How to solve Product Root Cause Analysis Cases in Product Execution Round?

You’re in a product execution interview, and the interviewer asks: “Our daily active users dropped by 20% last month. What’s the root cause?” Your mind races—“Is it a UX issue? A technical bug? A competitor’s move?” Without a structured approach, RCA cases can feel like finding a needle in a haystack.

At NextSprints, we’ve trained 500+ candidates to turn RCA questions into their secret weapon. In this guide, I’ll walk you through a battle-tested framework, real-world examples (Uber, Airbnb), and phrases that impress hiring managers. Let’s diagnose problems like a pro.

Why Root Cause Analysis Matters (And Why Most Candidates Fail)

RCA cases test your ability to think like a detective. Interviewers want to see:

1. Structured Problem-Solving: Can you break down ambiguity systematically?
2. Data-Driven Insights: Do you prioritize hypotheses with evidence, not hunches?
3. Business Impact Focus: Can you tie root causes to revenue, retention, or other KPIs?

Most candidates fail because they:

• Jump to conclusions (e.g., “It’s the app’s performance!”).
• Ignore data gaps (e.g., not asking for metrics).
• Solve symptoms, not causes (e.g., “Let’s add a feature to fix engagement”).

Here’s the good news: With the right framework, RCA cases become your chance to shine.

The NextSprints RCA Framework: A 6-Step Blueprint

Step 1: Clarify the Problem Scope

Mentor Tip: Start by asking questions to define the problem’s boundaries.

Example: If asked, “Why did Uber Eats orders drop in London?” ask:

“Is the decline isolated to London or broader? Are we seeing drops in new users, existing users, or both? When exactly did the trend start?”

Why This Works: A 20% drop in London could be a local issue (e.g., a competitor’s campaign) vs. a global bug.

Real-World Example:
When Airbnb noticed a booking decline in Paris, they discovered it coincided with a new local tax on short-term rentals—a root cause outsiders often miss.

Step 2: Gather Data (Be a Metrics Detective)

Mentor Tip: Request quantitative and qualitative data. Use the 5 Whys or Fishbone Diagram to organize findings.

Hypotheses for Uber Eats Decline:

1. Technical: App crashes during checkout.
2. Competitive: New rival (Deliveroo) offered discounts.
3. User Experience: Long delivery times.
4. External: Weather disruptions or strikes.

Data to Request:

• User retention cohorts (did existing users stop ordering?).
• App performance metrics (crash rates, load times).
• Customer support tickets (common complaints).

Step 3: Prioritize Hypotheses with the PIE Framework

PIE = Potential, Importance, Ease:

• Potential: How likely is this hypothesis to be the root cause?
• Importance: How much does it impact the business?
• Ease: How quickly can we validate it?

Prioritize: Validate app crashes first—it’s high-potential and easy to check.

Step 4: Identify the Root Cause (Not the Symptom)

Mentor Tip: Use the 5 Whys to dig deeper.

Example for Uber Eats:

1. Why did orders drop? Checkout crashes increased.
2. Why are crashes happening? Recent app update (v2.5).
3. Why did the update cause crashes? QA missed a payment gateway bug.
4. Why did QA miss it? Rushed release before a holiday.
5. Why the rushed release? Leadership pressured the team to meet Q2 targets.

Root Cause: Process breakdown in release management.

Step 5: Propose Solutions (Focus on Prevention)

Mentor Tip: Solve the root cause, not just the symptom.

Solutions for Uber Eats:

1. Short-Term: Roll back the buggy update + compensate affected users with discounts.
2. Long-Term: Implement a staged rollout process (e.g., 5% users first).
3. Systemic Fix: Add payment gateway testing to QA checklists.

Success Metrics:

• Reduction in crash rates (<1%).
• Order recovery rate (orders from affected users post-fix).

Step 6: Validate and Iterate

Mentor Tip: Show you’re data-driven, even in interviews.

Validation Plan for Uber Eats:

1. A/B Test: Compare order rates in London (rollback) vs. Manchester (control).
2. Track:
   • Crash rates.
   • Customer satisfaction (NPS).
3. Iterate: If orders don’t rebound, investigate competitor moves next.

Real-World Example: Solving “Why Did Airbnb’s Booking Rate Drop in Paris?”

Step 1: Clarify Scope

• Decline localized to Paris (not global). Started 3 months ago.

Step 2: Gather Data

• Quantitative: 40% drop in new bookings; no change in cancellations.
• Qualitative: Hosts reported fewer inquiries.

Step 3: Prioritize Hypotheses

• Top Hypothesis: New regulations (e.g., taxes, zoning laws).

Step 4: Root Cause

• Paris introduced a 30% tax on short-term rentals, making Airbnb less competitive than hotels.

Step 5: Solutions

• Short-Term: Highlight “entire home” listings exempt from the tax.
• Long-Term: Lobby for policy changes + partner with tax-compliant hosts.

Step 6: Validate

• Track booking rates for tax-exempt listings vs. others.

Common Mistakes to Avoid (From a FAANG PM’s Notes)

1. Confusing Symptoms with Causes:

   • ❌ “Orders dropped because of bad UX.”
   • ✅ “Orders dropped because a payment bug in v2.5 increased checkout crashes by 25%.”

2. Ignoring External Factors:

   • ❌ “It’s always a tech issue.”
   • ✅ “Check for policy changes, weather, or competitor moves.”

3. Overcomplicating Solutions:

   • ❌ “Rebuild the entire app.”
   • ✅ “Roll back the update and improve QA processes.”

Your RCA Action Plan

1. Practice with Real Cases: Use NextSprints’ RCA Case Library (e.g., “Why did Slack’s DAU drop?”).
2. Learn from Post-Mortems: Study how companies like Uber or Airbnb handle public crises.
3. Simulate Pressure: Do timed mock interviews with peers.

Pro Tip: Use the Fishbone Diagram to visually map causes (People, Process, Tech, External).

FAQs: Answering Your Burning Questions

Q: How long should my RCA answer take?

A: 7–10 minutes. Focus on depth, not speed.

Q: What if the interviewer gives no data?

A: Ask! “Could I see retention cohorts? Crash reports?” If they say no, state assumptions clearly.

Q: How technical should I get?

A: Mention tools (e.g., Splunk for logs) but focus on insights, not technical jargon.

Final Words

You’ve got this. 🚀

Root cause analysis isn’t about being a genius—it’s about being systematic. The next time you’re asked, “Why did metrics drop?” channel your inner detective: clarify, gather data, prioritize, and solve.

And remember: Even seasoned PMs miss root causes sometimes. What matters is showing you can learn and iterate.