $500,000. New customers acquired: 100. CAC = $500,000 / 100 = $5,000. The ratio: LTV:CAC = $20,000 / $5,000 = 4:1. This 4:1 ratio means you generate $4 of gross profit for every $1 spent acquiring a customer—strong unit economics that support aggressive growth investment.

Benchmark Interpretation

LTV:CAC ratio interpretation depends on context, but general SaaS benchmarks provide guidance. Below 1:1: Unsustainable—you lose money on every customer. Requires immediate action. 1:1 to 2:1: Marginal—barely sustainable. Growth investment risky. 2:1 to 3:1: Acceptable—sustainable but not efficient. Conservative growth appropriate. 3:1 to 5:1: Good—efficient unit economics. Supports aggressive growth. Above 5:1: Excellent—but question if you're underinvesting in growth. May be leaving revenue on table. The "3:1 rule" suggests healthy SaaS companies should target 3:1 or better. However, this benchmark assumes accurate calculation—false positives that inflate ratios lead to misplaced confidence and poor decisions.

The CAC Payback Complement

LTV:CAC alone is insufficient—CAC payback period provides essential context. CAC Payback = CAC / (ARPA × Gross Margin). Using our example: CAC Payback = $5,000 / ($500 × 0.80) = $5,000 / $400 = 12.5 months. This means you recover your acquisition investment in 12.5 months. A 4:1 LTV:CAC with 12-month payback is excellent. But 4:1 LTV:CAC with 36-month payback indicates high churn risk—you need customers to survive 3 years to realize that LTV. Always report LTV:CAC alongside CAC payback. A ratio without payback context is incomplete and potentially misleading.

The Unit Economics Test

Your LTV:CAC ratio answers: "For every dollar we spend acquiring customers, how many dollars of gross profit do we generate?" If your answer seems too good to be true (consistently above 6:1 without exceptional market position), you likely have calculation errors inflating the ratio.

LTV Overestimation Errors

The most common source of false positives is inflated LTV calculations that overestimate customer value.

Mistake: Using Revenue Instead of Gross Profit

The error: Calculating LTV using revenue rather than gross profit (revenue minus cost of goods sold). Why it happens: Revenue is simpler to track, and some companies don't accurately allocate COGS to subscriptions. The impact: Overstates LTV by 15-40% depending on your gross margin. A company with 75% gross margin using revenue instead of gross profit inflates LTV by 33%. Example: True LTV = $500 ARPA × 0.75 GM × 36 months = $13,500. False LTV = $500 ARPA × 1.00 × 36 months = $18,000 (33% inflation). The fix: Always use gross margin in LTV calculations. If you don't know your SaaS gross margin, estimate 70-85% for typical software, or calculate it properly by allocating hosting, support, and infrastructure costs.

Mistake: Understated Churn Rate

The error: Using a churn rate that's lower than actual customer attrition, inflating calculated customer lifetime. Why it happens: Excluding certain churn types (non-payment, downgrades to free), using logo churn when revenue churn is higher, or cherry-picking low-churn periods. The impact: Dramatically inflates LTV. Understating monthly churn from 3% to 2% increases calculated lifetime from 33 to 50 months—a 50% LTV inflation. Example: True: 3% monthly churn → 33 month lifetime → $500 × 0.80 × 33 = $13,200 LTV. False: 2% monthly churn → 50 month lifetime → $500 × 0.80 × 50 = $20,000 LTV (51% inflation). The fix: Use gross revenue churn (total lost MRR / starting MRR) from a representative time period. Include all churn: cancellations, non-payment, and downgrades. Don't exclude "involuntary" churn—it's still churn.

Mistake: Ignoring Gross Margin Variability

The error: Using a static gross margin when actual margins vary significantly by customer segment, contract size, or over time. Why it happens: Company-wide gross margin is easy to calculate; segment-specific margins require detailed cost allocation that many companies haven't done. The impact: If your highest-LTV customers have lower gross margins (common with enterprise customers requiring more support), using company-average margins overstates their LTV. Example: Enterprise customers: $5,000 ARPA but 65% gross margin (high-touch support). SMB customers: $500 ARPA but 85% gross margin (self-serve). Using blended 80% margin overstates enterprise LTV: False: $5,000 × 0.80 × 30 = $120,000. True: $5,000 × 0.65 × 30 = $97,500 (18% inflation). The fix: Calculate segment-specific gross margins for LTV calculations by segment. For company-wide LTV, use revenue-weighted average gross margin, not simple average.

Mistake: Including Unrealistic Expansion

The error: Projecting future expansion revenue into LTV based on historical expansion rates that aren't sustainable. Why it happens: Companies see strong expansion in early cohorts and project those rates indefinitely, or include one-time upsells as recurring expansion. The impact: Expansion can more than double calculated LTV if projected aggressively—but most expansion models assume historical rates continue forever, which they don't. Example: Base LTV: $15,000. Adding 10% annual expansion: $15,000 × 1.5 = $22,500 (50% increase). But if expansion only occurs in years 1-2 and then plateaus, true LTV might be $17,500. The fix: Use conservative expansion assumptions based on cohort maturation data. If you don't have mature cohorts (3+ years), don't project expansion beyond what you've observed. Many of the companies we work with calculate "contracted LTV" (no expansion) alongside "expanded LTV" to show the range.

LTV Sanity Check

Divide your calculated LTV by ARPA. This gives "customer lifetime" in months/years. If the result exceeds your oldest cohort age, your LTV is based on projection, not observation. Flag this clearly—projected LTV is inherently less certain than observed LTV.

CAC Underestimation Errors

False positives also arise from understated CAC calculations that exclude costs that should be included.

Mistake: Marketing-Only CAC

The error: Calculating CAC using only marketing spend, excluding sales costs. Why it happens: Marketing spend is easy to track in ad platforms. Sales costs require payroll allocation and are often managed separately. The impact: Understates CAC by 50-300% depending on sales model. A company with expensive enterprise sales team showing 10:1 "CAC" might have 2:1 true CAC. Example: Marketing spend: $100,000 → 50 customers → "CAC": $2,000. But sales team cost: $300,000 for same period. True CAC: $400,000 / 50 = $8,000 (4x higher). The fix: Always use fully-loaded CAC including all sales and marketing costs. If sales assists marketing-generated leads, include sales costs. If you want to track marketing efficiency separately, call it "Marketing Cost Per Acquisition" (MCPA), not CAC.

Mistake: Excluding Overhead and Support Costs

The error: Calculating CAC using only direct sales and marketing headcount, excluding operations, enablement, and allocated overhead. Why it happens: "Fully loaded" costs require overhead allocation methodologies that many companies haven't implemented. The impact: Understates CAC by 20-40%. Support staff, sales tools, office space, and management overhead are real costs of customer acquisition. Example: Direct S&M compensation: $500,000. Ops/enablement/tools: $100,000. Allocated overhead (management, office, benefits): $100,000. 100 customers acquired. Partial CAC: $500,000 / 100 = $5,000. Fully-loaded CAC: $700,000 / 100 = $7,000 (40% higher). The fix: Include all costs that wouldn't exist if you weren't acquiring customers. This includes sales ops, marketing ops, enablement, tools, and allocated overhead. The test: if you shut down customer acquisition, would this cost go away?

Mistake: Wrong Customer Count Denominator

The error: Dividing by incorrect customer counts—either including existing customers (for upsells) or excluding certain new customer types. Why it happens: Systems may not cleanly distinguish new vs. existing customers, or may count "logos" rather than "contracts" or vice versa. The impact: Using too-high customer count understates CAC, making unit economics look better than reality. Example: Marketing spend: $100,000. "Customers acquired": 100 (includes 30 existing customers who upsold). True new customers: 70. False CAC: $100,000 / 100 = $1,000. True CAC: $100,000 / 70 = $1,429 (43% higher). The fix: Only count net-new paying customers in CAC denominator. Upsells to existing customers are expansion, not acquisition. If a "new" contract is from an existing customer's new division, decide consistently whether that's new or expansion.

Mistake: Timing Misalignment

The error: Comparing current-period marketing spend to customers acquired over a different period. Why it happens: Marketing spend and customer acquisition don't align perfectly—spend in January may generate customers in March. Simple monthly calculation ignores lag. The impact: Can understate or overstate CAC depending on whether spend is increasing or decreasing. During growth, understates CAC (high recent spend, counting customers from lower past spend). Example: January spend: $50,000 → generates 50 customers (closes in March). March spend: $100,000 → generates 50 customers (closes in May). Simple March calculation: $100,000 / 50 = $2,000 CAC. But those March customers came from January spend. True CAC: $50,000 / 50 = $1,000. The fix: Use cohorted CAC that matches spend to the customers it actually generated, accounting for average sales cycle length. Or use trailing 6-12 month averages to smooth timing effects.

CAC Audit

Total your fully-loaded CAC and multiply by customers acquired. This should roughly equal your total S&M spend (within 10%). If CAC × customers is significantly less than S&M spend, you're missing costs in your CAC calculation.

Cohort and Timing Mismatches

False positives often arise from comparing LTV and CAC from incompatible time periods or customer segments.

Mistake: Mature LTV vs Current CAC

The error: Using LTV calculated from early/mature cohorts alongside CAC from current spending—comparing apples to oranges. Why it happens: LTV requires historical data (mature cohorts) while CAC uses current spend. The natural calculation uses available data from different periods. The impact: If early customers were higher quality or CAC has increased, this comparison overstates current unit economics. Example: 2022 cohort: $30,000 LTV, CAC was $5,000 → 6:1 ratio. 2024 CAC: $8,000. Using 2022 LTV with 2024 CAC: $30,000 / $8,000 = 3.75:1 (looks good). But 2024 cohort quality may yield $20,000 LTV: $20,000 / $8,000 = 2.5:1 (concerning). The fix: Compare like periods. Use projected LTV for recent cohorts based on early retention signals, or use cohorted CAC from the same period as LTV. Clearly label whether you're using observed or projected LTV.

Mistake: Blended Metrics Hiding Segment Differences

The error: Using company-wide blended LTV and CAC when segments have dramatically different unit economics. Why it happens: Blended metrics are simpler and the "official" numbers for reporting. Segment breakdowns require additional analysis. The impact: A strong segment can hide a weak segment, making overall unit economics look acceptable when part of the business is value-destructive. Example: Enterprise: $100,000 LTV / $25,000 CAC = 4:1 (profitable). SMB: $8,000 LTV / $5,000 CAC = 1.6:1 (marginal). Blended: $40,000 LTV / $12,000 CAC = 3.3:1 (looks healthy). But SMB is 80% of customers and barely sustainable. The fix: Calculate and report segment-level LTV:CAC alongside blended. Understand which segments drive overall ratio. Don't let blended metrics mask segment problems that require different strategies.

Mistake: Channel-Blind CAC

The error: Using average CAC across all channels when channel economics vary dramatically. Why it happens: Allocating CAC to specific channels requires attribution modeling that many companies haven't implemented. The impact: Efficient channels subsidize inefficient ones in the average, hiding where acquisition investment actually works. Example: Organic/referral: $500 CAC (200 customers). Paid ads: $8,000 CAC (50 customers). Events: $15,000 CAC (10 customers). Blended: ($100K + $400K + $150K) / 260 = $2,500. But investing more in events destroys value while organic is highly profitable. The fix: Calculate channel-specific CAC and compare to segment-appropriate LTV. Use this for acquisition investment decisions—invest more in channels with strong LTV:CAC, less in weak channels.

Mistake: Contract Term Mismatch

The error: Comparing LTV (which may assume full contract term) to CAC without considering contract duration and renewal probability. Why it happens: LTV formulas often use average lifetime, but CAC is a point-in-time acquisition cost. Contract commitments change the risk profile. The impact: For annual contracts, you've "locked in" year 1 revenue but not years 2+. LTV assumes renewal, but if renewal rates differ from assumptions, true LTV differs from calculated. Example: Annual contract: $12,000 ARR, 3-year expected lifetime → $36,000 LTV. Year 1 guaranteed (contracted), Years 2-3 depend on 75% annual renewal. Risk-adjusted LTV: $12,000 + ($12,000 × 0.75) + ($12,000 × 0.56) = $27,720. Using $36,000 instead of $27,720 overstates LTV:CAC by 30%. The fix: Consider contract terms in LTV calculation. Contracted revenue has higher certainty than projected renewals. Some companies report "contracted LTV" vs "projected LTV" to show the range.

Apples-to-Apples Test

For the customers you acquired last quarter, what's their observed retention through today, and what CAC did you pay to acquire them? This cohorted view eliminates timing mismatches and provides more accurate current unit economics.

Data Quality and Methodology Errors

Even with correct formulas, data quality issues create false positives that are difficult to detect without careful auditing.

Mistake: Survivorship Bias in LTV

The error: Calculating LTV using only customers who are still active, excluding early churners. Why it happens: Analytics systems often track "current customers" rather than cohort-based views that include all customers ever. The impact: Dramatically overstates LTV by excluding your worst-performing customers. If 30% of customers churn in month 1, calculating LTV on survivors inflates the result. Example: 100 customers acquired. 30 churn in month 1 (LTV: $500 each). 70 remain, average lifetime 24 months (LTV: $10,000 each). True average LTV: (30 × $500 + 70 × $10,000) / 100 = $7,150. Survivor-only LTV: $10,000 (40% inflation). The fix: Use cohort-based LTV calculation that includes all customers from acquisition, not just those who survived to a certain point. Early churners must be included in the average.

Mistake: Inconsistent Metric Definitions

The error: Using different definitions for the same metric across calculations or over time. Why it happens: Different teams or systems define metrics differently. Definitions evolve but historical data isn't restated. The impact: Comparisons become meaningless. If this year's CAC excludes costs that last year's included, trending shows false improvement. Example: 2023 CAC: $6,000 (includes sales ops). 2024 CAC: $5,000 (excludes sales ops). Apparent: 17% efficiency improvement. Reality: Definition change. True 2024 CAC: $6,200 (3% efficiency decline). The fix: Document metric definitions precisely. When definitions change, restate historical data or clearly note the discontinuity. Never compare metrics with different definitions.

Mistake: Sample Size Issues

The error: Calculating LTV from cohorts that are too small to be statistically meaningful. Why it happens: Early-stage companies have limited data. Breaking down by segment creates very small cohorts. The impact: Small cohorts have high variance—random luck (a few big customers or early churners) swings calculated LTV dramatically. Example: Enterprise cohort: 8 customers. 2 churned early (25% churn). 2 expanded significantly. Calculated LTV: $150,000. But with only 8 customers, adding or removing one outlier could change LTV by 20%+. The fix: Require minimum cohort sizes for reliable LTV calculation (generally 30+ customers per cohort for reasonable confidence). For smaller segments, use wider confidence intervals or combine with similar segments.

Mistake: Cherry-Picking Time Periods

The error: Selecting favorable time periods for either LTV (high-retention cohorts) or CAC (low-spend periods) to inflate the ratio. Why it happens: Conscious or unconscious selection bias when preparing metrics for investors or board. The impact: Creates a false positive that doesn't represent ongoing business performance. Example: Q1 2024: Seasonal low spending, efficient acquisition → $4,000 CAC. Q3 2024: Normal spending → $6,500 CAC. Reporting Q1 CAC with full-year LTV makes unit economics look 60% better than normal quarters. The fix: Use consistent, representative time periods—ideally trailing 12 months for both LTV and CAC. If showing point-in-time metrics, acknowledge they may not represent normal performance.

Data Quality Audit

Can you reproduce your LTV:CAC calculation from raw customer-level data? If the calculation depends on pre-aggregated reports you can't verify, you may have hidden data quality issues. True confidence requires end-to-end traceability.

Building Trustworthy LTV:CAC

Creating LTV:CAC calculations that investors and operators can trust requires systematic methodology and appropriate skepticism.

Establish Clear Methodology Documentation

Document every component of your LTV:CAC calculation in detail. LTV documentation: ARPA calculation (what's included/excluded in revenue), gross margin definition and calculation, churn definition and measurement period, expansion assumptions (if any), customer lifetime calculation method. CAC documentation: Included cost categories (with specific line items), customer count definition, time period alignment methodology, attribution approach (for channel-level CAC). Share documentation with stakeholders and update whenever methodology changes. Sophisticated investors will ask detailed methodology questions—vague answers erode credibility.

Build Cohort-Level Tracking

Invest in cohort-based analytics that track customer performance from acquisition through entire lifecycle. Minimum cohort data: Acquisition month, acquisition channel, initial ACV, acquisition cost, monthly MRR over time, churn date (if applicable), all expansion/contraction events. This enables: Cohort-specific LTV calculation (not just averages), LTV trending by vintage (are newer cohorts better or worse?), channel-specific unit economics, early warning indicators (cohort performance vs historical). Many LTV:CAC errors become obvious when you can see cohort-level data rather than aggregate averages.

Implement Confidence Intervals

Report LTV:CAC ranges rather than false-precision point estimates. Sources of uncertainty: Churn rate variance (actual may differ from historical average), expansion assumptions (projected vs guaranteed), gross margin estimates (if not precisely measured), small cohort sizes (statistical variance). Example presentation: "Our LTV:CAC ratio is 3.5:1, with 90% confidence interval of 2.8:1 to 4.2:1. The primary uncertainty driver is churn rate variance in newer cohorts." This honest presentation builds credibility. Investors prefer realistic ranges over suspiciously precise numbers.

Regular Audit and Validation

Audit your LTV:CAC calculation quarterly against actual cohort performance. Validation checks: Does projected LTV from 2 years ago match observed LTV for that cohort today? Does CAC × customer count equal actual S&M spend? Do segment-level metrics weight to company totals? Are all paying customers included in cohorts? Common findings: Projected LTV 10-20% higher than realized, certain cost categories missing from CAC, definition drift over time. Use audit findings to improve methodology and adjust projections. Honest auditing prevents compounding errors and builds trustworthy metrics over time.

The Honest Metrics Standard

Would you be comfortable if an investor with full data access recalculated your LTV:CAC using their methodology? If your number depends on favorable assumptions or definitions they might challenge, address those issues proactively rather than waiting to be questioned.

Frequently Asked Questions

What LTV:CAC ratio should we target for healthy unit economics?

The common benchmark is 3:1 or higher for sustainable growth. However, context matters significantly. Below 1:1 is unsustainable—you lose money on every customer. 1:1 to 2:1 is marginal—growth investment is risky. 2:1 to 3:1 is acceptable for conservative growth. 3:1 to 5:1 supports aggressive growth investment. Above 5:1 is excellent, but verify the calculation—you may also be underinvesting in growth. Importantly, LTV:CAC should be paired with CAC payback period. A 4:1 ratio with 12-month payback is excellent; the same ratio with 36-month payback indicates survival risk (customers must stay 3 years to realize that value). Target 3:1+ with payback under 18 months for healthy, fundable unit economics.

Should we include expansion revenue in LTV calculations?

Including expansion in LTV is acceptable if done conservatively and transparently. Best practice: Calculate and report both "contracted LTV" (no expansion assumed) and "expanded LTV" (including historical expansion rates). This shows the range and makes assumptions explicit. Common mistakes: Projecting expansion rates forever when they actually plateau after 2-3 years, including one-time upsells as if they were recurring expansion, and using expansion from best cohorts to project all-customer LTV. If you include expansion, base it on observed cohort behavior and clearly state your assumptions. Be especially skeptical of expansion assumptions for new customer segments where you don't have historical data.

How do we handle LTV:CAC for different customer segments?

Calculate segment-specific LTV:CAC alongside blended metrics to understand true unit economics by segment. Key principle: Different segments often have dramatically different economics. Enterprise may have high LTV and high CAC; SMB may have lower both but different ratios. Blended metrics can hide segment problems. Approach: Calculate LTV using segment-specific ARPA, gross margin, and churn rates. Calculate CAC by allocating costs to segments (some costs are direct, others allocated by acquisition volume). Compare segment ratios to identify which segments drive profitability and which may be value-destructive. Use segment-level insights for go-to-market investment decisions—invest more in high-ratio segments, consider reducing investment in low-ratio segments.

What costs should be included in fully-loaded CAC?

Fully-loaded CAC should include all costs that exist because you're acquiring customers. The test: If you stopped acquiring customers, would this cost go away? Direct costs: Sales team compensation (base, commission, bonus, benefits), marketing spend (paid, content, events, tools, agencies), sales operations and enablement, marketing operations. Allocated costs: Office space and equipment for S&M teams, management overhead for S&M functions, shared tools (CRM, marketing automation) proportionally. Exclude: Customer success costs (post-acquisition retention), product costs, G&A not related to S&M. Common mistake: Calculating "marketing-only CAC" that excludes sales—this can understate true CAC by 50-300% depending on your sales model.

How do we calculate LTV when we don't have mature cohorts?

Early-stage companies without mature cohorts must use projected LTV with appropriate caveats. Approach 1: Use observed retention to project lifetime. If you have 6 months of data showing 3% monthly churn, project lifetime as 1/0.03 = 33 months. This assumes churn remains constant, which may not hold. Approach 2: Use industry benchmarks for similar business models. B2B SaaS typically sees 5-7% annual churn for healthy companies, implying 14-20 year customer lifetime. Apply your ARPA and gross margin to benchmark lifetime. Approach 3: Use early retention signals. Early retention strongly predicts long-term retention. If your 90-day retention significantly exceeds benchmarks, you can justify higher lifetime projections. Key: Always label projected LTV as such, provide confidence intervals, and update projections as you get more data. Don't present projections as observed fact.

How often should we recalculate LTV:CAC and what triggers a recalculation?

Recalculate LTV:CAC quarterly at minimum, with triggers for interim updates. Quarterly review: Update all components with latest data, compare projected vs observed LTV for historical cohorts, verify methodology consistency, update confidence intervals. Triggers for immediate recalculation: Significant churn rate change (>20% shift), pricing or packaging changes, new customer segment entry, major CAC efficiency change (positive or negative), changes to gross margin (new infrastructure costs, support model changes). What to look for: Is LTV trending up or down by cohort vintage? Is CAC increasing as you scale? Are segment unit economics diverging? Regular recalculation prevents stale metrics from driving poor decisions. LTV:CAC from 12 months ago may not reflect current business reality.

Disclaimer

This content is for informational purposes only and does not constitute financial, accounting, or legal advice. Consult with qualified professionals before making business decisions. Metrics and benchmarks may vary by industry and company size.

Key Takeaways

LTV:CAC ratio is essential for understanding unit economics, but only when calculated correctly. False positives—inflated ratios that make unit economics appear healthier than reality—lead to catastrophic over-investment in growth that burns runway without building sustainable value. The most common false positive errors include overstated LTV (using revenue instead of gross profit, understating churn, projecting unrealistic expansion), understated CAC (excluding sales costs, omitting overhead, wrong customer counts), and timing/cohort mismatches (comparing mature cohort LTV to current CAC, blending segments with different economics). Building trustworthy LTV:CAC requires clear methodology documentation, cohort-level tracking, appropriate confidence intervals, and regular auditing against observed performance. The goal isn't a high ratio—it's an accurate ratio that reflects true business economics and enables sound investment decisions. Whether you're managing growth strategy or preparing for fundraising, honest unit economics serve you better than flattering but false metrics. Use this guide to audit your current calculations, identify potential errors, and build LTV:CAC metrics that you can defend with confidence.

Claire Dunphy