The Strategy Engine: Quantitative Models Master Reference¶

Purpose: Complete mathematical frameworks for business model analysis with Indian market examples and worked calculations.

Model 1: SaaS Metrics Model (Chapter 9)¶

Purpose¶

Evaluate SaaS business health through recurring revenue dynamics, customer acquisition efficiency, and overall company performance.

Formulas¶

MRR Waterfall:
Net New MRR = New MRR + Expansion MRR - Contraction MRR - Churned MRR

Net Revenue Retention (NRR):
NRR = [(Starting MRR + Expansion - Contraction - Churn) / Starting MRR] × 100%

CAC Payback Period:
CAC Payback (months) = CAC / (ARPU × Gross Margin)

Magic Number:
Magic Number = Net New ARR (Current Quarter) / S&M Spend (Previous Quarter)

Rule of 40:
Rule of 40 Score = Revenue Growth Rate (%) + EBITDA Margin (%)

Worked Example: Indian SaaS Company¶

Given Data:

Metric	Value
Starting MRR	₹50,00,000
New MRR	₹8,00,000
Expansion MRR	₹4,00,000
Contraction MRR	₹1,50,000
Churned MRR	₹2,50,000
CAC	₹60,000
ARPU	₹5,000/month
Gross Margin	75%
Q1 S&M Spend	₹2,00,00,000
Q2 Net New ARR	₹3,60,00,000
Revenue Growth	45%
EBITDA Margin	12%

Step-by-Step Calculations:

1. Net New MRR:

Net New MRR = ₹8,00,000 + ₹4,00,000 - ₹1,50,000 - ₹2,50,000
Net New MRR = ₹8,00,000

2. NRR:

NRR = [(₹50,00,000 + ₹4,00,000 - ₹1,50,000 - ₹2,50,000) / ₹50,00,000] × 100%
NRR = [₹50,00,000 / ₹50,00,000] × 100%
NRR = 100%

3. CAC Payback:

CAC Payback = ₹60,000 / (₹5,000 × 0.75)
CAC Payback = ₹60,000 / ₹3,750
CAC Payback = 16 months

4. Magic Number:

Magic Number = ₹3,60,00,000 / ₹2,00,00,000
Magic Number = 1.8

5. Rule of 40:

Rule of 40 = 45% + 12%
Rule of 40 = 57

Interpretation Guide¶

Metric	Poor	Acceptable	Good	Excellent
NRR	<90%	90-100%	100-120%	>120%
CAC Payback	>24 mo	18-24 mo	12-18 mo	<12 mo
Magic Number	<0.5	0.5-0.75	0.75-1.0	>1.0
Rule of 40	<20	20-30	30-40	>40

Our Example Assessment:

NRR at 100%: Acceptable (need more upselling to reach 120% benchmark)
CAC Payback at 16 months: Acceptable (optimize acquisition costs)
Magic Number at 1.8: Excellent (very efficient growth engine)
Rule of 40 at 57: Excellent (strong balance of growth + profitability)

Model 2: Marketplace Take-Rate Waterfall (Chapter 10)¶

Purpose¶

Calculate true marketplace profitability by tracking revenue leakage through the commission waterfall.

Formula¶

Contribution Margin Waterfall:

GMV (Gross Merchandise Value)
× Take Rate = Gross Commission
- Payment Processing (2-3% of GMV)
- Seller Subsidies
- Buyer Promotions
- Logistics Subsidies
- Refund Costs
= Net Take Rate Revenue
- Variable Operations Costs
= Contribution Margin per Transaction

Worked Example: Meesho-Style Marketplace¶

Given Data:

Component	Value
GMV	₹1,00,000
Stated Take Rate	0% (Zero commission model)
Payment Processing	2.0% of GMV
Shipping Revenue	₹80 per order
Avg Order Value	₹500
Orders from GMV	200 orders
Ad Revenue	₹5 per order
Logistics Cost	₹60 per order

Step-by-Step Calculations:

1. Commission Revenue:

Commission = ₹1,00,000 × 0%
Commission = ₹0

2. Payment Processing Cost:

Payment Processing = ₹1,00,000 × 2.0%
Payment Processing = ₹2,000 (cost)

3. Shipping Revenue:

Shipping Revenue = 200 orders × ₹80
Shipping Revenue = ₹16,000

4. Logistics Cost:

Logistics Cost = 200 orders × ₹60
Logistics Cost = ₹12,000

5. Shipping Margin:

Shipping Margin = ₹16,000 - ₹12,000
Shipping Margin = ₹4,000

6. Ad Revenue:

Ad Revenue = 200 orders × ₹5
Ad Revenue = ₹1,000

7. Total Contribution Margin:

Contribution Margin = ₹0 (Commission) - ₹2,000 (Payment) + ₹4,000 (Shipping) + ₹1,000 (Ads)
Contribution Margin = ₹3,000

8. Effective Take Rate:

Effective Take Rate = ₹3,000 / ₹1,00,000 × 100%
Effective Take Rate = 3.0%

Waterfall Visualization¶

flowchart TD
    A["GMV<br/>₹1,00,000<br/>100.0%"]
    B["Commission Revenue<br/>₹0<br/>0.0%"]
    C["Payment Processing<br/>(₹2,000)<br/>-2.0%"]
    D["Shipping Margin<br/>₹4,000<br/>4.0%"]
    E["Advertising Revenue<br/>₹1,000<br/>1.0%"]
    F["Contribution Margin<br/>₹3,000<br/>3.0%"]

    A --> B
    A --> C
    A --> D
    A --> E
    B --> F
    C --> F
    D --> F
    E --> F

Interpretation Guide¶

Effective Take Rate	Business Stage	Sustainability
<0%	Heavy investment phase	Unsustainable without funding
0-3%	Growth phase	Needs scale for profitability
3-8%	Maturing marketplace	Path to profitability visible
8-15%	Mature marketplace	Healthy unit economics
>15%	Dominant platform	Risk of disintermediation

Meesho Reality (₹7,615 Cr Revenue):

Monetizes through ads, shipping margins, and seller services
Zero commission enables massive seller acquisition
Contribution margin improves with scale through shipping aggregation

Seller-Side Economics (Provider Profitability)¶

Critical Gap: Platform economics typically focus on marketplace profitability, but seller sustainability determines long-term platform health. Unprofitable sellers churn, reducing supply quality.

Seller Unit Economics Formula:

Seller Contribution Margin:

Selling Price (to Customer)
- Platform Commission (Take Rate)
- Payment Processing (passed to seller or absorbed)
- Shipping Cost (if seller-paid)
- Packaging
- Returns/RTO Cost
= Seller Gross Margin
- Product COGS
= Seller Contribution Margin

Worked Example: Meesho Seller Economics

Component	Traditional Marketplace	Meesho (Zero Commission)
Selling Price	₹500	₹500
Platform Commission	₹75 (15%)	₹0 (0%)
Payment Processing	₹10 (2%)	₹10 (2%)
Shipping (to seller)	₹50	₹60 (charged to seller)
Packaging	₹15	₹15
Returns (20% rate)	₹30	₹30
Seller Gross Margin	₹320	₹385
Product COGS	₹200	₹200
Seller CM	₹120 (24%)	₹185 (37%)

Seller Profitability Thresholds:

Seller CM%	Assessment	Platform Impact
<10%	Unsustainable	High seller churn risk
10-20%	Marginal	Sellers vulnerable to cost increases
20-35%	Healthy	Stable supply base
>35%	Strong	Attracts quality sellers, reduces churn

Platform Health Indicators:

Metric	Healthy Range	Warning Signs
Seller Retention (Annual)	>70%	<50%
Avg Seller Revenue Growth	>15% YoY	Negative or flat
Seller Satisfaction (NPS)	>30	<0
% Sellers Profitable	>60%	<40%

Strategic Insight: Meesho's zero-commission model creates 13 percentage points higher seller margin (37% vs 24%), explaining their ability to attract 15M+ sellers despite lower traffic than Amazon/Flipkart. Seller economics drive supply-side network effects.

Model 3: Fintech Risk-Adjusted Return (Chapter 12)¶

Purpose¶

Calculate true profitability of lending/fintech operations accounting for credit risk and float economics.

Formulas¶

Net Interest Margin (NIM):
NIM = (Interest Income - Interest Expense) / Average Earning Assets × 100%

Expected Credit Loss:
Expected Loss = PD × LGD × EAD
Where:
  PD = Probability of Default (%)
  LGD = Loss Given Default (%)
  EAD = Exposure at Default (₹)

Float Value:
Float Value = Average Float Balance × Interest Rate × (Days / 365)

Risk-Adjusted Return:
Risk-Adjusted Return = Gross Revenue - Expected Loss - Operating Costs

Worked Example: Zerodha-Style Broker + NBFC¶

Given Data:

Metric	Value
Total Revenue	₹8,320 Cr
Interest Income	₹1,200 Cr
Interest Expense	₹150 Cr
Average Assets	₹15,000 Cr
Loan Portfolio (MTF)	₹5,000 Cr
PD (Probability of Default)	2.5%
LGD (Loss Given Default)	40%
Average Float	₹3,000 Cr
Risk-Free Rate	6.5%
Operating Margin	56.5%

Step-by-Step Calculations:

1. Net Interest Margin:

NIM = (₹1,200 Cr - ₹150 Cr) / ₹15,000 Cr × 100%
NIM = ₹1,050 Cr / ₹15,000 Cr × 100%
NIM = 7.0%

2. Expected Credit Loss:

Expected Loss = 2.5% × 40% × ₹5,000 Cr
Expected Loss = 0.025 × 0.40 × ₹5,000 Cr
Expected Loss = 0.01 × ₹5,000 Cr
Expected Loss = ₹50 Cr

3. Float Value:

Float Value = ₹3,000 Cr × 6.5% × (365/365)
Float Value = ₹3,000 Cr × 0.065
Float Value = ₹195 Cr annually

4. Risk-Adjusted Operating Profit:

Operating Profit = ₹8,320 Cr × 56.5%
Operating Profit = ₹4,701 Cr

Risk-Adjusted Profit = ₹4,701 Cr - ₹50 Cr (Expected Loss)
Risk-Adjusted Profit = ₹4,651 Cr

5. Loss Rate on Book:

Loss Rate = ₹50 Cr / ₹5,000 Cr × 100%
Loss Rate = 1.0%

Risk Metrics Summary¶

Metric	Calculated Value	Benchmark
NIM	7.0%	NBFC avg: 4-6%
Expected Loss Rate	1.0%	Secured: <1%, Unsecured: 2-5%
Float Contribution	₹195 Cr	Hidden profit driver
Risk-Adjusted Margin	55.9%	Industry leading

Interpretation Guide¶

NIM Interpretation:

<3%: Low margin, commodity business
3-6%: Healthy traditional lending
6-10%: Strong margin, likely secured + premium products
10%: High-risk lending or predatory rates

Expected Loss Benchmarks:

Loan Type	Typical PD	Typical LGD	Expected Loss
Home Loans	1-2%	20-30%	0.2-0.6%
Auto Loans	2-3%	40-50%	0.8-1.5%
Personal Loans	4-6%	60-70%	2.4-4.2%
Credit Cards	5-8%	80-90%	4.0-7.2%

Model 4: D2C Unit Economics (Chapter 13)¶

Purpose¶

Calculate true per-unit profitability for direct-to-consumer brands accounting for all fulfillment costs.

Formula¶

D2C Unit Economics:

Selling Price (MRP)
- Discount
= Net Revenue
- COGS (Product Cost)
= Gross Profit
- Packaging Cost
- Forward Shipping
- Payment Gateway Fee
- RTO Shipping (Return to Origin)
- Return Processing
- COD Handling Charges
= Contribution Margin (CM1)
- Customer Acquisition Cost (Allocated)
= Contribution Margin (CM2)

Worked Example: D2C Fashion Brand¶

Given Data:

Component	Value
MRP	₹1,499
Average Discount	25%
COGS	₹350
Packaging	₹45
Forward Shipping	₹65
Payment Gateway	2.0% of Net Revenue
Return Rate	25%
RTO Rate	8%
Return Shipping	₹75
COD %	40%
COD Handling	₹30 per COD order
CAC (Blended)	₹450

Step-by-Step Calculations:

1. Net Revenue:

Net Revenue = ₹1,499 × (1 - 0.25)
Net Revenue = ₹1,499 × 0.75
Net Revenue = ₹1,124.25

2. Gross Profit:

Gross Profit = ₹1,124.25 - ₹350
Gross Profit = ₹774.25

3. Payment Gateway Fee:

Gateway Fee = ₹1,124.25 × 2.0%
Gateway Fee = ₹22.49

4. RTO Cost (per order, allocated):

RTO Cost = 8% × (₹65 Forward + ₹75 Return)
RTO Cost = 0.08 × ₹140
RTO Cost = ₹11.20

5. Return Processing Cost (per order, allocated):

Return Cost = 25% × ₹75
Return Cost = ₹18.75

6. COD Handling Cost (per order, allocated):

COD Cost = 40% × ₹30
COD Cost = ₹12.00

7. CM1 Calculation:

CM1 = ₹774.25 (Gross Profit)
      - ₹45.00 (Packaging)
      - ₹65.00 (Forward Shipping)
      - ₹22.49 (Payment Gateway)
      - ₹11.20 (RTO Cost)
      - ₹18.75 (Returns)
      - ₹12.00 (COD Handling)

CM1 = ₹774.25 - ₹174.44
CM1 = ₹599.81

8. CM1 Margin:

CM1 Margin = ₹599.81 / ₹1,124.25 × 100%
CM1 Margin = 53.4%

9. CM2 (After CAC):

CM2 = ₹599.81 - ₹450.00
CM2 = ₹149.81

10. CM2 Margin:

CM2 Margin = ₹149.81 / ₹1,124.25 × 100%
CM2 Margin = 13.3%

Unit Economics Waterfall¶

flowchart TD
    A["MRP<br/>₹1,499.00<br/>100.0%"]
    B["Discount (25%)<br/>(₹374.75)<br/>-25.0%"]
    C["Net Revenue<br/>₹1,124.25<br/>75.0%"]
    D["COGS<br/>(₹350.00)<br/>-23.3%"]
    E["Gross Profit<br/>₹774.25<br/>51.7%"]
    F["Packaging<br/>(₹45.00)<br/>-3.0%"]
    G["Forward Shipping<br/>(₹65.00)<br/>-4.3%"]
    H["Payment Gateway<br/>(₹22.49)<br/>-1.5%"]
    I["RTO Allocation<br/>(₹11.20)<br/>-0.7%"]
    J["Returns Allocation<br/>(₹18.75)<br/>-1.3%"]
    K["COD Handling<br/>(₹12.00)<br/>-0.8%"]
    L["CM1<br/>₹599.81<br/>40.0%"]
    M["CAC<br/>(₹450.00)<br/>-30.0%"]
    N["CM2<br/>₹149.81<br/>10.0%"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    E --> G
    E --> H
    E --> I
    E --> J
    E --> K
    F --> L
    G --> L
    H --> L
    I --> L
    J --> L
    K --> L
    L --> M
    M --> N

Interpretation Guide¶

CM1 Margin	Assessment	Action
<20%	Unsustainable	Review pricing/costs urgently
20-35%	Thin margins	Optimize operations
35-50%	Healthy	Focus on scale
>50%	Strong	Invest in growth

CM2 Margin	Assessment
Negative	Need to reduce CAC or increase LTV
0-10%	Marginal, need repeat purchases
10-20%	Healthy for new brand
>20%	Excellent, scale aggressively

LTV with Repeat Purchases¶

Critical Insight: First-purchase CM2 (₹149.81) doesn't capture true customer value. D2C brands depend on repeat purchases to justify acquisition costs.

Extended LTV Formula:

LTV = Σ (CM1 per order × Orders per year × (1 - Churn Rate)^year) / (1 + Discount Rate)^year

Simplified:
LTV = CM1 × Average Orders/Year × Customer Lifespan

Worked Example: Repeat Purchase LTV

Assumption	Value
CM1 per order	₹599.81
Orders per year	2.5 (fashion typical)
Customer lifespan	3 years
Annual churn rate	40%
Discount rate	15%

Year-by-Year Calculation:

Year 1: 2.5 orders × ₹599.81 = ₹1,499.53
Year 2: 2.5 × ₹599.81 × (1 - 0.40) = ₹899.72
Year 3: 2.5 × ₹599.81 × (1 - 0.40)² = ₹539.83

Undiscounted LTV = ₹2,939.08

Discounted LTV:
Year 1: ₹1,499.53 / 1.15 = ₹1,303.94
Year 2: ₹899.72 / 1.15² = ₹680.36
Year 3: ₹539.83 / 1.15³ = ₹354.94

LTV (Discounted) = ₹2,339.24

LTV:CAC Analysis:

LTV:CAC = ₹2,339.24 / ₹450
LTV:CAC = 5.2x

LTV Sensitivity Table:

Orders/Year	Churn Rate	LTV	LTV:CAC
1.5	50%	₹1,124	2.5x
2.0	40%	₹1,799	4.0x
2.5	40%	₹2,339	5.2x
3.0	30%	₹3,419	7.6x
4.0	25%	₹5,039	11.2x

Key Insight: First-purchase CM2 of ₹149.81 suggests marginal business, but true LTV:CAC of 5.2x indicates healthy unit economics. D2C brands must track and optimize repeat purchase rate, not just first-order profitability.

Model 5: Business Model Transformation Bridge (Chapter 14)¶

Purpose¶

Model revenue and profitability transition from perpetual license to subscription model (Adobe-style transformation).

Formula¶

Year N Subscription Revenue = Year N-1 Subscribers × (1 - Churn) × Annual Price
                            + New Subscribers × Annual Price × (Months/12)

Perpetual License Decline:
Year N Perpetual = Year N-1 Perpetual × Decline Rate

Total Revenue Bridge:
Total = Perpetual Revenue + Subscription Revenue + Services Revenue

Worked Example: Enterprise Software Company Transformation¶

Given Data (Starting Point - Year 0):

Metric	Value
Perpetual License Revenue	₹500 Cr
Annual Maintenance Revenue	₹150 Cr
Services Revenue	₹100 Cr
Total Revenue	₹750 Cr
Perpetual License Price	₹10,00,000
Annual Subscription Price	₹3,00,000
Existing Customers	500

Assumptions:

Assumption	Value
Perpetual Decline Rate	40% per year
Customer Conversion Rate	30% of perpetual customers/year
New Customer Acquisition	50 per year
Subscription Churn	8% annually
Services Growth	5% annually

Step-by-Step Transformation (5 Years):

Year 1:

Perpetual Revenue = ₹500 Cr × 60% = ₹300 Cr
Converted Customers = 500 × 30% = 150
Subscription Revenue = 150 × ₹3,00,000 = ₹45 Cr
New Customers = 50 × ₹3,00,000 = ₹15 Cr
Total Subscription = ₹60 Cr
Services = ₹100 Cr × 1.05 = ₹105 Cr
Maintenance (declining) = ₹150 Cr × 80% = ₹120 Cr
Total Year 1 = ₹300 + ₹60 + ₹105 + ₹120 = ₹585 Cr

Year 2:

Perpetual = ₹300 Cr × 60% = ₹180 Cr
Existing Subscribers = (150 + 50) × 92% = 184
New Conversions = 350 × 30% = 105
New Acquisitions = 50
Total Subscribers = 184 + 105 + 50 = 339
Subscription Revenue = 339 × ₹3,00,000 = ₹101.7 Cr
Services = ₹105 Cr × 1.05 = ₹110.25 Cr
Maintenance = ₹120 Cr × 80% = ₹96 Cr
Total Year 2 = ₹180 + ₹101.7 + ₹110.25 + ₹96 = ₹488 Cr

Year 3:

Perpetual = ₹180 Cr × 60% = ₹108 Cr
Existing Subscribers = 339 × 92% = 312
New Conversions = 245 × 30% = 74
New Acquisitions = 50
Total Subscribers = 312 + 74 + 50 = 436
Subscription Revenue = 436 × ₹3,00,000 = ₹130.8 Cr
Services = ₹110.25 Cr × 1.05 = ₹115.76 Cr
Maintenance = ₹96 Cr × 80% = ₹76.8 Cr
Total Year 3 = ₹108 + ₹130.8 + ₹115.76 + ₹76.8 = ₹431.4 Cr

Year 4:

Perpetual = ₹108 Cr × 60% = ₹64.8 Cr
Existing Subscribers = 436 × 92% = 401
New Conversions = 171 × 30% = 51
New Acquisitions = 50
Total Subscribers = 401 + 51 + 50 = 502
Subscription Revenue = 502 × ₹3,00,000 = ₹150.6 Cr
Services = ₹115.76 Cr × 1.05 = ₹121.55 Cr
Maintenance = ₹76.8 Cr × 80% = ₹61.4 Cr
Total Year 4 = ₹64.8 + ₹150.6 + ₹121.55 + ₹61.4 = ₹398.4 Cr

Year 5:

Perpetual = ₹64.8 Cr × 60% = ₹38.9 Cr
Existing Subscribers = 502 × 92% = 462
New Conversions = 120 × 30% = 36
New Acquisitions = 50
Total Subscribers = 462 + 36 + 50 = 548
Subscription Revenue = 548 × ₹3,00,000 = ₹164.4 Cr
Services = ₹121.55 Cr × 1.05 = ₹127.63 Cr
Maintenance = ₹61.4 Cr × 80% = ₹49.1 Cr
Total Year 5 = ₹38.9 + ₹164.4 + ₹127.63 + ₹49.1 = ₹380 Cr

Revenue Bridge Summary¶

Year	Perpetual	Subscription	Services	Maintenance	Total	YoY Growth
0	₹500 Cr	₹0 Cr	₹100 Cr	₹150 Cr	₹750 Cr	-
1	₹300 Cr	₹60 Cr	₹105 Cr	₹120 Cr	₹585 Cr	-22%
2	₹180 Cr	₹102 Cr	₹110 Cr	₹96 Cr	₹488 Cr	-17%
3	₹108 Cr	₹131 Cr	₹116 Cr	₹77 Cr	₹432 Cr	-11%
4	₹65 Cr	₹151 Cr	₹122 Cr	₹61 Cr	₹399 Cr	-8%
5	₹39 Cr	₹164 Cr	₹128 Cr	₹49 Cr	₹380 Cr	-5%

Interpretation Guide¶

Revenue Valley:

Trough typically occurs Year 3-4
Revenue decline of 40-50% is common
Recovery begins when subscription revenue > perpetual decline

Key Success Metrics:

Metric	Target
Conversion Rate	>25% annually
Subscription Churn	<10% annually
ARR Growth Rate	>20% by Year 3
Recurring Revenue %	>60% by Year 5

Adobe Reference:

Adobe's transition: 2011-2015
Revenue valley: ~25% decline over 3 years
Stock price initial drop: 35%
10-year outcome: Revenue 4x, Stock 15x

Model 6: Competitive Advantage Scoring - Seven Powers (Chapter 15)¶

Purpose¶

Quantify competitive advantage strength using Hamilton Helmer's Seven Powers framework.

Formula¶

Seven Powers Score:

Each Power scored 0-10:
0-2 = Absent
3-4 = Emerging
5-6 = Developing
7-8 = Strong
9-10 = Dominant

Weighted Score = Σ(Power Score × Weight)
Total Weight = 100%

Power Scores + Weights (Industry Dependent):
1. Scale Economies (15%)
2. Network Effects (20%)
3. Counter-Positioning (10%)
4. Switching Costs (15%)
5. Branding (10%)
6. Cornered Resource (15%)
7. Process Power (15%)

Worked Example: Jio vs Airtel Analysis¶

Scoring Matrix:

Power	Jio Score	Jio Rationale	Airtel Score	Airtel Rationale
Scale Economies	9	400M+ users, lowest cost	8	350M+ users, efficient ops
Network Effects	8	JioPhone ecosystem, content	6	Limited ecosystem
Counter-Positioning	7	Digital-first, data-centric	4	Traditional telco model
Switching Costs	5	Number portability limits	5	Same limitation
Branding	8	"Digital India" association	7	Premium, reliable image
Cornered Resource	9	Spectrum holdings, Ambani capital	6	Good spectrum, less capital
Process Power	7	Rapid execution capability	7	Operational excellence

Step-by-Step Calculation (Jio):

Scale Economies:    9 × 0.15 = 1.35
Network Effects:    8 × 0.20 = 1.60
Counter-Positioning: 7 × 0.10 = 0.70
Switching Costs:    5 × 0.15 = 0.75
Branding:           8 × 0.10 = 0.80
Cornered Resource:  9 × 0.15 = 1.35
Process Power:      7 × 0.15 = 1.05

Total Jio Score = 7.60 / 10

Step-by-Step Calculation (Airtel):

Scale Economies:    8 × 0.15 = 1.20
Network Effects:    6 × 0.20 = 1.20
Counter-Positioning: 4 × 0.10 = 0.40
Switching Costs:    5 × 0.15 = 0.75
Branding:           7 × 0.10 = 0.70
Cornered Resource:  6 × 0.15 = 0.90
Process Power:      7 × 0.15 = 1.05

Total Airtel Score = 6.20 / 10

Results Summary¶

Company	Seven Powers Score	Competitive Position
Jio	7.60	Strong Leader
Airtel	6.20	Strong Challenger
Difference	+1.40	Significant Gap

Interpretation Guide¶

Score Range	Competitive Position	Strategic Implication
0-3	Weak	Vulnerable to disruption
3-5	Moderate	Needs strategic investment
5-7	Strong	Defensible position
7-9	Very Strong	Market leadership likely
9-10	Dominant	Near-monopoly power

Power Priority by Industry:

Industry	Top 3 Powers (Highest Weight)
Tech/SaaS	Network Effects, Switching Costs, Scale
Consumer	Branding, Scale, Process
Industrial	Scale, Process, Cornered Resource
Financial Services	Scale, Switching Costs, Process

Model 7: Moat Strength Scoring (Chapter 16)¶

Purpose¶

Evaluate business moat across three dimensions: Width, Depth, and Durability.

Formula¶

Moat Strength Index (MSI):
MSI = (Width × Depth × Durability) / 1000

Where:
- Width (0-10): How many competitive advantages exist
- Depth (0-10): How strong is each advantage
- Durability (0-10): How long will advantages last

Maximum Score = (10 × 10 × 10) / 1000 = 1.0

Worked Example: Zerodha Analysis¶

Dimension Scoring:

Width Score (How many moats?):

Moat Type	Present?	Strength
Cost leadership	Yes	Strong
Brand recognition	Yes	Strong
Network effects	Limited	Weak
Switching costs	Yes	Moderate
Technology	Yes	Strong
Regulatory advantage	Yes	Moderate

Width Score: 6 clear moats → Score: 7/10

Depth Score (How deep is each moat?):

Moat	Depth Assessment
Cost leadership	Deep - ₹0 delivery, ₹20 flat fee hard to match
Brand	Deep - "Zerodha = discount broker" mindshare
Technology	Moderate - Kite platform good but replicable
Switching costs	Shallow - Easy to move accounts

Average Depth: 7/10

Durability Score (How long will moats last?):

Factor	Assessment
Regulatory moat	5+ years - SEBI favorable
Cost advantage	3-5 years - Competition closing
Brand	5+ years - Trust built
Technology	2-3 years - Rapidly evolving

Weighted Durability: 7/10

Step-by-Step MSI Calculation:

MSI = (Width × Depth × Durability) / 1000
MSI = (7 × 7 × 7) / 1000
MSI = 343 / 1000
MSI = 0.343

Moat Analysis Summary¶

Dimension	Score	Assessment
Width	7/10	Multiple, diverse moats
Depth	7/10	Generally strong moats
Durability	7/10	Medium-term sustainable
MSI	0.343	Moderate-Strong Moat

Interpretation Guide¶

MSI Score	Moat Strength	Investment Implication
0-0.1	No moat	Avoid - commodity business
0.1-0.25	Weak moat	Caution - limited pricing power
0.25-0.5	Moderate moat	Investable with monitoring
0.5-0.75	Strong moat	Attractive long-term hold
0.75-1.0	Wide moat	Premium valuation justified

Comparative MSI Scores (Indian Context):

Company	Width	Depth	Durability	MSI
TCS	8	8	8	0.512
Zerodha	7	7	7	0.343
Jio	9	8	7	0.504
Zomato	7	6	6	0.252
Ola	5	4	4	0.080

Model 8: Disruption Vulnerability Index (Chapter 17)¶

Purpose¶

Assess how vulnerable an incumbent is to disruption using Christensen's framework.

Formula¶

Disruption Vulnerability Index (DVI):

DVI = (MP + CSG + TS + OF + SRC) / 5

Where (each scored 0-10):
MP = Margin Pressure Score
CSG = Customer Satisfaction Gap Score
TS = Technology Shift Score
OF = Organizational Flexibility Score (inverted - high flexibility = low vulnerability)
SRC = Strategic Response Capability Score (inverted)

Higher DVI = More Vulnerable to Disruption

Worked Example: Traditional Banks vs Fintech¶

Scoring: Traditional Large Bank

Dimension	Score	Rationale
Margin Pressure (MP)	8	NIMs compressing, fee income under attack
Customer Satisfaction Gap (CSG)	7	Mobile-first customers underserved
Technology Shift (TS)	9	AI/ML, embedded finance disrupting
Organizational Flexibility (OF)	3	Legacy systems, bureaucracy (inverted: 10-3=7)
Strategic Response Capability (SRC)	4	Slow decision making (inverted: 10-4=6)

Step-by-Step Calculation:

For inverted scores (OF, SRC), we use: 10 - raw score

MP = 8
CSG = 7
TS = 9
OF (inverted) = 10 - 3 = 7 (low flexibility = high vulnerability)
SRC (inverted) = 10 - 4 = 6 (low capability = high vulnerability)

DVI = (8 + 7 + 9 + 7 + 6) / 5
DVI = 37 / 5
DVI = 7.4

Scoring: Zerodha (Fintech)

Dimension	Score	Rationale
Margin Pressure (MP)	4	Already at floor pricing
Customer Satisfaction Gap (CSG)	3	Strong NPS, good UX
Technology Shift (TS)	5	Could be disrupted by new tech
Organizational Flexibility (OF)	8	Agile team (inverted: 10-8=2)
Strategic Response Capability (SRC)	8	Quick decision making (inverted: 10-8=2)

DVI = (4 + 3 + 5 + 2 + 2) / 5
DVI = 16 / 5
DVI = 3.2

Comparison Summary¶

Entity	DVI Score	Vulnerability Level
Traditional Bank	7.4	High
Zerodha	3.2	Low
Gap	4.2	Significant

Interpretation Guide¶

DVI Score	Vulnerability	Strategic Implication
0-2	Very Low	Disruptor position, maintain agility
2-4	Low	Strong position, watch for shifts
4-6	Moderate	Active transformation needed
6-8	High	Urgent disruption risk
8-10	Critical	Business model at existential risk

Dimension Deep-Dive¶

Margin Pressure Indicators:

Factor	Low (0-3)	Medium (4-6)	High (7-10)
Price competition	Stable	Increasing	Intense
Cost structure	Flexible	Mixed	Fixed/Legacy
Commoditization	Differentiated	Some	High

Customer Satisfaction Gap Indicators:

Factor	Low (0-3)	Medium (4-6)	High (7-10)
NPS	>50	20-50	<20
Complaint ratio	<1%	1-5%	>5%
Unserved segments	None	Some	Many

Model 9: HHI/CR4 Market Concentration (Chapter 18)¶

Purpose¶

Measure market concentration to assess competitive intensity and regulatory risk.

Formulas¶

Herfindahl-Hirschman Index (HHI):
HHI = Σ(Si)²
Where Si = Market share of firm i (as whole number, not decimal)

Concentration Ratio (CR4):
CR4 = S1 + S2 + S3 + S4
Where S1-S4 = Market shares of top 4 firms (as percentage)

Worked Example: Indian Telecom Market¶

Given Data:

Company	Market Share	Subscribers (M)
Jio	40.2%	450
Airtel	31.4%	352
Vi (Vodafone Idea)	18.7%	210
BSNL	8.1%	91
Others	1.6%	18

Step-by-Step HHI Calculation:

HHI = (40.2)² + (31.4)² + (18.7)² + (8.1)² + (1.6)²

HHI = 1,616.04 + 985.96 + 349.69 + 65.61 + 2.56

HHI = 3,019.86

Step-by-Step CR4 Calculation:

CR4 = 40.2% + 31.4% + 18.7% + 8.1%
CR4 = 98.4%

Results Analysis¶

Metric	Value	Assessment
HHI	3,020	Highly Concentrated
CR4	98.4%	Oligopoly
Market Leader Share	40.2%	Near-dominance
2-Firm Concentration	71.6%	Duopoly forming

Interpretation Guide¶

HHI Thresholds (DoJ/FTC Guidelines):

HHI Range	Market Classification	Merger Scrutiny
<1,000	Unconcentrated	Low
1,000-1,500	Moderately Concentrated	Moderate
1,500-2,500	Concentrated	High
>2,500	Highly Concentrated	Intense

CR4 Thresholds:

CR4 Range	Market Structure	Pricing Power
<40%	Competitive	Low
40-60%	Moderately Concentrated	Moderate
60-80%	Oligopoly	High
>80%	Tight Oligopoly/Near Monopoly	Very High

Industry Comparisons (India)¶

Industry	HHI	CR4	Structure
Telecom	3,020	98.4%	Duopoly forming
Payments (UPI)	4,500+	95%+	Duopoly (PhonePe/GPay)
E-commerce	2,800	85%	Concentrated
Airlines (Domestic)	2,600	90%	Oligopoly
FMCG	1,200	55%	Moderately competitive
IT Services	900	40%	Competitive

Strategic Implications¶

HHI Level	For Incumbents	For Entrants
<1,500	Compete on execution	Viable entry
1,500-2,500	Build moats	Niche entry possible
>2,500	Defend position	Entry very difficult

Regulatory Risk Assessment:

HHI > 2,500: CCI likely to scrutinize any M&A
Change in HHI > 200: Triggers regulatory review
Market leader > 40%: Dominant position concerns

Model 10: Game Theory Payoff Matrices (Chapter 19)¶

Purpose¶

Analyze competitive decisions using game theory to identify Nash equilibrium and optimal strategies.

Framework¶

Payoff Matrix Structure:
             Player B
           Option 1    Option 2
Player A   (A1,B1)     (A2,B2)
Option 1
Option 2   (A3,B3)     (A4,B4)

Where (Ax, Bx) = (Player A's payoff, Player B's payoff)

Nash Equilibrium: No player can improve by unilaterally changing strategy

Worked Example: Telecom Price War (Jio vs Airtel)¶

Scenario: Both companies deciding whether to cut prices by ₹50/month

Payoff Assumptions (Monthly Profit Change in ₹ Cr):

Market size: ₹20,000 Cr monthly revenue
Price cut gains 5% market share if competitor doesn't cut
Price cut loses ₹800 Cr industry profit if both cut
Maintaining price while competitor cuts loses 5% share

Constructing the Matrix:

Scenario	Jio Action	Airtel Action	Jio Profit Δ	Airtel Profit Δ
Both hold	Maintain	Maintain	0	0
Jio cuts	Cut	Maintain	+200	-150
Airtel cuts	Maintain	Cut	-150	+180
Both cut	Cut	Cut	-400	-400

Payoff Matrix:

                          Airtel
                   Maintain Price    Cut Price
              ┌──────────────────┬──────────────────┐
   Maintain   │                  │                  │
    Price     │   (0, 0)         │  (-150, +180)    │
Jio           ├──────────────────┼──────────────────┤
              │                  │                  │
   Cut Price  │  (+200, -150)    │  (-400, -400)    │
              │                  │                  │
              └──────────────────┴──────────────────┘

Step-by-Step Nash Equilibrium Analysis:

Step 1: Find Jio's Best Response to each Airtel strategy

If Airtel Maintains: Jio gets 0 (Maintain) vs +200 (Cut) → Jio Cuts
If Airtel Cuts: Jio gets -150 (Maintain) vs -400 (Cut) → Jio Maintains

Step 2: Find Airtel's Best Response to each Jio strategy

If Jio Maintains: Airtel gets 0 (Maintain) vs +180 (Cut) → Airtel Cuts
If Jio Cuts: Airtel gets -150 (Maintain) vs -400 (Cut) → Airtel Maintains

Step 3: Identify Nash Equilibrium

No pure strategy Nash equilibrium exists!

Best responses don't align:
- (Maintain, Maintain): Both want to deviate to Cut
- (Cut, Cut): Both want to deviate to Maintain
- (Cut, Maintain): Airtel wants to Cut
- (Maintain, Cut): Jio wants to Cut

Step 4: Mixed Strategy Equilibrium

Let p = probability Jio maintains price
Let q = probability Airtel maintains price

Jio indifferent when:
0(q) + (-150)(1-q) = 200(q) + (-400)(1-q)
-150 + 150q = 200q - 400 + 400q
-150 + 150q = 600q - 400
250 = 450q
q = 0.556 (55.6%)

Similarly, p ≈ 0.52 (52%)

Mixed Strategy Nash: Both maintain with ~55% probability

Interpretation Guide¶

Nash Equilibrium Types:

Type	Meaning	Strategic Implication
Dominant Strategy	Best regardless of opponent	Clear optimal choice
Pure Nash	Stable with certainty	Predictable outcome
Mixed Nash	Randomize strategies	Unpredictable competition
No Nash	Unstable dynamics	Price war likely

Common Game Structures:

Game Type	Example	Nash Outcome
Prisoner's Dilemma	Price wars	Both defect (cut prices)
Coordination Game	Technology standards	One standard emerges
Chicken Game	Market entry	One backs down
Battle of Sexes	Platform choice	Coordination with preference

Real-World Application¶

Telecom Price War Outcome:

Mixed equilibrium suggests periodic price cuts
Actual behavior: Aggressive initially, stabilizing over time
Jio's deep pockets changed payoff structure
Market moved toward tacit cooperation post-Vi weakness

Model 11: Revenue Model Comparison (Chapter 8)¶

Purpose¶

Compare long-term value creation across different revenue model architectures.

Formula¶

5-Year Revenue Projection:

One-Time Model:
Year N Revenue = New Customers × Average Deal Size

Subscription Model:
Year N Revenue = (Previous Subscribers × Retention Rate + New Subscribers) × Annual Price

Freemium Model:
Year N Revenue = Total Users × Conversion Rate × ARPU

Usage-Based Model:
Year N Revenue = Active Users × Average Usage × Price per Unit

Worked Example: 5-Year Model Comparison¶

Assumptions (Same Starting Point):

Metric	Value
Year 1 New Customers	1,000
Average Deal Size (One-time)	₹1,00,000
Annual Subscription	₹30,000
Subscription Retention	85%
New Customer Growth	20% YoY
Freemium Users Growth	50% YoY
Freemium Conversion	3%
Freemium ARPU	₹15,000
Usage ARPU	₹25,000
Usage Retention	80%

Model 1: One-Time Revenue

Year 1: 1,000 × ₹1,00,000 = ₹10.0 Cr
Year 2: 1,200 × ₹1,00,000 = ₹12.0 Cr
Year 3: 1,440 × ₹1,00,000 = ₹14.4 Cr
Year 4: 1,728 × ₹1,00,000 = ₹17.3 Cr
Year 5: 2,074 × ₹1,00,000 = ₹20.7 Cr

5-Year Total: ₹74.4 Cr

Model 2: Subscription Revenue

Year 1: 1,000 × ₹30,000 = ₹3.0 Cr
Year 2: (1,000 × 0.85 + 1,200) × ₹30,000 = 2,050 × ₹30,000 = ₹6.15 Cr
Year 3: (2,050 × 0.85 + 1,440) × ₹30,000 = 3,183 × ₹30,000 = ₹9.55 Cr
Year 4: (3,183 × 0.85 + 1,728) × ₹30,000 = 4,434 × ₹30,000 = ₹13.30 Cr
Year 5: (4,434 × 0.85 + 2,074) × ₹30,000 = 5,843 × ₹30,000 = ₹17.53 Cr

5-Year Total: ₹49.53 Cr

Model 3: Freemium Revenue

Year 1: 50,000 users × 3% × ₹15,000 = ₹2.25 Cr
Year 2: 75,000 users × 3% × ₹15,000 = ₹3.38 Cr
Year 3: 112,500 users × 3% × ₹15,000 = ₹5.06 Cr
Year 4: 168,750 users × 3% × ₹15,000 = ₹7.59 Cr
Year 5: 253,125 users × 3% × ₹15,000 = ₹11.39 Cr

5-Year Total: ₹29.67 Cr

Model 4: Usage-Based Revenue

Year 1: 1,000 × ₹25,000 = ₹2.5 Cr
Year 2: (1,000 × 0.80 + 1,200) × ₹25,000 × 1.1 = 2,000 × ₹27,500 = ₹5.5 Cr
Year 3: (2,000 × 0.80 + 1,440) × ₹30,250 = 3,040 × ₹30,250 = ₹9.2 Cr
Year 4: (3,040 × 0.80 + 1,728) × ₹33,275 = 4,160 × ₹33,275 = ₹13.8 Cr
Year 5: (4,160 × 0.80 + 2,074) × ₹36,603 = 5,402 × ₹36,603 = ₹19.8 Cr

5-Year Total: ₹50.8 Cr

5-Year Comparison Summary¶

Model	Y1	Y2	Y3	Y4	Y5	Total	CAGR
One-Time	₹10.0	₹12.0	₹14.4	₹17.3	₹20.7	₹74.4	20%
Subscription	₹3.0	₹6.2	₹9.6	₹13.3	₹17.5	₹49.5	55%
Freemium	₹2.3	₹3.4	₹5.1	₹7.6	₹11.4	₹29.7	50%
Usage-Based	₹2.5	₹5.5	₹9.2	₹13.8	₹19.8	₹50.8	68%

Interpretation Guide¶

Model Selection Criteria:

Criterion	Best Model
Immediate revenue	One-Time
Predictable revenue	Subscription
User growth priority	Freemium
Value-aligned pricing	Usage-Based
Customer relationship	Subscription/Usage
Valuation multiple	Subscription (highest)

SaaS Valuation Impact:

Revenue Type	Typical EV/Revenue Multiple
One-Time	1-3x
Subscription (Low NRR)	3-6x
Subscription (High NRR)	8-15x
Usage-Based	6-12x
Freemium	4-8x

Model 12: TAM/SAM/SOM Market Sizing (Chapter 5)¶

Purpose¶

Size market opportunity using three complementary methodologies.

Formulas¶

TAM (Total Addressable Market):
TAM = Total potential customers × Average revenue per customer

SAM (Serviceable Available Market):
SAM = TAM × Geographic/Segment constraints

SOM (Serviceable Obtainable Market):
SOM = SAM × Realistic market share capture

Three Methodologies:
1. Top-Down: Start with industry data, filter down
2. Bottom-Up: Unit economics × addressable customers
3. Value-Theory: Value created × capture percentage

Worked Example: Indian Online Brokerage Market¶

Method 1: Top-Down Approach

Step 1: India's Total Investment Pool
Total Indian Household Financial Assets: ₹280 Lakh Cr (2024)
Equity Allocation: 15% = ₹42 Lakh Cr

Step 2: Filter to Active Trading
Active Trading Segment: 20% of equity pool
TAM = ₹42 Lakh Cr × 20% = ₹8.4 Lakh Cr tradeable assets

Step 3: Revenue Potential
Average Commission Yield: 0.1% of traded value
Annual Turnover: 5x average holdings
Revenue TAM = ₹8.4 Lakh Cr × 5 × 0.1% = ₹4,200 Cr

Step 4: SAM (Online brokers only)
Online penetration: 70% of trades
SAM = ₹4,200 Cr × 70% = ₹2,940 Cr

Step 5: SOM (Realistic share)
Achievable market share: 30%
SOM = ₹2,940 Cr × 30% = ₹882 Cr

Method 2: Bottom-Up Approach

Step 1: Count Potential Customers
Demat Accounts in India: 14 Cr
Active Traders (trade >1x/month): 15% = 2.1 Cr accounts

Step 2: Revenue per Customer
Average trades/year: 120
Average trade value: ₹15,000
Average commission: ₹20/trade

ARPU = 120 × ₹20 = ₹2,400/year

Step 3: TAM Calculation
TAM = 2.1 Cr × ₹2,400 = ₹5,040 Cr

Step 4: SAM (Digital-savvy segment)
Digital platform preference: 60%
SAM = ₹5,040 Cr × 60% = ₹3,024 Cr

Step 5: SOM
Realistic capture: 25%
SOM = ₹3,024 Cr × 25% = ₹756 Cr

Method 3: Value-Theory Approach

Step 1: Value Created for Customer
Traditional broker cost: ₹500/trade average
Discount broker cost: ₹20/trade
Value created: ₹480/trade

Step 2: Total Value Pool
Active traders: 2.1 Cr
Avg trades/year: 120
Total value created = 2.1 Cr × 120 × ₹480 = ₹1,20,960 Cr

Step 3: Value Capture Rate
Typical SaaS value capture: 10-20%
Conservative capture: 5%
TAM = ₹1,20,960 Cr × 5% = ₹6,048 Cr

Step 4: Serviceable portion
Digital segment: 50%
SAM = ₹6,048 Cr × 50% = ₹3,024 Cr

Step 5: Obtainable share
SOM = ₹3,024 Cr × 25% = ₹756 Cr

Cross-Validation Summary¶

Method	TAM	SAM	SOM
Top-Down	₹4,200 Cr	₹2,940 Cr	₹882 Cr
Bottom-Up	₹5,040 Cr	₹3,024 Cr	₹756 Cr
Value-Theory	₹6,048 Cr	₹3,024 Cr	₹756 Cr
Average	₹5,096 Cr	₹2,996 Cr	₹798 Cr

Interpretation Guide¶

TAM/SAM/SOM Ratios:

Ratio	Typical Range	Red Flag
SAM/TAM	20-60%	<10% or >80%
SOM/SAM	5-30%	>50%
SOM/TAM	2-15%	>30%

Methodology Selection:

Situation	Best Method
New market	Value-Theory
Established market	Top-Down
Investor pitch	All three (cross-validate)
Internal planning	Bottom-Up

Reality Check (Zerodha):

Zerodha FY24 Revenue: ₹8,320 Cr
Our SOM estimate: ₹798 Cr
Delta explained by: MTF interest, larger ARPU, market growth since base data

Model 13: Unit Economics Deep Dive (Chapter 24)¶

Purpose¶

Comprehensive unit economics analysis with sensitivity testing for strategic decision-making.

Framework¶

Full Unit Economics Stack:

Revenue per Unit
├─ Gross Revenue
├─ Discounts & Returns
└─ Net Revenue (NR)

Variable Costs per Unit
├─ COGS
├─ Fulfillment
├─ Payment Processing
└─ Variable Marketing

= Contribution Margin 1 (CM1)

Allocated Costs per Unit
├─ Customer Acquisition (CAC)
├─ Customer Service
└─ Variable Overhead

= Contribution Margin 2 (CM2)

Unit-Level Profitability
├─ Repeat Purchase Factor
├─ Customer Lifetime
└─ Lifetime Contribution

= Customer Lifetime Value (CLV)

Worked Example: D2C Beauty Brand¶

Base Case Input Data:

Category	Item	Value
Revenue	MRP	₹999
	Discount	20%
	Return Rate	12%
COGS	Product Cost	₹200
	Packaging	₹35
Fulfillment	Shipping (Forward)	₹55
	Shipping (RTO)	₹45 (8% RTO rate)
Payment	Gateway Fee	2.2%
	COD Handling	₹25 (35% COD)
Marketing	CAC	₹350
Lifetime	Repeat Rate	35%
	Avg Lifetime Orders	2.4

Step-by-Step Calculation:

1. Net Revenue per Order:

Gross Revenue = ₹999
Discount = ₹999 × 20% = ₹199.80
Net Revenue (before returns) = ₹799.20
Return Adjustment = ₹799.20 × 12% = ₹95.90
Effective Net Revenue = ₹799.20 - ₹95.90 = ₹703.30

2. COGS:

Product Cost = ₹200
Packaging = ₹35
Total COGS = ₹235

3. Fulfillment Costs:

Forward Shipping = ₹55
RTO Cost = ₹45 × 8% = ₹3.60
Return Shipping = ₹55 × 12% = ₹6.60
Total Fulfillment = ₹65.20

4. Payment Costs:

Gateway Fee = ₹799.20 × 2.2% = ₹17.58
COD Handling = ₹25 × 35% = ₹8.75
Total Payment = ₹26.33

5. CM1 Calculation:

CM1 = ₹703.30 - ₹235 - ₹65.20 - ₹26.33
CM1 = ₹376.77
CM1 Margin = ₹376.77 / ₹703.30 = 53.6%

6. CM2 (After CAC):

CM2 = ₹376.77 - ₹350
CM2 = ₹26.77
CM2 Margin = 3.8%

7. Customer Lifetime Value:

CLV = CM1 × Lifetime Orders - CAC
CLV = ₹376.77 × 2.4 - ₹350
CLV = ₹904.25 - ₹350
CLV = ₹554.25

8. LTV:CAC Ratio:

LTV:CAC = ₹904.25 / ₹350
LTV:CAC = 2.58:1

Sensitivity Analysis¶

Variable: Discount Rate

Discount	Net Revenue	CM1	CM1 Margin
10%	₹792.42	₹465.89	58.8%
15%	₹747.86	₹421.33	56.3%
20% (Base)	₹703.30	₹376.77	53.6%
25%	₹658.74	₹332.21	50.4%
30%	₹614.18	₹287.65	46.8%

Variable: Return Rate

Return Rate	Effective Revenue	CM1	Impact vs Base
8%	₹735.26	₹408.73	+8.5%
10%	₹719.28	₹392.75	+4.2%
12% (Base)	₹703.30	₹376.77	-
15%	₹679.32	₹352.79	-6.4%
20%	₹639.36	₹312.83	-17.0%

Variable: CAC

CAC	CM2	LTV:CAC	Payback Orders
₹250	₹126.77	3.62:1	0.66
₹300	₹76.77	3.01:1	0.80
₹350 (Base)	₹26.77	2.58:1	0.93
₹400	-₹23.23	2.26:1	1.06
₹450	-₹73.23	2.01:1	1.19

Breakeven Analysis¶

Breakeven CAC (for CM2 = 0):
Max CAC = CM1 = ₹376.77

Breakeven Discount (for CM1 = CAC):
₹999 × (1 - D) × 0.88 - ₹326.53 = ₹350
D = 23.0%

Breakeven Return Rate (for CM1 = CAC):
₹703.30 × (1 - R) - ₹326.53 = ₹350
R = 3.8% → Target return rate

Interpretation Guide¶

Metric	Poor	Acceptable	Good	Excellent
CM1 Margin	<30%	30-45%	45-55%	>55%
CM2 Margin	<0%	0-5%	5-15%	>15%
LTV:CAC	<2:1	2-3:1	3-4:1	>4:1
CAC Payback	>3 orders	2-3 orders	1-2 orders	<1 order

Model 14: Platform Economics Model (Chapter 11)¶

Purpose¶

Model two-sided marketplace economics including GMV, take rates, and per-transaction contribution.

Framework¶

Platform Economics Flow:

Supply Side (Sellers)
├─ Number of Sellers
├─ Avg Listings per Seller
├─ Avg Transaction Value
└─ Seller Take Rate

Demand Side (Buyers)
├─ Number of Buyers
├─ Purchase Frequency
├─ Basket Size
└─ Buyer Fees

Platform Economics
├─ GMV = Buyers × Frequency × Basket
├─ Revenue = GMV × (Seller Take + Buyer Take)
├─ Variable Costs = Payment + Support + Trust
└─ Contribution = Revenue - Variable Costs

Worked Example: Urban Company-Style Services Marketplace¶

Platform Data:

Metric	Value
Active Service Providers	40,000
Active Customers (Monthly)	8,00,000
Avg Bookings/Customer/Month	1.2
Avg Booking Value	₹800
Platform Commission	25%
Customer Convenience Fee	₹29

Step-by-Step Calculations:

1. Monthly GMV:

Total Bookings = 8,00,000 × 1.2 = 9,60,000 bookings/month
GMV = 9,60,000 × ₹800 = ₹76.8 Cr/month
Annual GMV = ₹76.8 Cr × 12 = ₹921.6 Cr

2. Revenue Calculation:

Commission Revenue = ₹76.8 Cr × 25% = ₹19.2 Cr/month
Convenience Fee Revenue = 9,60,000 × ₹29 = ₹2.78 Cr/month
Total Revenue = ₹21.98 Cr/month
Annual Revenue = ₹263.8 Cr

3. Effective Take Rate:

Effective Take Rate = ₹21.98 Cr / ₹76.8 Cr × 100%
Effective Take Rate = 28.6%

4. Variable Costs per Transaction:

Payment Processing = ₹800 × 2.0% = ₹16
Customer Support = ₹8 per transaction
Trust & Safety = ₹5 per transaction
Provider Incentives = ₹20 per transaction (avg)
Total Variable Cost = ₹49 per transaction

5. Per-Transaction Economics:

Revenue per Transaction:
  Commission: ₹800 × 25% = ₹200
  Convenience Fee: ₹29
  Total Revenue: ₹229

Variable Costs: ₹49

Contribution per Transaction = ₹229 - ₹49 = ₹180
Contribution Margin = ₹180 / ₹229 = 78.6%

6. Monthly Platform Contribution:

Monthly Contribution = 9,60,000 × ₹180 = ₹17.28 Cr
Annual Contribution = ₹207.4 Cr

Platform Economics Summary¶

Metric	Monthly	Annual
GMV	₹76.8 Cr	₹921.6 Cr
Revenue	₹21.98 Cr	₹263.8 Cr
Take Rate (Effective)	28.6%	28.6%
Variable Costs	₹4.70 Cr	₹56.4 Cr
Contribution	₹17.28 Cr	₹207.4 Cr
Contribution Margin	78.6%	78.6%

Network Effects Analysis¶

Liquidity Metrics:

Provider Utilization = Bookings / Providers = 9,60,000 / 40,000 = 24 bookings/provider/month
Customer Fill Rate = Successful Bookings / Attempts = Target >95%
Time to Match = Average minutes to confirm provider = Target <5 mins

Cross-Side Network Effects:

Provider Value = f(Customer Base)
More customers → More bookings → Higher provider earnings → More providers join

Customer Value = f(Provider Base)
More providers → Better availability → Lower wait times → More customers

Interpretation Guide¶

Take Rate	Platform Stage	Sustainability
0-10%	Growth/Subsidy phase	Unsustainable
10-20%	Scaling phase	Path to profit
20-30%	Mature platform	Healthy economics
30-40%	Strong position	Premium platform
>40%	Dominant	Disintermediation risk

Marketplace Health Metrics:

Metric	Unhealthy	Developing	Healthy	Strong
Take Rate	<10%	10-20%	20-30%	>30%
Contribution Margin	<50%	50-65%	65-80%	>80%
Provider Utilization	<10	10-20	20-40	>40
Repeat Rate	<20%	20-40%	40-60%	>60%

Model 15: Strategic Investment Analysis (Chapter 26)¶

Purpose¶

Evaluate strategic investments using risk-adjusted NPV and scenario analysis.

Formulas¶

Net Present Value (NPV):
NPV = Σ[CFt / (1 + r)^t] - Initial Investment
Where:
  CFt = Cash flow in year t
  r = Discount rate (risk-adjusted)
  t = Time period

Risk-Adjusted Discount Rate:
r = Rf + β(Rm - Rf) + α
Where:
  Rf = Risk-free rate
  β = Beta (systematic risk)
  Rm - Rf = Market risk premium
  α = Company-specific risk premium

Expected NPV:
E(NPV) = P(Bull) × NPV(Bull) + P(Base) × NPV(Base) + P(Bear) × NPV(Bear)

Worked Example: SaaS Product Investment¶

Investment Parameters:

Parameter	Value
Initial Investment	₹50 Cr
Investment Timeline	5 years
Risk-Free Rate	7.0%
Market Risk Premium	6.0%
Company Beta	1.4
Company Risk Premium	3.0%

Step 1: Calculate Risk-Adjusted Discount Rate

r = 7.0% + 1.4 × 6.0% + 3.0%
r = 7.0% + 8.4% + 3.0%
r = 18.4%

Step 2: Project Cash Flows (Base Case)

Year	Revenue	Operating Margin	Operating CF	Growth Rate
1	₹15 Cr	-20%	-₹3 Cr	-
2	₹30 Cr	10%	₹3 Cr	100%
3	₹50 Cr	25%	₹12.5 Cr	67%
4	₹75 Cr	35%	₹26.25 Cr	50%
5	₹100 Cr	40%	₹40 Cr	33%

Terminal Value (Year 5):

Terminal Value = Year 5 CF × (1 + g) / (r - g)
Terminal Value = ₹40 Cr × 1.05 / (0.184 - 0.05)
Terminal Value = ₹42 Cr / 0.134
Terminal Value = ₹313.4 Cr

Step 3: Calculate NPV (Base Case)

PV of Cash Flows:
Year 1: -₹3 Cr / (1.184)^1 = -₹2.53 Cr
Year 2: ₹3 Cr / (1.184)^2 = ₹2.14 Cr
Year 3: ₹12.5 Cr / (1.184)^3 = ₹7.53 Cr
Year 4: ₹26.25 Cr / (1.184)^4 = ₹13.37 Cr
Year 5: ₹40 Cr / (1.184)^5 = ₹17.22 Cr
Year 5 TV: ₹313.4 Cr / (1.184)^5 = ₹134.89 Cr

Total PV of Cash Flows = -₹2.53 + ₹2.14 + ₹7.53 + ₹13.37 + ₹17.22 + ₹134.89
Total PV = ₹172.62 Cr

NPV = ₹172.62 Cr - ₹50 Cr = ₹122.62 Cr

Step 4: Scenario Analysis

Bull Case (30% probability): Revenue 30% higher, margins 5% better

Year 5 Revenue: ₹130 Cr
Year 5 Margin: 45%
Year 5 CF: ₹58.5 Cr
Terminal Value: ₹457.8 Cr
NPV (Bull) = ₹228.4 Cr

Bear Case (20% probability): Revenue 30% lower, margins 10% worse

Year 5 Revenue: ₹70 Cr
Year 5 Margin: 30%
Year 5 CF: ₹21 Cr
Terminal Value: ₹164.2 Cr
NPV (Bear) = ₹48.6 Cr

Step 5: Expected NPV

E(NPV) = 0.30 × ₹228.4 + 0.50 × ₹122.62 + 0.20 × ₹48.6
E(NPV) = ₹68.52 + ₹61.31 + ₹9.72
E(NPV) = ₹139.55 Cr

Step 6: Risk Metrics

IRR Calculation (Base Case):
₹50 = -₹3/(1+IRR) + ₹3/(1+IRR)² + ₹12.5/(1+IRR)³ + ₹26.25/(1+IRR)⁴ + (₹40+₹313.4)/(1+IRR)⁵
IRR ≈ 52%

Payback Period:
Cumulative CF: Y1: -₹3, Y2: ₹0, Y3: ₹12.5, Y4: ₹38.75
Initial Investment: ₹50 Cr
Payback: ~4.3 years (investment recovered during Y5)

Return Multiple:
Total Value Created / Investment = ₹172.62 / ₹50 = 3.45x

Investment Decision Summary¶

Metric	Value	Threshold	Decision
NPV (Base)	₹122.62 Cr	> ₹0	PASS
E(NPV)	₹139.55 Cr	> ₹0	PASS
IRR	52%	> 18.4% (WACC)	PASS
Payback	4.3 years	< 5 years	PASS
Return Multiple	3.45x	> 2.0x	PASS
Bear Case NPV	₹48.6 Cr	> ₹0	PASS

Recommendation: INVEST

Interpretation Guide¶

NPV Decision Rules:

NPV	Decision	Rationale
Negative	Reject	Destroys value
₹0 - Expected	Consider	Meeting hurdle rate
> Expected	Accept	Creating excess value

IRR Interpretation:

IRR vs WACC	Interpretation
IRR < WACC	Value destruction
IRR = WACC	Breaking even
IRR > WACC + 5%	Good investment
IRR > WACC + 15%	Excellent investment

Risk-Adjusted Discount Rates by Stage:

Company Stage	Base Rate	Risk Premium	Total Rate
Mature SaaS	12%	2-4%	14-16%
Growth SaaS	12%	4-8%	16-20%
Early Stage	12%	10-15%	22-27%
Pre-Revenue	12%	20-30%	32-42%

Quick Reference Card¶

Key Metrics by Business Model¶

Model	Primary Metrics	Benchmark
SaaS	NRR, CAC Payback, Rule of 40	120%, <12mo, >40
Marketplace	GMV, Take Rate, Liquidity	-, 15-25%, >80%
Fintech	NIM, Expected Loss, Float	4-6%, <2%, Maximize
D2C	CM1, CM2, LTV:CAC	>40%, >10%, >3:1
Platform	Take Rate, CM per Txn	20-30%, >70%

Universal Investment Criteria¶

Metric	Minimum	Target	Excellent
NPV	> ₹0	> Investment	> 2× Investment
IRR	> WACC	> WACC + 10%	> 40%
Payback	< 5 years	< 3 years	< 2 years
Return Multiple	> 1.5×	> 2.5×	> 4×

Document Version: 1.0 Last Updated: 2024 Source: The Strategy Engine - Quantitative Models Reference