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Calm·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Did a data science interview loop with Calm, got a product analytics case question about diagnosing geographic underperformance. Pretty standard case format but the scope of it was broader than I expected.

Questions Asked (1)

Q1

What factors would you investigate to figure out why the app is underperforming in a new geography?

Root Cause AnalysisProduct Analytics & MetricsProduct Strategy
Author's notes

I started with supply-side stuff like content availability and localization, then moved to acquisition funnel metrics, then retention.

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AI HintsAI Generated

Suggested Approach

Start by clarifying what 'underperforming' means in this context—compare key metrics against targets or other geographies. Then systematically investigate external factors (market, competition, culture), internal factors (product, tech, marketing), and user behavior to identify root causes.

Pro tip: Show that you'd prioritize hypotheses based on potential impact and ease of testing, and propose a data-driven approach to validate them. This demonstrates product thinking and engineering pragmatism.

1. Define success and gather baseline data

Clarify what metrics define performance (e.g., downloads, DAU, retention, revenue) and compare the new geography's data to targets and other regions to quantify the gap.

2. Analyze external market factors

Investigate market-specific factors like cultural fit, local competition, regulatory environment, device/OS distribution, and network infrastructure that could impact adoption.

3. Examine internal product and technical factors

Check for localization issues (language, content, pricing), technical problems (latency, crashes, payment methods), and marketing effectiveness (channels, messaging) in that geography.

4. Analyze user behavior and feedback

Look at funnel metrics (install-to-signup, engagement, retention) and user feedback (reviews, support tickets) to identify where users drop off and why.

5. Prioritize and test hypotheses

Rank potential causes by impact and feasibility, then design experiments or A/B tests to validate the most promising ones and inform next steps.

Key Points to Mention

  • Localization: language, content, cultural relevance, and pricing in local currency
  • Technical performance: app load time, crash rates, and compatibility with local devices and networks
  • Competitive landscape: presence of local competitors and their features/pricing
  • Marketing and acquisition channels: effectiveness of local campaigns and app store optimization
  • User behavior metrics: retention, engagement, and conversion rates compared to other markets
  • Regulatory and payment factors: compliance with local laws and availability of preferred payment methods

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.