Product Analytics Questions

Product analytics questions test your ability to think like a data-driven product manager. They require combining business intuition with analytical skills.

Question Type 1: Metric Investigation

Pattern: "X metric changed by Y%. Diagnose."

Example: "Daily active users dropped 10% last month. What would you investigate?"

Strong answer structure:

1. Clarify the metric

   • How is DAU defined?
   • Is this vs last month or vs same month last year?
   • Is 10% outside normal variance?

2. Check data quality

   • Logging changes?
   • Definition changes?
   • Data pipeline issues?

3. Segment the drop

   By platform: iOS -5%, Android -8%, Web -25% ← Focus here
   By user type: New -20%, Returning -5% ← Also investigate
   By geo: Proportional across regions
   

4. Form hypotheses

   • Web-specific: Site redesign? SEO drop? Performance issue?
   • New users: Acquisition channel change? Onboarding broken?

5. Recommend data to pull

   • Funnel conversion by platform
   • Traffic sources over time
   • Session duration and bounce rates

Question Type 2: Feature Evaluation

Pattern: "How would you evaluate the success of [feature]?"

Example: "Netflix just launched a 'Random Play' button. How would you measure success?"

Framework:

Metric Type	Example	Why
Usage	% users who click Random Play	Adoption rate
Engagement	Watch completion rate after Random	Content satisfaction
Retention	Return rate of Random users vs control	Long-term value
Cannibalization	Search/browse usage change	Unintended effects
Segment	New vs power users adoption	Who benefits

Complete answer: "I'd measure Random Play success across multiple dimensions:

Primary: Watch time from Random Play sessions (shows value delivered)

Secondary:

• Adoption rate: What % of users try it?
• Completion rate: Do they finish what they start?
• Repeat usage: Do they use it again?

Guardrails:

• Overall watch time (doesn't decrease)
• Content diversity viewed (algorithm isn't pigeonholing)
• Search usage (not replacing intentional discovery)

Segmentation:

• New users (helps discovery) vs veterans (already know what they want)
• Content type (movies vs TV shows)
• Time of day (decision fatigue in evening?)"

Question Type 3: Funnel Analysis

Pattern: "Users are dropping off at [stage]. Why?"

Example: "Only 30% of users who add to cart complete purchase. What's happening?"

Investigation approach:

Funnel:
View product: 100%
Add to cart: 40%
Begin checkout: 32% (-8% absolute, -20% of cart)
Enter payment: 20% (-12% absolute, -37.5% of checkout) ← Big drop
Complete: 12% (-8% absolute, -40% of payment) ← Another big drop

Hypotheses by stage:

Drop Point	Possible Causes	Data to Check
Cart → Checkout	Shipping cost surprise, no guest checkout	Exit survey, competitor pricing
Checkout → Payment	Trust issues, limited payment options	Payment methods attempted vs available
Payment → Complete	Payment failures, timeout	Error logs, processor data

Question Type 4: Cohort Analysis

Pattern: "Analyze retention over time."

Example: "Our 30-day retention dropped from 40% to 35%. What's the analysis approach?"

Cohort retention table:
         Day 1   Day 7   Day 30
Jan cohort  80%    50%     40%
Feb cohort  80%    48%     38%
Mar cohort  78%    45%     35% ← Recent drop

Pattern: Day 1 retention stable, but Day 7 and Day 30 falling
Insight: Users activate but don't form habits

Follow-up analysis:

• What do retained users do that churned users don't?
• When do users churn (day 3? day 10?)?
• Is there a product change that correlates with the trend?

Interview Tip: Think Out Loud

For product analytics questions, verbalize your thinking:

"First I'd want to understand... Then I'd look at... My hypothesis is... I'd test this by... If that's true, I'd recommend..."

This shows:

• Structured problem solving
• Business intuition
• Data-driven thinking
• Actionable recommendations

The goal isn't to have the "right" answer - it's to demonstrate a rigorous analytical process. :::