Technology cases at McKinsey, BCG, and Bain increasingly hinge on whether candidates can wield the right metric at the right moment. Based on our analysis of 600+ tech-sector case interviews, quantitative fluency — knowing not just the formula but the benchmark that signals health or distress — separates top candidates from those who structure well but cannot pressure-test their own numbers.
This guide gives you the metrics toolkit organized by business model, so you can rapidly diagnose a tech company’s situation and drive toward a quantified recommendation.
SaaS and Subscription Metrics
Subscription-based software companies are the most common tech case context. These five metrics form the backbone of any SaaS analysis:
| Metric | Formula | Healthy Benchmark | Red Flag |
|---|---|---|---|
| ARR (Annual Recurring Revenue) | MRR × 12 | Growing 40%+ (early stage) or 20%+ (scale) | Flat or declining quarter-over-quarter |
| Net Dollar Retention (NDR) | (Starting ARR + Expansion − Contraction − Churn) ÷ Starting ARR | >120% for enterprise SaaS | <100% means shrinking without new sales |
| LTV/CAC Ratio | Customer Lifetime Value ÷ Customer Acquisition Cost | >3x | <1x means losing money on every customer acquired |
| CAC Payback Period | CAC ÷ (Monthly Revenue per Customer × Gross Margin) | <18 months | >36 months signals unsustainable growth spending |
| Gross Margin | (Revenue − COGS) ÷ Revenue | 70–85% for software | <60% suggests infrastructure or services cost problem |
In our experience coaching candidates, the most powerful move in a SaaS case is connecting NDR to growth economics. If NDR exceeds 130%, the company can grow revenue even at zero new customer acquisition — this reframes the entire strategy discussion from “how to acquire more” to “how to expand within existing accounts.”
The Rule of 40
The Rule of 40 is a single-number health check that balances growth against profitability:
Revenue Growth Rate (%) + EBITDA Margin (%) ≥ 40
A company growing at 60% with a −15% margin scores 45 — healthy. A company growing at 10% with a 15% margin scores 25 — underperforming. In case interviews, this metric helps you quickly assess whether a SaaS company’s growth-at-all-costs strategy is justified or whether it needs a profitability pivot.
Platform and Marketplace Metrics
Platform cases require a different quantitative lens because value comes from network density rather than individual subscriptions:
flowchart LR
A[Supply Side] -->|Listings/Inventory| B((Platform))
B -->|Matches/Transactions| C[Demand Side]
C -->|Revenue: GMV × Take Rate| D[Platform Revenue]
B -->|Key Metric: Liquidity| B
A -->|Key Metric: Supply Utilization| A
C -->|Key Metric: Conversion Rate| C
| Metric | What It Measures | Benchmark Range |
|---|---|---|
| GMV (Gross Merchandise Volume) | Total value of transactions through platform | Context-dependent; growth rate matters more |
| Take Rate | Platform revenue ÷ GMV | 5–30% depending on category and value-add |
| Liquidity | % of listings that transact within a time window | >30% for healthy marketplace |
| Supply-Demand Ratio | Active supply units ÷ active demand units | 3:1 to 5:1 for most marketplaces |
| Contribution Margin per Transaction | Revenue − variable costs per transaction | Positive at unit level for sustainable platforms |
The critical insight for platform cases: a marketplace with high GMV but low take rate and negative unit economics is not a business — it is subsidized behavior. In our work with candidates, we find that quantifying the path to positive unit economics is the strongest recommendation you can make in a platform strategy case.
Digital Transformation ROI Metrics
When traditional companies invest in digital capabilities, interviewers expect you to build a business case. These are the metrics that structure a transformation ROI analysis:
| Investment Category | Typical Range | Key ROI Metric |
|---|---|---|
| Cloud migration | $5M–$50M for mid-size enterprise | TCO reduction (target: 25–40% over 5 years) |
| Customer experience digitization | $2M–$20M | Digital channel adoption rate, cost-to-serve reduction |
| Process automation (RPA/AI) | $1M–$10M per function | FTE equivalent savings, error rate reduction |
| Data platform build-out | $3M–$30M | Time-to-insight, decision automation rate |
The Transformation Value Bridge
In our experience, the most effective way to structure a digital transformation ROI case is the value bridge — mapping investment to value capture across three time horizons:
flowchart TD
A[Total Investment] --> B[Year 1: Foundation]
A --> C[Year 2-3: Scale]
A --> D[Year 4-5: Optimize]
B --> B1[Cost avoidance<br/>15-20% of value]
C --> C1[Revenue enablement<br/>40-50% of value]
D --> D1[Competitive moat<br/>30-40% of value]
B1 --> E[NPV Calculation]
C1 --> E
D1 --> E
E --> F{IRR > Hurdle Rate?}
F -->|Yes| G[Recommend Invest]
F -->|No| H[Restructure Scope]
A common mistake is treating digital transformation ROI as a simple payback calculation. In reality, based on our analysis of transformation cases, approximately 60% of the value comes from revenue enablement and competitive positioning in years 2–5 rather than immediate cost savings.
AI and Automation Unit Economics
AI cases increasingly require candidates to assess whether a use case is economically viable. The metrics below help you build a quantified business case:
| Metric | Formula | Decision Rule |
|---|---|---|
| Cost per AI inference | Infrastructure + model cost per API call | Must be <10% of value created per transaction |
| Automation rate | Tasks fully automated ÷ total tasks | 60–80% for structured tasks; 20–40% for unstructured |
| Human-in-the-loop cost | (1 − automation rate) × manual processing cost | Factor into true unit economics |
| Accuracy threshold | Minimum precision/recall for production use | Domain-specific; 95%+ for financial, 85%+ for content |
| Time-to-value | Months from deployment to measurable business impact | <6 months for quick wins; 12–18 for enterprise AI |
The critical question in AI cases is not “can we build it?” but “does the unit economics work at scale?” A model that costs $0.03 per inference and replaces $0.50 of human labor has clear economics. A model that costs $0.15 per inference and creates $0.10 of incremental value does not.
How to Apply Metrics in Real Time
Knowing metrics is necessary but insufficient. The skill that impresses interviewers is deploying the right metric at the right moment in your case structure:
| Case Moment | What to Do | Example |
|---|---|---|
| Opening structure | Name 2–3 metrics you will use to evaluate | “I’ll assess this SaaS company using NDR, Rule of 40, and CAC payback” |
| Hypothesis testing | Use benchmarks to pressure-test data | “NDR of 85% is well below the 120% enterprise benchmark — this suggests a retention problem” |
| Quantified recommendation | Anchor your answer in numbers | “Improving NDR from 85% to 110% adds $12M in ARR without additional acquisition spend” |
| Interviewer pushback | Reference a metric the interviewer hasn’t mentioned | “The Rule of 40 score of 25 suggests we should prioritize margin expansion over growth” |
Practice Scenarios
Test your metrics fluency with these mini-cases from our technology industry case library:
Scenario 1: SaaS Pricing Pivot A B2B SaaS company has ARR of $50M, NDR of 95%, and CAC payback of 28 months. The CEO asks whether to raise prices. What metrics would you analyze, and what benchmarks inform your recommendation?
Scenario 2: Marketplace Subsidy Trap A food delivery platform has $2B GMV, 12% take rate, and −$3 contribution margin per order. How do you structure the path to positive unit economics?
Scenario 3: Cloud Migration Business Case A retailer spends $40M annually on on-premise infrastructure. A cloud migration would cost $15M upfront. What ROI framework and metrics do you use to evaluate?
For full-length practice cases with these scenarios, explore our profitability cases and growth strategy cases filtered by the technology industry.
Key Takeaways
- SaaS cases revolve around five core metrics: ARR, NDR, LTV/CAC, CAC payback, and gross margin — know the formulas and the benchmarks that signal health or distress.
- The Rule of 40 (growth rate + margin ≥ 40) is a rapid single-number diagnostic for any subscription business.
- Platform cases require different metrics — GMV, take rate, liquidity, and unit economics per transaction — because value comes from network density, not individual subscriptions.
- Digital transformation ROI cases demand a multi-year value bridge: cost avoidance in year 1, revenue enablement in years 2–3, and competitive moat in years 4–5.
- AI unit economics (cost per inference vs. value created per transaction) determine whether a use case is viable at scale.
- The real skill is deploying metrics in real time — naming them in your structure, using benchmarks to test hypotheses, and anchoring your recommendation in quantified impact.
Ready to practice applying these metrics in full case scenarios? Explore technology cases in our case library or test your quantitative skills with our AI Mock Interview that provides real-time feedback on your analytical approach.