gh-lyndonkl-claude/skills/financial-unit-economics/resources/methodology.md

Financial Unit Economics Methodology

Advanced techniques for calculating, analyzing, and optimizing unit economics.

Table of Contents

1. Customer Acquisition Cost (CAC)
2. Lifetime Value (LTV)
3. Contribution Margin Analysis
4. Cohort Analysis
5. Interpreting Unit Economics
6. Advanced Topics

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1. Customer Acquisition Cost (CAC)

Fully-Loaded CAC Components

Formula: CAC = (Total S&M Costs) ÷ New Customers Acquired

Sales & Marketing (S&M) Costs to include:

• Marketing spend: Paid ads (Google, Facebook, LinkedIn), content marketing, SEO tools, events, sponsorships
• Sales team compensation: Base salaries, commissions, bonuses, benefits, taxes
• Marketing team compensation: Marketers, designers, writers, contractors
• Sales tools: CRM (Salesforce, HubSpot), sales engagement (Outreach, SalesLoft), analytics
• Marketing tools: Marketing automation (Marketo, Pardot), analytics (Google Analytics, Mixpanel), advertising platforms
• Overhead allocation: Portion of office space, admin support, IT costs attributable to S&M teams
• Agency/consultant fees: External agencies, freelancers, consultants for marketing or sales

What NOT to include (not acquisition costs):

• Engineering/product development (build the product, not acquire customers)
• Customer success/support (retain customers, not acquire)
• General & administrative (not directly related to acquisition)

Time Period for CAC

Match costs to revenue period: If calculating monthly CAC, use monthly S&M costs and monthly new customers.

Lag effect: CAC spent today may yield customers next month. Adjust if significant lag (e.g., long sales cycles). Use 1-3 month lag for enterprise sales.

Example:

• Month 1: $50k S&M spend, 100 customers acquired → CAC = $500
• But if customers from Month 1 spend came from ads run in Month 0, adjust accordingly.

CAC by Channel

Breaking down CAC by channel reveals which channels are efficient vs. inefficient.

Method: Track spend and new customers per channel.

Example:

Channel	S&M Spend	New Customers	CAC	LTV	LTV/CAC
Paid Search	$30k	100	$300	$900	3.0
Organic	$10k	100	$100	$1,200	12.0
Referral	$5k	50	$100	$1,500	15.0
Paid Social	$20k	50	$400	$700	1.75

Insight: Organic and Referral have best economics (low CAC, high LTV). Paid Social is unprofitable (LTV/CAC <2:1). Action: Increase organic/referral investment, pause paid social.

CAC Trends Over Time

Monitor CAC trends: Is CAC increasing or decreasing over time?

Causes of rising CAC:

• Market saturation (exhausted easy channels)
• Increased competition (competitors bidding up ad costs)
• Product-market fit weakening (harder to acquire customers)
• Inefficient spend (poor targeting, low conversion rates)

Causes of falling CAC:

• Improved conversion rates (better landing pages, messaging)
• Brand awareness (more direct/organic traffic)
• Product-led growth (virality, word-of-mouth)
• Channel optimization (focusing on best-performing channels)

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2. Lifetime Value (LTV)

LTV Calculation Methods

Method 1: Simple LTV (Subscription)

LTV = ARPU × Gross Margin % ÷ Monthly Churn Rate

When to use: Early-stage SaaS, limited data, need quick estimate.

Example:

• ARPU = $50/month
• Gross Margin = 80%
• Monthly Churn = 5%
• LTV = $50 × 80% ÷ 0.05 = $50 × 80% × 20 months = $800

Method 2: Cohort-Based LTV (More Accurate)

Track actual retention by cohort, sum revenue over observed periods.

LTV = ARPU × Gross Margin × Σ(Retention at month i)

Example Cohort (acquired Jan 2024):

Month	Retention %	Revenue (ARPU × Retention)	Cumulative
0	100%	$50 × 1.0 = $50	$50
1	95%	$50 × 0.95 = $47.50	$97.50
2	88%	$50 × 0.88 = $44	$141.50
3	80%	$50 × 0.80 = $40	$181.50
6	60%	$50 × 0.60 = $30	~$280
12	40%	$50 × 0.40 = $20	~$450

LTV = $450 × 80% gross margin = $360

Note: This is more conservative than simple LTV ($800) because early churn is higher than average.

Method 3: Predictive LTV (Machine Learning)

Use historical data to predict future retention and spending patterns. Advanced approach for companies with large datasets.

Inputs: Customer attributes (demographics, behavior, acquisition channel), historical purchase/churn data.

Model: Regression, survival analysis, or ML model predicts LTV for each customer segment.

LTV for Different Business Models

Transactional (E-commerce):

LTV = AOV × Purchase Frequency × Gross Margin % × Customer Lifetime (years)

Example:

• AOV = $100
• Purchases/year = 3
• Gross Margin = 50%
• Lifetime = 2 years
• LTV = $100 × 3 × 50% × 2 = $300

Marketplace:

LTV = GMV per user × Take Rate × Gross Margin % ÷ Churn Rate

Example (ride-sharing):

• Monthly GMV per rider = $200 (total rides)
• Take Rate = 25%
• Gross Margin = 80% (after payment processing)
• Monthly Churn = 10%
• Lifetime = 1 ÷ 0.10 = 10 months
• Monthly Revenue = $200 × 25% = $50
• LTV = $50 × 10 months × 80% = $400

Freemium:

Blended LTV = (Free-to-Paid Conversion % × Paid User LTV) - (Free User Costs × Avg Free User Lifetime)

Example:

• 100 free users, 5% convert to paid
• Paid LTV = $1,000
• Free user cost = $2/month (hosting), avg lifetime 6 months
• Blended LTV = (0.05 × $1,000) - ($2 × 6) = $50 - $12 = $38 per free user

Improving LTV

Levers to increase LTV:

1. Reduce churn: Improve onboarding, product engagement, customer success. 1% churn reduction → 10-25% LTV increase.
2. Increase ARPU: Upsells, cross-sells, premium tiers, usage-based pricing.
3. Improve gross margin: Reduce COGS, optimize infrastructure, negotiate better rates.
4. Extend lifetime: Long-term contracts, annual billing (locks in customers).

Example impact (SaaS):

• Current: ARPU $50, Churn 5%, Margin 80% → LTV = $800
• Reduce churn to 4%: LTV = $50 × 80% ÷ 0.04 = $1,000 (+25%)
• Increase ARPU to $60: LTV = $60 × 80% ÷ 0.05 = $960 (+20%)
• Both: LTV = $60 × 80% ÷ 0.04 = $1,200 (+50%)

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3. Contribution Margin Analysis

Contribution Margin Formula

Contribution Margin = Revenue - Variable Costs
Contribution Margin % = (Revenue - Variable Costs) ÷ Revenue

Variable costs (scale with each unit):

• COGS (cost of goods sold)
• Hosting/infrastructure per user
• Payment processing fees (2-3% of revenue)
• Customer support (per-customer time)
• Shipping/fulfillment
• Transaction-specific costs

Fixed costs (do NOT include):

• Engineering salaries (build product once)
• Rent, utilities
• Admin, HR, finance teams

Contribution Margin by Business Model

SaaS:

• Revenue: $100/month subscription
• Variable costs: $15 hosting + $3 payment fees = $18
• Contribution Margin: $100 - $18 = $82
• Margin %: 82%

E-commerce:

• Revenue: $80 product sale
• Variable costs: $30 COGS + $5 shipping + $2.40 payment fees = $37.40
• Contribution Margin: $80 - $37.40 = $42.60
• Margin %: 53%

Marketplace:

• GMV: $200 transaction
• Take Rate: 20% → Revenue = $40
• Variable costs: $2 payment fees + $3 support = $5
• Contribution Margin: $40 - $5 = $35
• Margin %: 87.5% (of platform revenue)

Improving Contribution Margin

Levers:

1. Increase prices: Directly increases revenue per unit.
2. Reduce COGS: Negotiate supplier costs, economies of scale, vertical integration.
3. Optimize infrastructure: Right-size hosting, use cheaper providers, optimize usage.
4. Automate support: Self-service, chatbots, knowledge base reduce manual support time.
5. Negotiate fees: Lower payment processing rates (volume discounts), reduce transaction costs.

Example (E-commerce):

• Current: Revenue $80, COGS $30, Margin 53%
• Negotiate COGS to $25: Margin = ($80 - $32.40) / $80 = 59.5% (+6.5pp)
• Increase price to $90: Margin = ($90 - $37.65) / $90 = 58% (+5pp)
• Both: Margin = ($90 - $32.65) / $90 = 63.7% (+10.7pp)

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4. Cohort Analysis

Why Cohort Analysis Matters

Problem with averages: Blending all customers hides important trends. Early customers may have different behavior than recent customers.

Cohort analysis: Track customers grouped by acquisition period (month, quarter) to see how metrics evolve.

Benefits:

• Detect improving/worsening trends
• Compare channels/segments
• Forecast future LTV based on observed behavior

Building a Retention Cohort Table

Structure: Rows = cohorts (acquisition month), Columns = months since acquisition.

Example:

Cohort	M0	M1	M2	M3	M6	M12
Jan 2024	100%	92%	84%	78%	62%	42%
Feb 2024	100%	90%	81%	75%	60%	-
Mar 2024	100%	93%	86%	80%	65%	-
Apr 2024	100%	91%	83%	77%	-	-

Insights:

• Improving retention: Mar cohort (93% M1 retention) > Jan cohort (92%). Product improvements working.
• Stable long-term retention: ~60% at M6 across cohorts. Predictable LTV.

Calculating LTV from Cohorts

Method: Sum revenue at each time period, weighted by retention.

Example (Jan 2024 cohort, ARPU $50, Margin 80%):

LTV = $50 × 80% × (1.0 + 0.92 + 0.84 + 0.78 + ... + 0.42 at M12)

Approximate sum of retention % = ~9.5 months equivalent

LTV = $50 × 80% × 9.5 = $380

More accurate: Sum all observed months, extrapolate tail based on churn rate stabilization.

Cohort Analysis by Channel

Compare retention and LTV across acquisition channels.

Example:

Channel	M0	M1	M3	M6	M12	LTV
Organic	100%	95%	85%	70%	55%	$450
Paid Search	100%	88%	75%	55%	35%	$300
Referral	100%	97%	90%	75%	60%	$500

Insight: Referral has best retention and LTV. Paid Search has worst retention (high early churn). Focus on referral growth.

Trends to Monitor

1. Retention curve shape: Does churn stabilize (flatten) after a few months, or continue accelerating?
2. Cohort improvement: Are newer cohorts retaining better than older cohorts? (Product improvements working)
3. Channel differences: Which channels yield stickiest customers?
4. Time to payback: How long until cumulative revenue (× margin) > CAC?

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5. Interpreting Unit Economics

LTV/CAC Ratio Benchmarks

Ratio	Assessment	Recommendation
<1:1	Unsustainable	Losing money on every customer. Fix or pivot.
1-2:1	Marginal	Barely profitable. Don't scale yet.
2-3:1	Acceptable	Unit economics work. Optimize before scaling.
3-5:1	Good	Can profitably grow. Scale marketing spend.
>5:1	Excellent	Strong economics. Aggressive growth, raise capital.

Why 3:1 is the target:

• 1× covers CAC
• 1× covers operating expenses (R&D, G&A, customer success)
• 1× profit

Context matters:

• Payback period: 10:1 LTV/CAC with 24-month payback is worse than 4:1 with 6-month payback (cash strain).
• Market size: Low LTV/CAC acceptable if huge market (can still build large business).
• Stage: Early-stage startups may accept 2-3:1 while finding product-market fit. Growth-stage should target >3:1.

Payback Period Benchmarks

Payback	Assessment	Cash Impact
<6 months	Excellent	Can reinvest quickly, fuel rapid growth.
6-12 months	Good	Manageable, standard for SaaS.
12-18 months	Acceptable	Need patient capital, slower growth.
>18 months	Challenging	High cash burn, risky. Hard to scale.

Why payback matters: Short payback = fast capital recovery = can reinvest in growth without needing external funding.

Example:

• Company A: LTV/CAC 8:1, Payback 18 months → High cash burn, slow reinvestment despite good ratio.
• Company B: LTV/CAC 4:1, Payback 6 months → Faster reinvestment, can scale more aggressively.

Cash Efficiency Metrics

CAC Payback (SaaS-specific):

CAC Payback (months) = S&M Spend ÷ (New ARR × Gross Margin %)

Example:

• Q1 S&M spend: $100k
• New ARR added: $120k
• Gross Margin: 80%
• CAC Payback = $100k ÷ ($120k × 80%) = 1.04 quarters = ~3.1 months

Sales Efficiency (Magic Number):

Sales Efficiency = (New ARR in Quarter) ÷ (S&M Spend in Prior Quarter)

Benchmarks:

• <0.75: Inefficient, unprofitable growth
• 0.75-1.0: Acceptable

• 1.0: Efficient, profitable growth

• 1.5: Highly efficient

Example:

• Q1 S&M spend: $200k
• Q2 new ARR: $180k
• Sales Efficiency = $180k / $200k = 0.9 (acceptable)

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6. Advanced Topics

Net Revenue Retention (NRR)

Formula:

NRR = (Starting ARR + Expansion - Contraction - Churn) ÷ Starting ARR

Components:

• Starting ARR: Revenue from cohort at start of period
• Expansion: Upsells, cross-sells, usage growth
• Contraction: Downgrades, reduced usage
• Churn: Customers leaving

Example:

• Starting ARR (Jan 2024 cohort): $100k
• Expansion (upsells): +$25k
• Contraction (downgrades): -$5k
• Churn (lost customers): -$10k
• Ending ARR: $100k + $25k - $5k - $10k = $110k
• NRR = $110k / $100k = 110%

Benchmarks:

• <100%: Shrinking revenue from existing customers (bad)
• 100-110%: Stable, small growth from expansion
• 110-120%: Good, strong expansion

• 120%: Excellent, revenue grows even without new customers

Why NRR matters: >100% NRR means you can grow revenue without adding new customers. Powerful compounding effect.

Unit Economics for Different Stages

Early-stage (finding product-market fit):

• Target: LTV/CAC >2:1
• Focus: Find repeatable, scalable channels
• Acceptable: Higher CAC, longer payback while iterating

Growth-stage (scaling):

• Target: LTV/CAC >3:1, Payback <12 months
• Focus: Optimize channels, improve retention
• Need: Efficient growth to justify increasing spend

Late-stage (mature):

• Target: LTV/CAC >4:1, Payback <6 months, NRR >110%
• Focus: Profitability, margin expansion
• Optimize: Every channel, reduce CAC, maximize LTV

Multi-Product Unit Economics

Challenge: Customers may buy multiple products. How to attribute value?

Approaches:

1. Customer-level LTV: Sum revenue across all products purchased by customer.

   • LTV = Total revenue from customer × Margin

2. Product-level LTV: Track LTV separately per product.

   • Useful if products have different margins, retention patterns.

3. Blended LTV: Weight by product mix.

   • Blended LTV = (% Product A × LTV_A) + (% Product B × LTV_B) + ...

Example (SaaS with two tiers):

• 70% subscribe to Basic ($50/month, LTV $800)
• 30% subscribe to Pro ($150/month, LTV $2,400)
• Blended LTV = (0.7 × $800) + (0.3 × $2,400) = $560 + $720 = $1,280

Sensitivity Analysis

Test how changes to assumptions impact unit economics.

Variables to test:

• Churn rate (+/- 1-2%)
• ARPU (+/- 10-20%)
• CAC (+/- 10-20%)
• Gross margin (+/- 5-10%)

Example:

• Base case: LTV $1,000, CAC $250, Ratio 4:1
• Churn increases 5% → 4%: LTV drops to $800, Ratio 3.2:1 (still acceptable)
• Churn increases 5% → 6%: LTV drops to $667, Ratio 2.7:1 (marginal)
• CAC increases 20% to $300: Ratio drops to 3.3:1 (still good)

Insight: Unit economics are sensitive to churn. Small churn increases significantly hurt LTV.

Competitive Dynamics

CAC increases over time due to:

• Market saturation (easier customers already acquired)
• Competition (bidding wars on ads, higher sales/marketing costs)
• Channel exhaustion (diminishing returns on channels)

Strategies:

1. Build moats: Brand, network effects, switching costs reduce reliance on paid acquisition.
2. Product-led growth: Virality, word-of-mouth, organic growth reduce CAC.
3. Expand TAM: Enter new markets, segments to access untapped customers.
4. Improve conversion: Better product, messaging, sales process → more customers from same spend.

Example (competitive landscape):

• Year 1: CAC $200, LTV $1,000, Ratio 5:1
• Year 3: CAC $350 (competition), LTV $1,200 (retention improvements), Ratio 3.4:1
• Year 5: CAC $500, LTV $1,500, Ratio 3:1

Insight: Even with rising CAC, improving LTV (retention, upsells) maintains healthy ratio.

Key Takeaways

1. CAC must be fully-loaded: Include all S&M costs (salaries, tools, overhead). Break down by channel.
2. LTV requires cohort data: Track retention by cohort, extrapolate conservatively. Don't rely on averages.
3. Contribution margin sets ceiling: Need high margin (>60% SaaS, >40% ecommerce) for viable economics.
4. Both ratio and payback matter: 5:1 ratio with 24-month payback < 3:1 with 6-month payback (cash efficiency).
5. Retention > Acquisition: Small churn improvements have exponential LTV impact. Prioritize retention.
6. Channel-level analysis: Blended metrics hide truth. Analyze CAC/LTV per channel, optimize spend accordingly.
7. Update quarterly: Unit economics evolve with scale, market changes, competition. Re-calculate regularly.