Case Study Framework

Case study interviews are open-ended problems that test your ability to structure ambiguity, make reasonable assumptions, and communicate clearly. Here's a framework that works across question types.

The STAR Method for Data Cases

Adapt the classic STAR method for data science:

Letter	Standard	Data Science Adaptation
Situation	Context	Clarify the problem, define metrics
Task	Objective	What decision are we trying to make?
Action	Approach	Analytical approach, data sources
Result	Outcome	Insights, recommendations, tradeoffs

Step 1: Clarify Before You Solve

Never jump to an answer. Ask questions:

Problem scoping:

• What's the business goal?
• Who is the stakeholder?
• What's the timeline for decision?
• What data is available?

Metric clarification:

• How is success defined?
• What's the current baseline?
• What would be a meaningful improvement?

Example clarification: Interviewer: "How would you improve user retention?"

You: "Before I dive in, I'd like to clarify:

• Are we focused on a specific product or overall platform?
• How is retention currently measured? 7-day, 30-day, or something else?
• What's our current retention rate and what's the target?
• Are there specific user segments we're most concerned about?"

Step 2: Structure Your Approach

Organize your thinking before analyzing:

Framework options:

Framework	Best For
AARRR funnel	User lifecycle questions
Issue tree	Root cause analysis
2x2 matrix	Prioritization decisions
Hypothesis-driven	Metric investigation

Example issue tree:

Why is retention low?
├── Users don't find value
│   ├── Wrong target users
│   ├── Product doesn't solve their problem
│   └── Value isn't communicated clearly
├── Users find value but leave
│   ├── Competitor offers better value
│   ├── Price is too high
│   └── Usage occasion is infrequent
└── Users want to stay but can't
    ├── Technical issues
    ├── Account/payment problems
    └── Platform availability

Step 3: Make Explicit Assumptions

State assumptions clearly - interviewers want to see your reasoning:

Good assumption format: "I'll assume that [X] because [reason]. If this assumption is wrong, my analysis would change because [Y]."

Examples:

• "I'll assume our users are price-sensitive because we're in a commoditized market. If they're actually premium-focused, I'd prioritize quality metrics over conversion."
• "I'll assume mobile and desktop behaviors are similar. If data shows they differ significantly, I'd segment the analysis."

Step 4: Prioritize with Tradeoffs

Show you understand business constraints:

Impact vs Effort matrix:

High Impact, Low Effort	High Impact, High Effort
Quick wins - do first	Strategic bets - plan carefully
Fix broken onboarding	Build personalization engine

Low Impact, Low Effort	Low Impact, High Effort
Nice to have - maybe	Avoid
Minor UI tweaks	Complex features few want

Articulate tradeoffs: "We could pursue A, which is faster but only addresses 30% of users, or B, which takes longer but addresses 70%. Given our timeline of [X], I'd recommend [choice] because [reasoning]."

Step 5: Deliver Recommendations

End with clear, actionable recommendations:

Structure:

1. Lead with the recommendation: "I recommend X"
2. Support with evidence: "Because the data shows Y"
3. Acknowledge tradeoffs: "The risk is Z, which we'd mitigate by W"
4. Define success: "We'd measure success by [metric] and expect [result]"

Example: "Based on the analysis, I recommend:

Primary: Fix the mobile checkout flow (high impact, medium effort)

• Data shows 40% of cart abandonment happens on mobile payment screen
• Expected impact: +5% conversion rate
• Timeline: 2 weeks

Secondary: Add guest checkout option (medium impact, low effort)

• 15% of abandonment comes from users refusing to create account
• Expected impact: +2% conversion rate
• Timeline: 1 week

Measurement: Track daily conversion rate by device and checkout type. Success is +5% overall conversion within 4 weeks of launch."

Practice Question

Question: "Uber is considering launching grocery delivery. How would you analyze this opportunity?"

Try structuring your answer using the framework above before reading further.

Sample approach:

1. Clarify: Target market, timeline, success criteria
2. Structure: Market sizing → Competitive landscape → Operational fit → Financial model
3. Assumptions: Leverage existing driver network, similar demand patterns to food delivery
4. Prioritize: Which cities/markets to test first
5. Recommend: Pilot in 3 markets with specific success metrics

The framework is your safety net. Even if you don't know the "right" answer, you can demonstrate rigorous thinking. :::