Technology Industry Deep Dive: Complete Framework for Case Interviews

Technology cases appear in roughly 12% of MBB consulting interviews and are increasing in frequency as tech permeates every industry. Unlike traditional industries with stable business models, tech cases often involve rapid growth, winner-take-all dynamics, and business models that monetize users long after acquisition. This guide provides the complete framework to excel in technology cases.

Products and Services Landscape

Technology spans multiple distinct sub-sectors with fundamentally different economics. Identifying the sub-sector immediately shapes your entire analysis.

Based on our analysis of technology cases, the most common scenarios tested are SaaS (35%), platform/marketplace (25%), and hardware (20%).

Revenue Tree: Understanding Tech Economics

Technology business models differ significantly from traditional industries. The dominant models are:

1. Subscription/SaaS Model

ARR = Customers × Average Contract Value
Growth = New ARR + Expansion ARR - Churned ARR

flowchart TD
    A[Annual Recurring Revenue] --> B[New ARR]
    A --> C[Expansion ARR]
    A --> D[Churned ARR]
    
    B --> B1[New Logos]
    B --> B2[Land ACV]
    
    C --> C1[Upsell]
    C --> C2[Cross-sell]
    C --> C3[Price Increases]
    
    D --> D1[Logo Churn]
    D --> D2[Contraction]
    
    style A fill:#1e3a5f,color:#fff
    style B fill:#22c55e,color:#fff
    style C fill:#22c55e,color:#fff
    style D fill:#dc2626,color:#fff

2. Transaction/Platform Model

Revenue = GMV × Take Rate
GMV = Transactions × Average Transaction Value

3. Advertising Model

Revenue = Impressions × CPM / 1000
or
Revenue = Clicks × CPC

Key Revenue Metrics by Model

SaaS Unit Economics Deep Dive

Understanding SaaS unit economics is critical for technology cases:

Cost Structure: Where Tech Dollars Go

SaaS Cost Structure

pie title SaaS Company Cost Structure (% of Revenue)
    "Cost of Revenue" : 25
    "Sales & Marketing" : 35
    "Research & Development" : 20
    "General & Administrative" : 15
    "Operating Profit" : 5

Hardware Cost Structure

Key Cost Insight: Operating Leverage in Software

Software has extraordinary operating leverage because marginal cost of serving an additional customer is near zero. This means:

• Early-stage companies often operate at significant losses while investing in growth
• At scale, software companies can achieve 25-35% operating margins
• The “Rule of 40” (growth rate + profit margin > 40%) balances growth and profitability

Competitive Landscape

Tech competition follows distinct patterns depending on whether network effects exist.

Porter’s Five Forces for Technology

Network Effects Framework

Network effects are the defining competitive advantage in technology. Understanding which type applies is essential:

flowchart LR
    A[Network Effects] --> B[Direct]
    A --> C[Indirect]
    A --> D[Data]
    
    B --> B1[More users → more value]
    B --> B2[Social networks, messaging]
    
    C --> C1[More users → more supply]
    C --> C2[Marketplaces, platforms]
    
    D --> D1[More data → better product]
    D --> D2[AI, recommendations]
    
    style A fill:#1e3a5f,color:#fff
    style B fill:#2563eb,color:#fff
    style C fill:#2563eb,color:#fff
    style D fill:#2563eb,color:#fff

Customer Analysis

Tech customer analysis varies significantly by business model.

Enterprise SaaS Customer Segmentation

Key Customer Metrics

Distribution Channels

Tech distribution has evolved dramatically toward product-led and digital channels.

Software Distribution Models

Hardware Distribution

Supply Chain

Tech supply chains vary by sub-sector, but hardware and semiconductors have particularly complex chains.

Hardware Supply Chain

flowchart LR
    A[Raw Materials] --> B[Component Suppliers]
    B --> C[Contract Manufacturers]
    C --> D[OEM/Brand]
    D --> E[Distribution]
    E --> F[End Customer]
    
    B --> B1[Chips, displays, memory]
    C --> C1[Foxconn, Pegatron]
    E --> E1[Retail, carriers, D2C]
    
    style A fill:#1e3a5f,color:#fff
    style D fill:#2563eb,color:#fff
    style F fill:#1e3a5f,color:#fff

Key Supply Chain Metrics

Semiconductor-Specific Considerations

• Fab vs. Fabless: Foundries (TSMC, Samsung) vs. design-only (Nvidia, AMD, Qualcomm)
• Process Node: Smaller = more performance, higher cost (5nm, 3nm, etc.)
• Capacity Constraints: Fab capacity is limited and lead times are long (18-24 months)
• Cyclicality: Semiconductor demand is highly cyclical

Key Industry Trends

These trends frequently appear in technology cases and shape strategic recommendations.

Important Terminology

Master these terms before your technology case interview:

SaaS Metrics

Platform/Marketplace Terms

Technical/Product Terms

Important Calculations

These calculations frequently appear in technology cases.

SaaS Valuation Metrics

ARR Multiple = Enterprise Value / ARR

• High-growth SaaS: 10-20x ARR
• Mature SaaS: 5-10x ARR
• Struggling: <5x ARR

Rule of 40 = Revenue Growth Rate % + Operating Margin %

• Excellent: >40%
• Good: 20-40%
• Concerning: <20%

Magic Number = Net New ARR (Q) / S&M Spend (Q-1)

• Efficient: >1.0
• Acceptable: 0.5-1.0
• Inefficient: <0.5

Unit Economics Calculations

CAC = Total Sales & Marketing Cost / New Customers Acquired

LTV = (Average Revenue per Customer × Gross Margin) / Churn Rate

• Or: ARPA × Gross Margin × Average Customer Lifetime

LTV:CAC Ratio = LTV / CAC

• Healthy: >3x
• Break-even: 1x
• Unprofitable: <1x

CAC Payback = CAC / (Monthly Revenue per Customer × Gross Margin)

• Best-in-class: <12 months
• Acceptable: 12-18 months
• Concerning: >24 months

Hardware/Platform Calculations

Gross Margin = (Revenue - COGS) / Revenue

• Premium hardware: 35-45%
• Commodity hardware: 15-25%

Take Rate = Platform Revenue / GMV × 100

• Marketplaces: 10-25%
• Payments: 2-3%
• App stores: 15-30%

ARPU = Revenue / Active Users

• Use monthly (ARPU) or annually (ARPA)

Important Considerations

These factors separate strong candidates from average ones in technology cases.

Common Pitfalls

1. Ignoring Unit Economics: High growth means nothing if LTV:CAC is unfavorable. Always ask about customer acquisition efficiency.

2. Underestimating Network Effects: In platform businesses, being second often means being irrelevant. Winner-take-most dynamics are real.

3. Confusing Revenue and Bookings: SaaS companies recognize revenue over time. A $1M deal signed today doesn’t mean $1M revenue today.

4. Missing the Cohort Analysis: Early customers often have different economics than later ones. Ask about cohort performance.

5. Overlooking Switching Costs: High switching costs = pricing power and retention. Low switching costs = commoditization risk.

Questions to Always Ask

• What is the business model (SaaS, marketplace, advertising, hardware)?
• What are the unit economics (LTV:CAC, CAC payback)?
• Is there a network effect, and what type?
• What is the competitive landscape and differentiation?
• What stage is the company (early growth, scaling, mature)?
• What is the go-to-market motion (PLG, inside sales, enterprise)?

Red Flags in Tech Cases

Key Takeaways

• Technology cases require immediate business model identification — SaaS, platform, hardware, and advertising have fundamentally different economics
• SaaS unit economics are critical: know CAC, LTV, NRR, and the Rule of 40 cold
• Network effects define tech competition — understand direct, indirect, and data network effects
• Software has extraordinary operating leverage; expect losses early but high margins at scale
• Customer acquisition motion matters: PLG vs. sales-led has major cost and scalability implications
• Key metrics vary by model: ARR/NRR for SaaS, GMV/take rate for platforms, ASP/units for hardware
• Trends to know: AI transformation, cloud migration, PLG, verticalization, and regulatory pressure

Ready to practice? Browse technology industry cases in our case library, or test your framework in a timed AI Mock Interview to build speed and confidence.