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RCA Interview Simulator
Generates complete example RCA investigations showing realistic PM execution interview conversations from scenario through evaluation. Study systematic thinking, data requests, and company-specific feedback for Meta, Google, and Uber.
What You Get
Master metric drop debugging by studying complete example investigations that demonstrate strong RCA approaches, systematic thinking patterns, strategic data requests, and detailed performance evaluation calibrated to Meta, Google, or Uber interview standards.
The Problem
The Solution
How It Works
- 1 Request an example investigation by specifying your preferences: difficulty level (easy/medium/hard), product type (social media/marketplace/B2B SaaS/e-commerce), company calibration (Meta/Google/Uber), time pressure, and experience level
- 2 Skill generates complete example investigation showing scenario presentation, full interviewer-candidate conversation with 5-15 exchanges, strategic data requests and responses, hypothesis formation and testing, root cause identification with causal mechanism, and detailed performance evaluation
- 3 Study the example to learn systematic investigation frameworks (segment → time → changes → mechanism), understand what 'good' data requests look like, see how to form and state hypotheses with reasoning, study company-specific evaluation criteria, and identify patterns in strong vs weak approaches
- 4 Apply learnings by noting the framework used, observing how questions were bundled for efficiency, seeing how hypotheses were stated with explicit reasoning, understanding company-specific focus areas (MECE for Meta, quantification for Google, feedback loops for Uber), and using improvement areas to guide your own practice
- 5 Optionally request additional examples at different difficulty levels, product types, or company calibrations to study various scenarios and approaches
What You'll Need
- Specify your preferences (difficulty, product type, company calibration, time pressure, experience level) - all optional with sensible defaults
- Study the complete example investigation provided to learn systematic thinking patterns and company-specific evaluation criteria
- Apply the frameworks and patterns you observe to your own interview practice or interactive sessions
Get This Skill
Requires Pro subscription ($9/month)
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B2B SaaS Activation Drop - Mid-Market Customization Needs
Medium difficulty B2B SaaS investigation with 15-minute time limit and first-timer guidance showing 26% activation drop in DataSync platform. Demonstrates bundled questioning efficiency, company size segmentation revealing mid-market concentration, funnel analysis pinpointing exact drop at 'Start Sync' button, understanding user psychology around customization needs and trust, and connecting product changes to segment-specific behavior patterns. Shows time management completing in 13.5 minutes, includes beginner-friendly guidance throughout, and provides both Meta and Google calibration feedback. Excellent example for first-time RCA practitioners.
Music Streaming Session Drop - Android Playback Quality Bug
Medium difficulty investigation of a 16% session duration drop in Streamify music streaming. Demonstrates systematic platform segmentation, version isolation technique to prove causation, behavioral pattern analysis (song skipping vs completion), and connecting technical bugs (media player library) to user behavior changes. Shows strong MECE thinking and complete causal chain explanation from bug to metric impact.
Marketplace Supply-Demand Spiral - Pricing Algorithm Backfire
Hard difficulty marketplace investigation of 22% trip volume drop in RideShare platform showing two-sided dynamics and feedback loops. Demonstrates advanced marketplace thinking by requesting both rider AND driver metrics simultaneously, identifying vicious cycle where supply reduction drives demand destruction which further reduces supply, tracing 7-step causal chain from algorithm change through driver economics to marketplace equilibrium failure, and quantifying impact at each cascade step. Includes Uber-style calibration focusing on feedback loops and multi-order effects. Shows staff-level PM marketplace expertise.