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.
| Sub-Sector | Key Products/Services | Typical Margins | Key Success Factors |
|---|---|---|---|
| Enterprise SaaS | Subscription software (CRM, ERP, HCM, collaboration) | Gross 70-85%, Operating 15-25% | Net revenue retention, CAC payback, land-and-expand |
| Consumer Software | Apps, gaming, productivity tools | Gross 80-95%, Net varies widely | DAU/MAU, engagement, monetization |
| Cloud Infrastructure | IaaS, PaaS (AWS, Azure, GCP) | Gross 60-65%, improving with scale | Market share, enterprise workload capture |
| Hardware | Devices, components, networking equipment | Gross 30-45% | Supply chain, R&D cycles, ecosystem lock-in |
| Semiconductors | Chips, processors, memory | Gross 45-65% | Fab capacity, design wins, Moore’s Law |
| Advertising/Platforms | Search, social media, marketplaces | Gross 60-85% | User engagement, data, ad inventory |
| IT Services | Consulting, system integration, managed services | Gross 25-35% | Utilization, talent, client relationships |
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
| Business Model | Primary Metrics | Healthy Benchmarks | Diagnostic Questions |
|---|---|---|---|
| SaaS | ARR, NRR, CAC Payback, LTV:CAC | NRR >110%, CAC Payback <18mo, LTV:CAC >3x | Is growth efficient? Are customers expanding? |
| Marketplace | GMV, Take Rate, Liquidity | Take rate 10-25%, buyer/seller retention >70% | Is there sufficient liquidity? Is take rate sustainable? |
| Advertising | DAU/MAU, ARPU, Ad Load, CPM | DAU/MAU >50%, ARPU growing | Is engagement strong? Is ad load at capacity? |
| Hardware | ASP, Units, Attach Rate | Attach rate >30%, gross margin >35% | Is there services/software attach? Is ASP rising or falling? |
SaaS Unit Economics Deep Dive
Understanding SaaS unit economics is critical for technology cases:
| Metric | Definition | Best-in-Class | Warning Signs |
|---|---|---|---|
| Net Revenue Retention (NRR) | Revenue from existing customers this year / last year | >120% | <100% indicates net contraction |
| Gross Revenue Retention (GRR) | Revenue retained (excl. expansion) | >90% | <85% indicates churn problem |
| CAC Payback | Months to recover customer acquisition cost | <12 months | >24 months is concerning |
| LTV:CAC Ratio | Lifetime value / Customer acquisition cost | >3x | <2x means unprofitable growth |
| Magic Number | Net new ARR / Sales & Marketing spend | >0.75 | <0.5 indicates inefficient spend |
| Rule of 40 | Revenue growth % + Operating margin % | >40% | <20% is below healthy threshold |
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
| Cost Category | % of Revenue (Growth Stage) | % of Revenue (Mature) | Key Drivers |
|---|---|---|---|
| Cost of Revenue | 20-30% | 15-25% | Hosting, support, customer success |
| Sales & Marketing | 40-60% | 20-35% | Sales headcount, demand gen, brand |
| R&D | 20-35% | 15-25% | Engineering headcount, tools |
| G&A | 10-20% | 8-15% | Admin, legal, finance |
| Operating Margin | -20% to +10% | 15-30% | Improves with scale |
Hardware Cost Structure
| Cost Category | % of Revenue | Sub-Components | Optimization Levers |
|---|---|---|---|
| COGS | 55-70% | Components, manufacturing, logistics | Volume discounts, design-to-cost, vertical integration |
| R&D | 8-15% | Hardware design, firmware, testing | Platform reuse, modular design |
| Sales & Marketing | 8-15% | Channel costs, advertising, retail | Direct-to-consumer shift, digital marketing |
| G&A | 5-10% | Corporate overhead | Scale leverage |
| Operating Margin | 5-15% | — | Services attach, premium positioning |
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
| Force | SaaS | Platforms/Marketplaces | Hardware |
|---|---|---|---|
| Rivalry | High (crowded categories) | Medium-Low (winner-take-most) | High (commoditization) |
| New Entrants | High (low barriers to start) | Low (network effects defend) | Medium (capital-intensive) |
| Supplier Power | Low (cloud commoditized) | Low | Medium-High (key components) |
| Buyer Power | Medium (switching costs) | Low (locked into ecosystem) | High (price comparison easy) |
| Substitutes | High (build vs. buy, alternatives) | Low (few viable alternatives) | Medium (competing ecosystems) |
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
| Network Effect Type | Definition | Examples | Defensibility |
|---|---|---|---|
| Direct | Value increases with each user | WhatsApp, Zoom, Slack | Very high — hard to displace |
| Indirect/Cross-side | More users attract more supply (and vice versa) | Uber, Airbnb, App Store | High — requires both sides |
| Data | More usage creates better ML/AI | Google Search, Netflix recommendations | Medium-high — data moats erode |
| Economies of Scale | Unit costs decrease with volume | AWS, manufacturing | Medium — can be replicated |
Customer Analysis
Tech customer analysis varies significantly by business model.
Enterprise SaaS Customer Segmentation
| Segment | Definition | Characteristics | Sales Motion |
|---|---|---|---|
| Enterprise | >1000 employees, >$500K ACV | Long sales cycles (6-12mo), custom requirements, multi-year deals | Field sales, solutions selling |
| Mid-Market | 100-1000 employees, $25K-500K ACV | 2-4 month cycles, growing sophistication | Inside sales + field |
| SMB | <100 employees, <$25K ACV | Self-serve or light touch, high volume, higher churn | PLG, inside sales |
| Consumer | Individual users | Freemium conversion, viral loops | Product-led, marketing |
Key Customer Metrics
| Metric | Definition | Benchmark | Diagnostic Value |
|---|---|---|---|
| DAU/MAU | Daily active / Monthly active users | >50% is strong engagement | Measures stickiness |
| Time to Value | Days from signup to activation | <7 days ideal | Predicts retention |
| Net Promoter Score (NPS) | Likelihood to recommend | >40 is excellent for B2B | Predicts expansion |
| Logo Retention | % of customers retained | >85% annually | Base health metric |
| Dollar Retention (NRR) | Revenue retained + expanded | >110% is best-in-class | Growth sustainability |
Distribution Channels
Tech distribution has evolved dramatically toward product-led and digital channels.
Software Distribution Models
| Channel | CAC | Control | Best For |
|---|---|---|---|
| Product-Led Growth (PLG) | Low ($100-500) | High | SMB, prosumer, viral products |
| Inside Sales | Medium ($2K-15K) | High | Mid-market, transactional |
| Field Sales | High ($30K-100K+) | High | Enterprise, complex deals |
| Channel/Partners | Variable (15-30% of ACV) | Low | Geographic expansion, verticals |
| Marketplaces | Variable (15-25% of transaction) | Low | Discovery, credibility |
Hardware Distribution
| Channel | Margin Impact | Volume | Control | Best For |
|---|---|---|---|---|
| Direct (D2C) | Highest | Lower | Very high | Premium, high-touch |
| Retail | Medium (40-50% of MSRP) | High | Low | Mass market, impulse |
| Carrier | Medium | Very high | Low | Subsidized devices |
| B2B/Enterprise | Variable | Medium | Medium | Corporate accounts |
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
| Metric | Definition | Benchmark | Significance |
|---|---|---|---|
| Inventory Turns | COGS / Average Inventory | 8-12x for hardware | Working capital efficiency |
| Days of Supply | Inventory / Daily shipments | 30-60 days | Demand-supply balance |
| Lead Time | Order to delivery | Varies widely | Responsiveness |
| Yield Rate | Good units / Total units produced | >95% | Manufacturing quality |
| Component Cost % of BOM | Key component cost / Total BOM | Varies | Supply concentration risk |
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.
| Trend | Impact | Case Relevance | Key Data |
|---|---|---|---|
| AI/ML Everywhere | Transforming products, operations, entire industries | Product strategy, competitive response | ChatGPT reached 100M users in 2 months |
| Cloud Migration | Shift from on-premise to cloud infrastructure | Market sizing, pricing strategy | Cloud is ~$500B market, growing 20%+ annually |
| Product-Led Growth | Self-serve replacing sales-led for many segments | GTM strategy, unit economics | PLG companies grow faster with lower CAC |
| Verticalization | Horizontal platforms becoming vertical-specific | Market entry, differentiation | Vertical SaaS growing faster than horizontal |
| Cybersecurity Imperative | Security as requirement, not feature | All tech cases | Cyber market >$200B, growing 10%+ |
| Privacy/Regulation | GDPR, CCPA, antitrust scrutiny | Risk assessment, strategy constraints | Big Tech facing multiple regulatory actions |
Important Terminology
Master these terms before your technology case interview:
SaaS Metrics
| Term | Definition | Usage Context |
|---|---|---|
| ARR/MRR | Annual/Monthly Recurring Revenue | Core subscription metric |
| NRR/NDR | Net Revenue Retention / Net Dollar Retention | Expansion + retention health |
| CAC | Customer Acquisition Cost | Sales efficiency |
| LTV | Lifetime Value of a customer | Unit economics |
| ACV | Annual Contract Value | Deal sizing |
| Bookings | Total contract value signed | Leading indicator |
| Billings | Amount invoiced | Cash flow indicator |
| Deferred Revenue | Collected but not yet recognized | Balance sheet liability |
Platform/Marketplace Terms
| Term | Definition | Usage Context |
|---|---|---|
| GMV | Gross Merchandise Value | Total transaction volume |
| Take Rate | Platform fee % of GMV | Monetization metric |
| Liquidity | Sufficient supply meeting demand | Marketplace health |
| Network Effects | Value increases with more users | Competitive moat |
| Multi-homing | Users on multiple platforms | Competitive risk |
| Disintermediation | Parties transacting off-platform | Leakage risk |
Technical/Product Terms
| Term | Definition | Usage Context |
|---|---|---|
| API | Application Programming Interface | Integration, platform strategy |
| Freemium | Free basic, paid premium | Acquisition model |
| PLG | Product-Led Growth | Go-to-market strategy |
| Churn | Customer/revenue loss | Retention metric |
| Cohort | Group of customers by acquisition period | Analysis methodology |
| Stickiness | DAU/MAU ratio | Engagement metric |
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
-
Ignoring Unit Economics: High growth means nothing if LTV:CAC is unfavorable. Always ask about customer acquisition efficiency.
-
Underestimating Network Effects: In platform businesses, being second often means being irrelevant. Winner-take-most dynamics are real.
-
Confusing Revenue and Bookings: SaaS companies recognize revenue over time. A $1M deal signed today doesn’t mean $1M revenue today.
-
Missing the Cohort Analysis: Early customers often have different economics than later ones. Ask about cohort performance.
-
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
| Signal | What It Suggests | Follow-Up Analysis |
|---|---|---|
| High growth but CAC payback >24 months | Unsustainable growth | Examine unit economics, path to efficiency |
| NRR declining despite logo retention | Contraction, pricing pressure | Analyze expansion drivers, competitive threats |
| Take rate increasing while GMV slows | Platform squeeze, disintermediation risk | Assess value delivered, multi-homing |
| R&D % of revenue increasing without product velocity | Engineering inefficiency | Examine team productivity, technical debt |
| Customer concentration >20% | Revenue risk | Assess contract terms, expansion potential |
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.