Industry Overlays

Industry-specific overlays applied automatically via /set-context: Technology/SaaS, Healthcare, Financial Services, Consumer/Retail, Industrial/Manufacturing.

On this page

• Industry Overlay: Consumer / Retail
• Industry Overlay: Financial Services
• Industry Overlay: Healthcare
• Industry Overlay: Industrial / Manufacturing
• Industry Overlay: Technology / SaaS

Industry Overlay: Consumer / Retail

Apply these industry-specific adaptations when analyzing consumer goods companies, retailers, and D2C brands.

Industry Structure

Key Sub-Sectors

| Sub-Sector | Key Players | Revenue Model | Key Metrics | |-----------|------------|---------------|-------------| | Grocery / Supermarkets | Walmart, Kroger, Costco, Aldi | Product sales (thin margins, high volume) | Same-store sales, basket size, foot traffic | | Apparel & Fashion | Nike, Inditex (Zara), H&M, LVMH | Product sales + D2C | Comp sales, inventory turnover, sell-through rate | | E-Commerce | Amazon, Shopify merchants | Product sales + marketplace fees | GMV, conversion rate, AOV, repeat rate | | CPG (Consumer Packaged Goods) | P&G, Unilever, Nestle | Product sales through retail | Market share, distribution penetration, price realization | | Restaurants / QSR | McDonald's, Starbucks, Chipotle | Food sales + franchise fees | Same-store sales, AUV, traffic vs. ticket | | D2C Brands | Warby Parker, Allbirds, Glossier | Direct product sales | CAC, LTV, repeat rate, contribution margin | | Specialty Retail | Home Depot, Best Buy, Ulta | Product sales + services | Comp sales, sales per sq ft, inventory turns |

Key Metrics

Store-Level Metrics

• Same-store sales (comps): YoY revenue growth in stores open 12+ months. Decomposes into traffic × ticket.
• Sales per square foot: Revenue / total selling sq ft. Best-in-class (Apple): >$5,000. Good specialty: $300-600. Grocery: $500-800.
• Foot traffic: Customer visits per period. Leading indicator of sales.
• Conversion rate: Transactions / customer visits. In-store: 20-40%. Online: 2-4%.
• Average transaction value (ATV): Revenue / number of transactions.
• Units per transaction (UPT): Items sold / transactions.
• Four-wall EBITDA margin: Store-level profitability before corporate overhead. Healthy: 15-25%.

Brand & Marketing Metrics

• Brand awareness: Aided and unaided awareness (% of target population)
• Net Promoter Score (NPS): Customer loyalty indicator
• Share of shelf / Share of search: Physical or digital visibility vs. competitors
• Customer acquisition cost (CAC): Total marketing spend / new customers
• Return on ad spend (ROAS): Revenue attributed to ads / ad spend. Target: 3-5×.
• Social media engagement: Followers, engagement rate, share of voice

E-Commerce / D2C Metrics

• Gross Merchandise Value (GMV): Total value of transactions
• Conversion rate: Orders / sessions. Benchmark: 2-4% (general), 5-8% (strong brands)
• Average Order Value (AOV): Revenue / orders
• Cart abandonment rate: Abandoned carts / total carts. Typical: 65-75%.
• Customer lifetime value (LTV): Revenue per customer over their lifecycle
• Repeat purchase rate: % of customers who buy again within 12 months. Strong D2C: >40%.
• LTV:CAC ratio: Target 3-5×
• Contribution margin after marketing: Revenue - COGS - shipping - marketing per order

CPG / Brand Metrics

• Market share: Brand volume or value / total category. Track trend vs. competitors.
• Distribution (ACV%): % of All Commodity Volume — measures how widely the product is available in stores
• Velocity: Units sold per point of distribution per week
• Price realization: ASP growth vs. volume growth
• Trade spend efficiency: Promotional lift / trade spend invested
• Household penetration: % of target households that purchased in last 12 months

Valuation Benchmarks

| Sub-Sector | EV/Revenue | EV/EBITDA | P/E | Key Driver | |-----------|-----------|-----------|-----|-----------| | Grocery | 0.3-0.6× | 6-10× | 12-18× | Comp sales, market density | | Specialty retail | 0.5-2.0× | 8-14× | 15-25× | Comp sales, margin trend | | Apparel (branded) | 1-3× | 10-18× | 18-30× | Brand strength, D2C mix | | CPG | 2-4× | 12-18× | 20-28× | Organic growth, category leadership | | Restaurants (franchisor) | 5-10× | 15-25× | 25-35× | Unit growth, same-store sales | | E-commerce | 1-4× | 15-30× | 25-50× | GMV growth, take rate, profitability path | | D2C brands | 1-3× | 15-30× | N/A (often unprofitable) | Revenue growth, unit economics | | Luxury | 3-6× | 15-25× | 25-40× | Brand exclusivity, pricing power |

Common Frameworks — Consumer/Retail Adaptations

Porter's Five Forces — Retail

• Buyer power: HIGH. Consumers have near-perfect price transparency (online comparison), low switching costs.
• Supplier power: Low for retailers (they aggregate demand). High for luxury brands (they ARE the supplier).
• New entrants: Low barriers in e-commerce. High barriers in physical retail (real estate, capital). D2C brands can launch with minimal capital.
• Substitution: Online substituting physical. Private label substituting brands. Rental/resale substituting new purchase.
• Rivalry: INTENSE. Retail is one of the most competitive industries. Price competition, promotional intensity, format competition (warehouse, convenience, online).

Market Sizing — Consumer

• Top-down: Total consumer spending in category × geographic share × format share
• Bottom-up: Target population × penetration rate × purchase frequency × average spend per occasion
• Store-level: Number of potential store locations × average revenue per store

Industry Trends

• Omnichannel convergence: Physical + digital integration (BOPIS, ship-from-store, social commerce)
• Private label growth: Retailer brands gaining share vs. national brands (Costco Kirkland, Amazon Basics)
• Sustainability pressure: Consumer demand for sustainable products, packaging, supply chain
• Personalization: AI-driven product recommendations, dynamic pricing, targeted marketing
• Social commerce: Shopping directly through TikTok, Instagram, Pinterest
• Quick commerce: Ultra-fast delivery (15-30 minutes) for convenience items

Industry-Specific Risks

• Consumer sentiment/recession sensitivity (discretionary spend drops first)
• Inventory management (markdown risk, obsolescence in fashion)
• Supply chain disruption (global sourcing, port congestion, freight costs)
• Channel conflict (D2C vs. wholesale relationships)
• Amazon/Walmart competitive pressure (price and convenience benchmarks)
• Labor cost inflation and availability (retail and warehouse labor markets)

Industry Overlay: Financial Services

Apply these industry-specific adaptations when analyzing financial services companies (banks, insurance, asset management, fintech, payments).

Industry Structure

Key Sub-Sectors

| Sub-Sector | Key Players | Revenue Model | Key Metrics | |-----------|------------|---------------|-------------| | Commercial/Retail Banking | JPM, BofA, Wells Fargo, regionals | Net interest income + fee income | NIM, loan growth, deposit growth, efficiency ratio | | Investment Banking | Goldman, Morgan Stanley, Lazard | Advisory fees, underwriting | Deal volume, league table ranking, wallet share | | Insurance (P&C) | Berkshire, AIG, Chubb | Premiums - claims | Combined ratio, loss ratio, premium growth | | Insurance (Life) | MetLife, Prudential, Lincoln | Premiums + investment income | New business value, persistency, embedded value | | Asset Management | BlackRock, Vanguard, Fidelity | AUM × management fees | AUM, net flows, fee rate, investment performance | | Payments | Visa, Mastercard, PayPal | Transaction fees (take rate) | TPV, transactions, take rate, active accounts | | Fintech (Lending) | SoFi, LendingClub, Affirm | Interest income + origination fees | Origination volume, delinquency rate, unit economics | | Fintech (Neobanks) | Chime, Revolut, Nubank | Interchange + premium subscriptions | Active users, ARPU, engagement, deposits |

Key Metrics by Sub-Sector

Banking Metrics

• Net Interest Margin (NIM): (Interest income - Interest expense) / Average earning assets. Typical: 2.5-3.5%
• Efficiency Ratio: Non-interest expense / Total revenue. Lower is better. Best-in-class: <55%. Median: 60-65%.
• Loan-to-Deposit Ratio: Total loans / Total deposits. 80-90% is typical.
• Non-Performing Loans (NPL) Ratio: NPLs / Total loans. <1% is good. >3% is concerning.
• Return on Assets (ROA): Net income / Total assets. Target: >1.0%
• Return on Equity (ROE): Net income / Shareholder equity. Target: >10-12%
• CET1 Capital Ratio: Common equity tier 1 / Risk-weighted assets. Regulatory minimum ~4.5%, well-capitalized >10%
• Cost of Deposits: Interest paid / Average deposits. Critical driver of NIM.

Insurance Metrics

• Combined Ratio: (Claims + Expenses) / Premiums. Below 100% = underwriting profit. Above 100% = underwriting loss.
• Loss Ratio: Claims paid / Premiums earned. Typical P&C: 60-70%.
• Expense Ratio: Underwriting expenses / Premiums. Typical: 25-35%.
• Investment Yield: Investment income / Average invested assets.
• Premium Growth: Written premium YoY growth rate.
• Reserving Adequacy: Prior year reserve development (favorable = conservative reserving).

Asset Management Metrics

• AUM (Assets Under Management): Total assets managed. Key scale metric.
• Net Flows: New money in - redemptions. Positive = growing. Negative = losing share.
• Fee Rate (bps): Management fee as basis points of AUM. Active equity: 50-100 bps. Passive: 3-10 bps. Alternatives: 100-200 bps + carry.
• Investment Performance: % of funds beating benchmark over 1, 3, 5 years.
• Revenue = AUM × Fee Rate. Growth comes from: market appreciation, net flows, or fee increases.

Payments Metrics

• Total Payment Volume (TPV): Total dollar value processed through the network.
• Transactions: Number of payment transactions processed.
• Take Rate: Revenue / TPV. Visa/MC: ~0.15-0.20%. Stripe: ~2.5-3%. PayPal: ~2-2.5%.
• Active Accounts: Users/merchants actively transacting.
• Revenue per Transaction: Revenue / number of transactions.

Regulatory Landscape

Key Regulations

• Basel III/IV: Capital adequacy, leverage, liquidity requirements for banks
• Dodd-Frank: Systemic risk oversight, Volcker Rule, consumer protection (US)
• GDPR/CCPA: Data protection requirements for customer financial data
• PSD2/Open Banking: Mandated API access to bank accounts (EU, expanding globally)
• AML/KYC: Anti-money laundering and know-your-customer requirements
• SOX: Financial reporting and internal controls (public companies)
• State Insurance Regulation: Insurance regulated at state level in US (NAIC coordination)

Regulatory Risks

• Interest rate environment (rising rates help bank NIM, hurt bond portfolios)
• Capital requirement increases (reduce ROE, constrain growth)
• Fintech regulation (pending — could increase or decrease competitive pressure)
• Open banking mandates (could commoditize banking relationships)
• Crypto regulation (uncertain regulatory treatment)
• Climate risk disclosure requirements (emerging)

Valuation Benchmarks

| Sub-Sector | Primary Multiple | Typical Range | Key Driver | |-----------|-----------------|--------------|-----------| | Banks | P/E, P/TBV | 8-14× P/E, 1.0-2.0× P/TBV | ROE, asset quality, growth | | Insurance (P&C) | P/E, P/BV | 10-15× P/E, 1.5-2.5× P/BV | Combined ratio, reserve strength | | Insurance (Life) | P/Embedded Value | 0.8-1.5× EV | New business margins, persistency | | Asset Management | P/E, % of AUM | 10-15× P/E, 2-4% of AUM | Fee rate sustainability, flows | | Payments | EV/Revenue, P/E | 8-15× revenue, 25-40× P/E | TPV growth, take rate durability | | Fintech (growth) | EV/Revenue | 5-15× revenue | Revenue growth, path to profit |

Note: Banks and insurance are valued on P/BV (book value) or P/E because EV/EBITDA is not meaningful (debt is operational, not financial).

Common Frameworks — Financial Services Adaptations

Porter's Five Forces — Financial Services

• Buyer power: Retail customers have moderate power (switching is possible but friction is high). Institutional clients have high power.
• Supplier power: Low in most segments. Capital markets are the "supplier" of funds.
• New entrants: Fintech creates real disruption in payments, lending, and wealth. Regulatory moats protect incumbents in banking and insurance.
• Substitution: BNPL substituting credit cards. Robo-advisors substituting financial advisors. Crypto substituting (partially) traditional payments.
• Rivalry: Intense in commoditized products (mortgages, auto loans, term insurance). Lower in specialized niches.

Market Sizing — Financial Services

• Banking: Total deposits in market × NIM, or Total loans × net interest spread
• Insurance: Total premiums written in market
• Payments: Total consumer spend × digital payment penetration × take rate
• Wealth management: Total investable assets × average fee rate

Industry-Specific Risks

• Credit cycle risk (loan losses spike in recessions)
• Interest rate risk (duration mismatch between assets and liabilities)
• Regulatory change (capital requirements, licensing, consumer protection)
• Technology disruption (fintech unbundling traditional bank services)
• Cybersecurity (financial data is high-value target)
• Operational risk (systems failures, fraud, compliance failures)

Industry Overlay: Healthcare

Apply these industry-specific adaptations when analyzing healthcare companies (providers, payers, pharma, medtech, healthtech).

Industry Structure

Key Sub-Sectors

| Sub-Sector | Key Players | Revenue Model | Key Metrics | |-----------|------------|---------------|-------------| | Hospitals / Health Systems | HCA, CommonSpirit, Ascension | Fee-for-service, value-based contracts | Patient volume, case mix index, average length of stay, readmission rate | | Health Insurance (Payers) | UnitedHealth, Anthem, Cigna, Aetna | Premiums - medical costs | Medical loss ratio (MLR), membership, premium growth | | Pharma / Biotech | Pfizer, J&J, Roche, AbbVie | Drug sales, licensing | Pipeline stage, FDA approval probability, patent cliff timing | | Medical Devices | Medtronic, Abbott, Stryker | Device sales + service | Procedure volume, ASP trends, installed base | | Healthtech / Digital Health | Epic, Veeva, Teladoc | SaaS, platform fees | EMR adoption, patient engagement, utilization | | CROs / CDMOs | IQVIA, Labcorp, Catalent | Service fees, project-based | Backlog, book-to-bill ratio, capacity utilization |

Value Chain

Pharma R&D → Manufacturing → Distribution → Payers (insurance) → Providers (hospitals/clinics) → Patients

Key Metrics by Sub-Sector

Hospital / Provider Metrics

• Patient volume: Admissions, outpatient visits, ED visits, surgical cases
• Case mix index (CMI): Acuity of patients (higher = more complex, higher reimbursement)
• Average length of stay (ALOS): Days per admission (lower is generally better)
• Bed occupancy rate: Occupied beds / total beds (target: 75-85%)
• Revenue per adjusted admission: Total revenue / adjusted patient days
• Readmission rate: 30-day readmission rate (CMS tracks this; penalties for high rates)
• Payer mix: % Medicare, % Medicaid, % Commercial, % Self-pay (commercial pays best)

Payer Metrics

• Medical Loss Ratio (MLR): Medical costs / Premium revenue. ACA requires 80-85% minimum.
• Membership: Total lives covered, growth rate, retention rate
• Premium PMPM: Premium per member per month
• Total cost of care (TCOC): All-in medical cost per member
• Star ratings: CMS quality ratings (1-5 stars) — affect MA bonus payments

Pharma / Biotech Metrics

• Pipeline by stage: Preclinical, Phase I, II, III, NDA/BLA, Approved
• Probability of success by stage: Phase I (10%), Phase II (30%), Phase III (60%), NDA (85%)
• Patent cliff exposure: Revenue at risk from patent expirations in next 5 years
• R&D ROI: NPV of pipeline / R&D investment
• Revenue concentration: Top drug as % of total revenue (>30% = high risk)

Regulatory Landscape

Key Regulations

• FDA approval process: IND → Phase I-III → NDA/BLA → Approval (8-12 years, $1-2B average cost)
• CMS reimbursement: Medicare/Medicaid payment rates set by CMS, updated annually
• HIPAA: Data privacy and security requirements for protected health information (PHI)
• ACA (Affordable Care Act): MLR requirements, essential health benefits, exchange marketplaces
• 340B Drug Pricing: Discounted drug purchasing for qualifying healthcare organizations
• Stark Law / Anti-Kickback: Prohibitions on physician self-referrals and improper financial relationships

Regulatory Risks to Flag

• Drug pricing reform (potential government negotiation or caps)
• Value-based reimbursement transition (fee-for-service → outcomes-based)
• Prior authorization reform (payer-provider friction point)
• AI/digital health regulation (FDA software as medical device framework)
• Data interoperability mandates (21st Century Cures Act)

Valuation Benchmarks

| Sub-Sector | EV/Revenue | EV/EBITDA | Key Driver | |-----------|-----------|-----------|-----------| | Hospitals | 1-2× | 8-12× | Payer mix, occupancy, market position | | Health insurance | 0.5-1.5× | 10-14× | Membership growth, MLR management | | Large pharma | 3-5× | 10-15× | Pipeline depth, patent portfolio | | Biotech (commercial) | 4-8× | 15-25× | Growth rate, pipeline optionality | | Biotech (pre-revenue) | N/A (use pipeline NPV) | N/A | Clinical data, probability-weighted NPV | | Medical devices | 3-6× | 15-22× | Growth rate, margin profile | | Healthtech / Digital health | 5-15× | 20-40× | ARR growth, retention, TAM |

Common Frameworks — Healthcare Adaptations

Porter's Five Forces — Healthcare

• Buyer power: Government payers (CMS) have high power. Commercial payers have moderate power. Patients have low power (limited price transparency).
• Supplier power: Pharma suppliers have high power (patented drugs). Labor (physicians, nurses) has very high power due to shortages.
• New entrants: High barriers (regulation, capital, credentials). But tech companies (Amazon, Google) are increasingly entering.
• Substitution: Telehealth substituting in-person visits. Generics/biosimilars substituting branded drugs. AI diagnostics substituting specialist consultations.
• Rivalry: Intense in payer market. Moderate in provider market (geographic quasi-monopolies).

PESTEL — Healthcare

• Political: Government healthcare policy, drug pricing reform, ACA stability
• Economic: Healthcare spending as % GDP (approaching 20% in US), employer health cost burden
• Social: Aging population (65+ growing fastest), chronic disease prevalence, consumer expectations for digital
• Technological: AI diagnostics, remote monitoring, gene therapy, electronic health records
• Environmental: Pharmaceutical pollution, hospital waste, supply chain sustainability
• Legal: Patent litigation, malpractice liability, data privacy enforcement

Industry-Specific Risks

• Reimbursement rate cuts (CMS rate changes can eliminate margins overnight)
• Drug patent cliffs (revenue replacement challenge)
• Clinical trial failures (binary outcomes for pipeline-stage biotech)
• Workforce shortages (nursing, physician) driving labor cost inflation
• Cybersecurity (healthcare is the #1 targeted industry for ransomware)
• Value-based care transition execution risk

Industry Overlay: Industrial / Manufacturing

Apply these industry-specific adaptations when analyzing manufacturing, industrial, and B2B companies.

Industry Structure

Key Sub-Sectors

| Sub-Sector | Key Players | Revenue Model | Key Metrics | |-----------|------------|---------------|-------------| | Diversified Industrials | GE, Honeywell, 3M, Siemens | Equipment sales + aftermarket/service | Organic growth, book-to-bill, backlog | | Aerospace & Defense | Boeing, Lockheed, RTX | Platforms + MRO + defense contracts | Backlog, deliveries, aftermarket mix | | Automotive | Toyota, VW, GM, Tesla | Vehicle sales + parts + financing | Units sold, ASP, incentive spend, EV mix | | Chemicals | BASF, Dow, DuPont | Product sales (commodity + specialty) | Volumes, pricing, capacity utilization | | Building Materials | CRH, Vulcan, Martin Marietta | Product sales (aggregates, cement, etc.) | Shipments, ASP, geographic pricing power | | Capital Equipment | Caterpillar, Deere, KION | Equipment sales + parts + service | Orders, backlog, dealer inventory, utilization | | Contract Manufacturing | Foxconn, Jabil, Flex | Manufacturing services | Revenue per program, capacity utilization, yield |

Key Metrics

Operational Metrics

• Capacity utilization: Actual output / max capacity. Target: 80-90%. Below 75% = excess capacity pressure. Above 90% = need expansion.
• Overall Equipment Effectiveness (OEE): Availability × Performance × Quality. World-class: >85%. Average: 60%.
• Yield rate / First pass yield: Good units / total units produced. Target: >95%.
• Scrap rate: Scrapped materials / total materials. Target: <2%.
• Cycle time: Time to produce one unit from start to finish.
• Changeover time: Time to switch production from one product to another (SMED methodology targets <10 minutes).
• Inventory turns: COGS / average inventory. Higher = more efficient. Benchmark varies by industry (automotive: 8-12, chemicals: 4-8, equipment: 3-6).

Commercial Metrics

• Book-to-bill ratio: New orders / revenue. Above 1.0 = growing backlog. Below 1.0 = shrinking.
• Backlog: Unfulfilled orders in dollars. Provides revenue visibility.
• Organic growth: Revenue growth excluding M&A, FX, and divestitures.
• Aftermarket / service mix: Recurring service and parts revenue as % of total. Higher = more stable. Target: 30-50%.
• Price realization: Price increases achieved vs. cost inflation.
• Win rate on RFPs/bids: Proposals won / total proposals submitted.

Supply Chain Metrics

• Supplier on-time delivery: % of supplier shipments arriving on time. Target: >95%.
• Supplier defect rate: Defective incoming materials as % of total. Target: <1%.
• Procurement savings: Year-over-year savings from sourcing initiatives.
• Raw material cost as % of revenue: Track trend and compare to peers.
• Supply chain lead time: Order to delivery time for key materials.

Safety & Sustainability

• Total Recordable Incident Rate (TRIR): Recordable incidents × 200,000 / total hours worked. Good: <1.0.
• Lost Time Injury Rate (LTIR): Lost time injuries × 200,000 / total hours worked.
• Carbon intensity: CO2 emissions per unit of production or per $M revenue.
• Energy intensity: Energy consumed per unit of production.
• Water usage intensity: Water consumed per unit of production.

Valuation Benchmarks

| Sub-Sector | EV/Revenue | EV/EBITDA | P/E | Key Driver | |-----------|-----------|-----------|-----|-----------| | Diversified industrials | 2-4× | 10-15× | 18-25× | Organic growth, margin expansion, aftermarket mix | | Aerospace & Defense | 2-4× | 12-18× | 18-25× | Backlog visibility, defense budget, aftermarket | | Automotive OEMs | 0.3-1.0× | 4-8× | 6-12× | Volume, EV transition, margin recovery | | Auto suppliers | 0.5-1.5× | 5-9× | 8-14× | Content per vehicle growth, customer diversification | | Chemicals (specialty) | 2-4× | 10-15× | 18-25× | Pricing power, innovation pipeline | | Chemicals (commodity) | 0.5-1.5× | 5-8× | 8-14× | Capacity position, cost curve placement | | Capital equipment | 1.5-3× | 10-14× | 15-22× | Cycle position, aftermarket, technology | | Building materials | 3-6× | 12-18× | 20-30× | Infrastructure spend, geographic monopolies |

Common Frameworks — Industrial Adaptations

Porter's Five Forces — Industrial

• Buyer power: Moderate to high. Large OEMs and industrial buyers negotiate aggressively. Long-term contracts common.
• Supplier power: Varies. Commodity raw materials = low. Specialized components = high. Single-source parts = very high.
• New entrants: High barriers (capital, engineering expertise, certifications, customer relationships, economies of scale).
• Substitution: Material substitution (steel → aluminum → composites), process substitution (traditional → additive manufacturing).
• Rivalry: Moderate in specialties. Intense in commodities. Cyclicality amplifies competition during downturns.

Market Sizing — Industrial

• Installed base approach: Number of machines/systems installed × replacement rate × average price + aftermarket spend
• Capacity-based: Industry capacity × utilization rate × price per unit
• Capex-driven: End-market capex budgets × share going to this product category

Competitive Analysis — Industrial Specifics

Track these additional signals:

• Backlog growth vs. competitors (proxy for market share momentum)
• Capacity additions announced (who's investing in growth?)
• Patent filings in key technology areas
• Apprenticeship/training programs (signals long-term workforce investment)
• Government contract wins (defense, infrastructure)

Industry Cycles

• Capex cycle: Industrial demand is tied to end-market capex. Track PMI, industrial production, housing starts, defense budgets.
• Inventory cycle: Destocking/restocking amplifies demand swings. Track channel inventory levels.
• Commodity cycle: Raw material prices (steel, oil, copper, lithium) directly impact costs and sometimes demand.

Cycle Indicators to Monitor

| Indicator | Source | What It Signals | |-----------|--------|----------------| | ISM Manufacturing PMI | ISM | >50 = expansion, <50 = contraction | | Industrial production index | Federal Reserve | Output volume trend | | Durable goods orders | Census Bureau | Forward demand | | Housing starts | Census Bureau | Construction demand | | Auto production schedules | IHS Markit, OEM guidance | Automotive demand | | Oil/gas rig count | Baker Hughes | Energy sector activity |

Industry-Specific Risks

• Cyclicality (revenue can swing 20-30% in downturns)
• Raw material cost volatility (input cost spikes compress margins)
• Supply chain disruption (single-source components, geopolitical risks)
• Energy transition risk (stranded assets in fossil fuel-related manufacturing)
• Skilled labor shortage (aging workforce, insufficient apprenticeship pipeline)
• Tariff and trade policy risk (cross-border supply chains)
• Product liability and warranty risk
• Environmental compliance costs (emissions, waste disposal, remediation)

Industry Overlay: Technology / SaaS

Apply these industry-specific adaptations when analyzing technology or SaaS companies.

Key Metrics (Prioritized)

Growth Metrics

• ARR / MRR (most important revenue metric — not recognized revenue)
• Net Revenue Retention (NRR): World-class >130%, Good >110%, Concerning <100%
• Logo Retention: >90% for enterprise, >80% for SMB
• New ARR by Source: New logos vs. expansion vs. reactivation
• Pipeline Coverage: 3-4× quota minimum

Efficiency Metrics

• Magic Number: Net New ARR / Prior Quarter S&M Spend. >1.0 = efficient. <0.5 = broken GTM.
• Burn Multiple: Net Burn / Net New ARR. <1.5× = efficient. >2× = fix it.
• Rule of 40: Growth % + FCF Margin % > 40% for premium valuation
• CAC Payback: <12 months (SMB), <18 months (mid-market), <24 months (enterprise)
• LTV:CAC: 3-5× is healthy

Product Metrics

• DAU/MAU ratio: >20% = sticky product
• Feature adoption: % of users engaging with core features
• Time to value: Days from signup to first value realization
• Product-qualified leads (PQLs): For PLG companies

Valuation Benchmarks

| Growth Rate | High NRR (>120%) | Medium NRR (100-120%) | Low NRR (<100%) | |------------|------------------|----------------------|-----------------| | >50% | 20-35× ARR | 12-20× ARR | 8-15× ARR | | 30-50% | 12-20× ARR | 8-14× ARR | 5-10× ARR | | 15-30% | 7-12× ARR | 5-8× ARR | 3-6× ARR | | <15% | 4-7× ARR | 3-5× ARR | 2-4× ARR |

Premium drivers: >130% NRR, >80% gross margin, TAM >$10B, PLG motion, mission-critical product.

Common Frameworks — SaaS Adaptations

Porter's Five Forces — SaaS Considerations

• Buyer power: Lower when switching costs are high (data lock-in, workflow integration, user training). Higher in commoditized categories (e.g., email, basic CRM).
• Supplier power: Cloud providers (AWS, Azure, GCP) are key suppliers. Multi-cloud reduces dependency.
• New entrants: Low capital barriers but high distribution barriers. Open-source competitors are a specific threat.
• Substitution: Point solutions vs. platforms. AI-native tools disrupting established software.
• Rivalry: Feature parity is common — competition shifts to distribution, brand, ecosystem.

Competitive Analysis — SaaS Specifics

Track these additional signals:

• G2, Capterra, TrustRadius reviews and rankings
• GitHub stars and community activity (for developer tools)
• Integration ecosystem breadth (Zapier, native integrations)
• Pricing page changes (use Wayback Machine)
• Product changelog velocity (signals R&D output)

Market Sizing — SaaS Specifics

• Seat-based: Potential users × average seats per company × price per seat per year
• Usage-based: Potential transactions/API calls/events × price per unit
• Land and expand: Initial deal size × expansion multiple (typically 1.3-2.0× over 3 years)

Industry-Specific Risks

• Platform dependency: Risk of AWS/Salesforce/Shopify building competing features
• Open-source disruption: Free alternatives commoditizing paid categories
• AI disruption: AI-native tools replacing traditional software workflows
• Data security/privacy: SOC 2, GDPR, HIPAA compliance requirements
• Concentration risk: Large enterprise customers with outsized revenue contribution

Benchmarks by Stage

| Metric | Seed/A | Series B | Series C+ | Public | |--------|--------|----------|-----------|--------| | ARR | <$2M | $5-15M | $20-50M | $100M+ | | Growth rate | >100% | 60-100% | 40-60% | 20-40% | | Gross margin | 60-70% | 70-80% | 75-85% | 75-85% | | Burn multiple | <3× | <2× | <1.5× | N/A (profitable) | | NRR | >100% | >110% | >115% | >110% | | S&M as % rev | 80-120% | 50-80% | 40-60% | 30-50% | | R&D as % rev | 40-60% | 30-50% | 25-35% | 20-30% | | G&A as % rev | 20-30% | 15-25% | 10-18% | 8-15% |