# SCP Industry Analysis Model (Structure-Conduct-Performance)

> **Core Value**: Reveals how industrial structure determines firm behavior, and how firm behavior determines business performance
>
> **Creator**: Joe Bain & Edward Mason (Harvard); popularized by McKinsey
>
> **One-liner**: Structure determines conduct, conduct determines performance — understand the industry structure, and you can predict firm behavior

---

> **Case Boundary**: Company/industry cases in this file are illustrative examples for framework structure and reasoning, not current factual claims by Alpha Insights. Before reusing any numbers, shares, dates, or conclusions in a live study, re-check sources and record them in the Evidence Claim Ledger.

## Overview

The SCP (Structure-Conduct-Performance) model explains how industry competitive landscapes shape firm behavior and business outcomes through a "Structure → Conduct → Performance" causal chain. It is one of the most predictive frameworks in industry analysis.

**Core Design Principles**:
- **Causal Chain Thinking**: Industrial structure determines firm behavior, firm behavior determines business performance — firms don't "choose" strategies; they are "pushed toward" strategies by structure
- **Bidirectional Feedback**: Performance feeds back to influence structure (e.g., leading firms' high profits → increased M&A → rising concentration)
- **Predictive Power**: Understanding structural characteristics enables prediction of rational behavior patterns within the industry

**Best Use Cases**:
- Deep analysis phase of industry research (typically after PESTEL and Five Forces)
- Understanding "why do all firms in this industry behave this way"
- Predicting how firm behavior will shift following structural changes
- Evaluating industry landscape evolution trends for investment decisions

**Output Value**:
- Clear S→C→P causal deduction chain
- Feedback loop analysis revealing industry evolution direction
- Strategic implications and behavioral predictions based on structural characteristics

---

## Framework Overview

```
S (Structure)        C (Conduct)          P (Performance)
Industrial     ──→   Firm           ──→    Business
Structure            Behavior              Performance

┌──────────┐    ┌──────────┐    ┌──────────┐
│ Concentra-│    │ Pricing   │    │ Profit   │
│ tion      │    │ strategy  │    │ margins  │
│ Entry     │ →  │ Product   │ →  │ Market   │
│ barriers  │    │ strategy  │    │ share    │
│ Product   │    │ Marketing │    │ Innova-  │
│ differenti│    │ spend     │    │ tion     │
│ ation     │    │ R&D spend │    │ Social   │
│ Supply/   │    │ M&A       │    │ effici-  │
│ demand    │    │           │    │ ency     │
│ Info      │    │           │    │          │
│ symmetry  │    │           │    │          │
└──────────┘    └──────────┘    └──────────┘
       ↑                               │
       └───────── Feedback Loop ───────┘
```

**Difference from Porter's Five Forces**:
- Five Forces answers "is the industry profitable" (industry attractiveness)
- SCP answers "why is the industry this way" and "why do firms behave this way" (causal chain)
- Five Forces is a derivative tool of SCP; SCP is more foundational and complete

---

## Three-Layer Deep Dive

### S — Structure (Industrial Structure)

| Structural Element | Analysis Content | Core Indicators |
|-------------------|-----------------|-----------------|
| **Market concentration** | How many firms dominate the industry | CR3, CR5, CR10, HHI index |
| **Entry barriers** | Difficulty for new firms to enter | Capital requirements, technology thresholds, licenses, economies of scale |
| **Exit barriers** | Difficulty for firms to exit | Sunk costs, switching costs, emotional barriers |
| **Product differentiation** | Substitutability between products | Brand premium, functional differences, user lock-in |
| **Supply/demand structure** | Power balance between buyers and sellers | Buyer/seller concentration, information symmetry |
| **Vertical integration** | Degree of value chain integration | In-house vs. outsourced ratio |

**Concentration Level Reference**:

| HHI Index | CR3 | Market Type | Typical Industries |
|-----------|-----|------------|-------------------|
| >2500 | >70% | Highly concentrated (oligopoly) | Mobile chipsets, search engines |
| 1500-2500 | 40-70% | Moderately concentrated | Food delivery, ride-hailing |
| <1500 | <40% | Fragmented competition | Restaurants, local services |

### C — Conduct (Firm Behavior)

| Behavior Dimension | Analysis Questions |
|-------------------|-------------------|
| **Pricing behavior** | Price war or value competition? What is the pricing strategy? |
| **Product strategy** | Homogenization or differentiation? What is the innovation direction? |
| **Marketing spend** | Customer acquisition cost trends? Marketing expense ratio? |
| **R&D spend** | R&D expense ratio? Technology barrier investment direction? |
| **Expansion approach** | Organic growth or M&A? Vertical integration or horizontal expansion? |
| **Competitive strategy** | Cost leadership/differentiation/focus? Blue ocean or red ocean? |
| **Cooperation/alliances** | Ecosystem alliances, strategic investments, exclusive agreements? |

**Key Analytical Logic**: Firm behavior is not random — it is "forced out" by industrial structure.
- High concentration → Oligopolistic games, tacit pricing
- Low differentiation → Price wars
- High entry barriers → High profits for incumbents
- Information asymmetry → Brand premiums

### P — Performance (Business Performance)

| Performance Dimension | Analysis Indicators |
|----------------------|---------------------|
| **Profitability** | Gross margin, net margin, ROE, ROIC |
| **Market share** | Share change trends, relative share |
| **Growth** | Revenue growth, user growth |
| **Efficiency** | Revenue per employee, revenue per sqft, asset turnover |
| **Innovation output** | Number of new products/services, patent count |

---

## Execution Steps

### Step 1: Analyze Structure (Industrial Structure Scan)

Collect the following data:
1. Industry CR3/CR5/CR10 and trends over the past 3 years
2. Types and height of entry/exit barriers
3. Degree of product differentiation
4. Buyer/seller concentration

### Step 2: Analyze Conduct (Firm Behavior Observation)

Observe behavior patterns of the top 3-5 firms:
1. Pricing strategy (price bands, discount intensity, subsidy behavior)
2. Product/technology investment direction
3. Expansion strategy (build vs. acquire vs. alliance)
4. Marketing strategy and customer acquisition methods

### Step 3: Analyze Performance (Performance Verification)

Collect operating data from leading firms to verify whether the "Structure → Conduct → Performance" causal chain holds.

### Step 4: S→C→P Causal Chain Deduction

**Core analytical action**: Identify causal relationships

```
[Structural Feature] → leads to → [Behavior Pattern] → leads to → [Performance Outcome]

Example:
Low industry concentration (CR5<30%) → leads to → intense price wars → leads to → industry-wide gross margin <15%
```

### Step 5: Feedback Loop Analysis

How performance feeds back to influence structure (this is the advanced application of SCP):

```
Example:
Leading firms' high profits → increased M&A investment → rising industry concentration → easing price competition
```

### Step 6: Derive Strategic Implications

Based on SCP analysis, answer:
1. Under the current structure, what behavior is most rational?
2. In which direction is the structure evolving?
3. Is it possible to actively change the structure (e.g., through M&A, technological disruption)?

---

## Output Format

```markdown
## SCP Analysis: [Industry Name]

### Structure (Industrial Structure)
| Structural Element | Current State | Trend |
|-------------------|--------------|-------|
| Concentration | CR5=[XX%], [type] | [trend] |
| Entry barriers | [High/Medium/Low], [barrier types] | [changes] |
| Product differentiation | [High/Medium/Low] | [trend] |
| Supply/demand structure | [description] | [changes] |

### Conduct (Firm Behavior)
| Firm | Pricing Strategy | Product Strategy | Expansion Strategy |
|------|-----------------|-----------------|-------------------|
| [Firm A] | [description] | [description] | [description] |
| [Firm B] | [description] | [description] | [description] |

### Performance (Business Performance)
| Firm | Revenue Growth | Gross Margin | Market Share |
|------|---------------|-------------|-------------|
| [Firm A] | [XX%] | [XX%] | [XX%] |
| [Firm B] | [XX%] | [XX%] | [XX%] |

### S→C→P Causal Chain
1. [Structural feature] → [Behavior pattern] → [Performance outcome]
2. [Structural feature] → [Behavior pattern] → [Performance outcome]

### Feedback Loop
[Performance] → [How it changes structure] → [Impact on industry future]

### Strategic Implications
1. [Implication 1]
2. [Implication 2]
```

---

## Best Practices

### Full Industry SCP Cases

#### Case 1: China Food Delivery Industry (2024)

**S — Structure**

| Structural Element | Current State | Data Source |
|-------------------|--------------|-----------|
| Concentration | CR2 ≈ 95% (Meituan ~67%, Ele.me ~28%), HHI >4500, typical duopoly | QuestMobile / brokerage research |
| Entry barriers | Extremely high — requires simultaneously building merchant network, rider capacity, and user traffic; cold-start cost >1B RMB | Industry estimates |
| Exit barriers | High — rider systems, ground sales teams, merchant relationships are all sunk assets | Company financials |
| Product differentiation | Low — the same merchants are on both platforms simultaneously; user switching cost is just one app download | Merchant surveys |
| Supply/demand structure | Buyers (users) fragmented, sellers (platforms) highly concentrated; merchants have weak bargaining power, riders even weaker | Industry observation |

**C — Conduct**

| Behavior Dimension | Meituan | Ele.me |
|-------------------|---------|--------|
| Pricing strategy | Commission rate 20-26%, reduced subsidies for high-frequency users, shifting toward profitability | Commission rate 18-23%, using subsidies for share but reducing intensity |
| Expansion strategy | Horizontal expansion (Meituan Grocery, Meituan Select), vertical integration (self-built delivery) | Leveraging Alibaba ecosystem (Amap, Taobao entry points), focusing on local services |
| Marketing spend | Marketing expense ratio ~15%, customer lock-in through membership system (cross-category membership) | Marketing expense ratio ~18%, traffic from Alibaba 88VIP |
| Competitive strategy | Cost leadership + ecosystem expansion: using scale efficiency to drive down per-order costs | Differentiation focus: quality merchants, brand partnerships, instant retail |

**P — Performance**

| Metric | Meituan Delivery | Ele.me |
|--------|-----------------|--------|
| Daily orders | ~55M orders | ~22M orders |
| Delivery gross margin | ~28% (2023 financials) | Not separately disclosed, estimated ~15% |
| Per-order rider cost | ~5.5 RMB (scale effects) | ~6.2 RMB |

**S→C→P Causal Chain**:
1. **Duopoly + low differentiation** → Subsidy competition was the dominant theme, but both have shifted from cash-burning to efficiency competition → Meituan's scale advantage leads to significantly higher profit margins
2. **Extremely high entry barriers** → Incumbents need not worry about new entrants; competitive focus shifts to ecosystem expansion → Meituan leverages delivery infrastructure to enter community group buying, grocery, and other new businesses
3. **Weak merchant/rider bargaining power** → Platforms continue raising commission rates → Profit margins improving year over year (Meituan delivery went from losses to 28% gross margin)

**Feedback Loop**: Meituan high profits → investment in instant retail/community group buying → rider capacity reuse → delivery costs further decrease → profits continue to rise → competitive moat deepens. Ele.me is in a disadvantaged position in this positive feedback loop.

**So What**: The food delivery industry structure is highly locked in, and new entrants have virtually no chance of competing head-on. However, **instant retail** (delivering everything, not just meals) is reshaping the Structure — product differentiation is higher, merchant exclusivity is stronger, which may create structural windows for vertical players (e.g., Dingdong Maicai, Pupu Supermarket).

---

#### Case 2: China New Energy Vehicle Industry (2024)

**S — Structure**

| Structural Element | Current State | Data Source |
|-------------------|--------------|-----------|
| Concentration | CR5 ≈ 65% (BYD ~35%, Tesla ~8%, Geely ~8%, Changan ~7%, SAIC ~7%), moderately concentrated and rapidly increasing | CPCA |
| Entry barriers | Medium-high — capital threshold >20B RMB, but technology barriers lowered due to mature supply chain (compared to 2018) | Industry estimates |
| Exit barriers | High — factory/production line sunk costs are large; local governments reluctant to see local automakers fail | Public reports |
| Product differentiation | Medium — 100-200K RMB segment is highly homogenized (range/intelligent driving converging); >300K RMB still has differentiation space | Product comparisons |
| Vertical integration | BYD highly vertically integrated (battery + chips + motors); most others outsource | Company financials |

**C — Conduct**

| Behavior Dimension | BYD | Tesla China | NIO/XPeng/Li Auto |
|-------------------|-----|------------|-------------------|
| Pricing strategy | Aggressive price cuts (Qin PLUS starting at 79,800 RMB), leveraging cost advantage to initiate price wars | Following price cuts but with smaller reductions, maintaining gross margin floor ~18% | Li Auto insists on no price cuts; NIO uses battery swap model for lock-in; XPeng forced to follow |
| Product strategy | Full price-range coverage (79.8K-1.098M RMB), vehicle lineup flooding (10+ new models/year) | Lean product line (4 models), focusing on FSD intelligent driving | Each differentiated: Li Auto = family SUV, NIO = service ecosystem, XPeng = intelligent driving |
| R&D spend | R&D expense ratio ~5%, focusing on manufacturing processes and cost control | R&D expense ratio ~4% (global), focusing on AI and autonomous driving | R&D expense ratio 15-25%, focusing on intelligence but under profitability pressure |

**P — Performance**

| Metric | BYD | Tesla China | Li Auto | NIO | XPeng |
|--------|-----|------------|---------|-----|-------|
| 2024 sales (10K units) | ~425 | ~66 | ~50 | ~22 | ~19 |
| Gross margin | ~22% | ~18% | ~21% | ~10% | ~14% |
| Net margin | ~5% | ~8% | ~8% | Loss | Loss |

**S→C→P Causal Chain**:
1. **Moderate concentration + 100-200K RMB homogenization** → BYD uses vertical integration cost advantage to launch price wars → SME brands' margins compressed or in losses, accelerating industry shakeout
2. **High exit barriers** → Loss-making firms don't exit, continue cutting prices to clear inventory → Industry-wide prices under sustained pressure, "involution" becomes the norm
3. **High-end still has differentiation space** → Li Auto focused on family SUVs to find a structural niche → Achieved profitability alone (net margin ~8%), validating the viability of differentiation within the current structure

**Feedback Loop**: BYD high sales volume → scale effects lower costs → further price cuts → competitors' margins compressed/exit → concentration rises → BYD's share continues growing. This is a classic "scale-cost-price" positive feedback flywheel.

**So What**:
- **New entrants**: Head-on entry into the 100-200K RMB segment is suicidal competition; must find structural differentiation (like the Li Auto model) or entirely new categories (like Xiaomi's cross-industry ecosystem play)
- **Incumbents**: BYD's flywheel effect is extremely difficult to break; should focus on value zones BYD cannot cover (extreme intelligent driving, luxury experience, overseas markets)
- **Investment perspective**: The industry is transitioning from growth to maturity consolidation; investment logic shifts from "bet on the track" to "pick winners," focusing on cost structure and profitability rather than pure sales volume growth

---

### Data Source Recommendations

| SCP Layer | Recommended Data Sources |
|-----------|------------------------|
| S Structure | Brokerage industry research reports (CR3/CR5 data), business registration data (firm counts), national statistics bureau, industry association annual reports |
| C Conduct | Company financials (expense structures), product price tracking, news/announcements (M&A/investments), e-commerce platforms (pricing strategies) |
| P Performance | Public company financials, Wind/Choice financial terminals, industry average profit margin data, brokerage analysis |

### China Market Specifics

| Dimension | China Characteristics | Case |
|-----------|----------------------|------|
| S Structure | Dual-track SOE vs. private enterprise structure; impact of local protectionism on market segmentation; "winner-take-all" characteristics of platform economies | Telecom industry's three-SOE oligopoly; internet platform (Alibaba/Tencent) winner-take-all landscape |
| C Conduct | Subsidy wars (common in internet industries); policy-driven behavioral changes (e.g., platform behavior shifts after antitrust enforcement); industrial policy guiding investment direction | Didi-Kuaidi subsidy wars; Alibaba ending "exclusive dealing" after antitrust enforcement |
| P Performance | Large data availability gap between listed and unlisted companies; attention to the impact of policy subsidies on profits (true profitability) | NEV companies' true profitability varies dramatically after deducting subsidies (BYD vs. NIO) |

---

## Common Mistakes

| Mistake | Correct Approach |
|---------|-----------------|
| Only doing S without C and P | All three layers must be complete; the causal chain is the core value |
| Treating SCP as a Five Forces clone | SCP emphasizes causal deduction; Five Forces emphasizes competitive intensity assessment |
| Ignoring feedback loops | Feedback loops reveal the industry's future evolution direction |
| Listing data without causal deduction | Must explicitly state "because of XX structure, firms behave XX way" |

---

## Integration with Other Frameworks

| Partner Framework | Relationship |
|-------------------|-------------|
| Five Forces | Five Forces is a simplified tool for SCP's Structure layer |
| PESTEL | Macro factors (P, L) influence industrial structure |
| Industry Lifecycle | Different lifecycle stages have different SCP characteristics |
| Competitive Positioning Map | Visual presentation of Conduct analysis |
| BCG Matrix | Portfolio perspective on Performance analysis |
