Technology & Digital Strategy Cases: Patterns and Problem-Solving

Technology and digital strategy cases now represent roughly one in four consulting interviews at McKinsey, BCG, and Bain. Unlike traditional industry cases, these require candidates to demonstrate product thinking, understand agile delivery models, and connect technology decisions to measurable business outcomes. Based on our analysis of 800+ case interviews, the candidates who fail these cases typically apply generic frameworks without adapting them to how technology actually creates value.

Two Distinct Case Archetypes

Technology cases in consulting interviews split into two recurring archetypes. Recognizing which one you face in the first 30 seconds determines your entire approach.

In our experience working with candidates across MBB firms, roughly 60% of technology cases fall into the digital transformation archetype, while 40% involve tech-native companies. The key differentiator: tech-native cases demand you understand the product itself, while transformation cases demand you understand the organization adopting it.

The Product Thinking Framework

For tech-native cases, a standard profitability or market entry framework falls flat. Interviewers expect you to think like a product manager — starting from user needs and working outward to business impact.

flowchart TD
    A[Identify User Problem] --> B[Define Target Segment]
    B --> C[Map Current Solutions]
    C --> D{Build vs. Buy vs. Partner?}
    D -->|Build| E[MVP Scope & Sprint Plan]
    D -->|Buy| F[Vendor Evaluation & Integration]
    D -->|Partner| G[Partnership Economics]
    E --> H[Success Metrics & KPIs]
    F --> H
    G --> H
    H --> I[Scale & Iterate]

This flow applies whether the case involves launching a new feature, entering an adjacent market, or deciding on a technology investment. The critical skill interviewers assess is your ability to scope an MVP — identifying the minimum set of capabilities that validates the business hypothesis before committing full resources.

Key Concepts Interviewers Expect You to Know

Based on our review of technology cases from top consulting firms, these concepts appear repeatedly. You do not need deep technical expertise, but you must use these terms accurately and connect them to business outcomes.

Agile vs. Waterfall Delivery

Traditional consulting cases assume linear project execution. Technology cases require you to understand iterative delivery:

When an interviewer asks “how would you recommend the client implement this?”, demonstrating awareness of agile delivery — and when waterfall is actually appropriate — signals genuine technology fluency.

Platform Economics and Network Effects

Technology cases frequently involve platforms where value increases with each additional user. The critical metrics:

• Liquidity: The percentage of listings that result in transactions (marketplaces aim for 15-30%)
• Take rate: Platform’s revenue share per transaction (typically 5-25% depending on value-add)
• Cross-side network effects: More buyers attract more sellers, creating a defensible flywheel
• Multi-homing cost: How easy it is for users to switch — low multi-homing cost means weaker competitive moats

Build vs. Buy Decision Framework

This question appears in approximately 35% of technology cases. Structure your analysis around four dimensions:

1. Strategic importance — Is this capability core to the client’s differentiation?
2. Time-to-market — How urgent is the competitive window?
3. Internal capability — Does the organization have engineering talent and infrastructure?
4. Total cost of ownership — Including integration, maintenance, vendor lock-in, and opportunity cost

Common Pitfalls in Technology Cases

In our experience coaching candidates through technology-focused mock interviews, these are the five mistakes that most frequently lead to poor evaluations:

1. Applying generic frameworks without adaptation — Using a standard profitability tree for a freemium SaaS company misses the entire unit economics logic
2. Ignoring the organizational dimension — Technology implementation fails 70% of the time due to people and process issues, not technical ones
3. Conflating revenue with value — Platform businesses often sacrifice short-term revenue to build network effects; recommending immediate monetization shows misunderstanding
4. Assuming linear scaling — Technology products have near-zero marginal cost, which fundamentally changes how you model growth scenarios
5. Skipping success metrics — Every technology recommendation needs measurable KPIs (conversion rate, adoption rate, time-to-value) to demonstrate analytical rigor

Sample Mini-Case: E-Commerce Platform Expansion

Prompt: A mid-size e-commerce marketplace (GMV of $2B, 15% take rate) is considering expanding from consumer electronics into home furnishings. How would you evaluate this opportunity?

Strong approach structure:

1. Market sizing — Home furnishings addressable market, online penetration rate (currently ~25% vs. 45% for electronics)
2. Supply-side feasibility — Can existing seller acquisition playbook attract furniture merchants? What unique logistics requirements exist (large items, white-glove delivery)?
3. Demand-side synergy — What percentage of current buyers also purchase home furnishings? Cross-selling potential from existing user base
4. Platform adaptation — Does the product need new features (3D visualization, room planning tools)? Estimated development investment
5. Unit economics — Expected take rate in furnishings (likely lower, 8-12%, due to higher AOV), customer acquisition cost, payback period
6. Competitive landscape — Existing specialized players (Wayfair, IKEA online), their strengths, and potential differentiation

This structure demonstrates product thinking (features needed), platform economics (take rate differences), and strategic reasoning (why this category, why now).

Preparing for Technology Cases

Key Takeaways

• Technology cases split into two archetypes: tech-native strategy (product/platform focus) and digital transformation (adoption by traditional companies) — recognize which you face immediately
• Product thinking — starting from user problems, scoping MVPs, and measuring outcomes — differentiates strong candidates from those who apply generic frameworks
• Platform economics (network effects, liquidity, take rate) and SaaS metrics (ARR, churn, LTV/CAC) are non-negotiable vocabulary for technology cases
• Build-vs-buy decisions should be structured around strategic importance, time pressure, internal capability, and total cost of ownership
• Technology implementations fail most often due to organizational resistance, not technical limitations — always address the people dimension
• Every technology recommendation needs specific, measurable success metrics to demonstrate analytical rigor

Ready to practice technology and digital strategy cases with real-time feedback? Explore technology industry cases in our case library, or sharpen your skills with AI Mock Interview sessions that simulate the exact pressure of a consulting interview. For foundational frameworks, review our growth strategy guide and operations case framework.