All examples for Hypothesis Prioritization Matrix

E-commerce Conversion Rate Drop

Baseline scenario analyzing a 5% conversion rate drop over one week for a generic web product. Demonstrates standard MECE hypothesis generation across Technical, Product, Platform, External, and Data Quality categories.

Input

What you provide to the skill

Our conversion rate dropped 5% over the last week. What could be causing this?

Output

What the skill generates for you

SCENARIO: Conversion rate dropped 5% over the last week

═══════════════════════════════════════════════════════════
GENERATED HYPOTHESES (18 total)
═══════════════════════════════════════════════════════════

CATEGORY: TECHNICAL
─────────────────────────────────────────────────────────

  1. Payment gateway experiencing errors or increased latency
    Impact: 5/5 | Likelihood: 4/5 | Ease: 5/5 | PRIORITY: 100
    Rationale: Payment failures directly block conversions; check gateway logs instantly

  2. Checkout page experiencing JavaScript errors
    Impact: 5/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 75
    Rationale: Broken checkout prevents completion; check error monitoring dashboard

  3. Page load time increased on conversion funnel pages
    Impact: 4/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 60
    Rationale: Slow pages drive abandonment; check performance metrics

  4. Form validation errors preventing submission
    Impact: 4/5 | Likelihood: 3/5 | Ease: 4/5 | PRIORITY: 48
    Rationale: UX friction at critical moment; review form error logs

CATEGORY: PRODUCT
─────────────────────────────────────────────────────────

  1. Recent pricing change or promotion ended
    Impact: 5/5 | Likelihood: 4/5 | Ease: 5/5 | PRIORITY: 100
    Rationale: Price sensitivity is high; check product/pricing changelog

  2. Checkout flow redesign or added friction
    Impact: 5/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 75
    Rationale: UI changes impact conversion directly; review deployments

  3. Trust signals removed (badges, testimonials, guarantees)
    Impact: 4/5 | Likelihood: 2/5 | Ease: 4/5 | PRIORITY: 32
    Rationale: Reduces confidence at purchase moment; check recent changes

  4. New required fields or account creation requirement
    Impact: 4/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 60
    Rationale: Increased friction reduces completion; review form changes

CATEGORY: PLATFORM/SEGMENTATION
─────────────────────────────────────────────────────────

  1. Mobile vs Desktop conversion divergence
    Impact: 5/5 | Likelihood: 4/5 | Ease: 5/5 | PRIORITY: 100
    Rationale: Platform-specific issues isolate root cause; segment immediately

  2. Browser-specific bug (Safari, Chrome, etc.)
    Impact: 4/5 | Likelihood: 3/5 | Ease: 4/5 | PRIORITY: 48
    Rationale: Compatibility issues affect subset; check by browser

  3. Geographic segment experiencing issues
    Impact: 4/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 60
    Rationale: Regional payment methods or shipping issues; segment by country

CATEGORY: EXTERNAL
─────────────────────────────────────────────────────────

  1. Competitor launched aggressive promotion
    Impact: 3/5 | Likelihood: 3/5 | Ease: 3/5 | PRIORITY: 27
    Rationale: Market dynamics shift demand; quick competitor research

  2. Negative press or social media sentiment shift
    Impact: 3/5 | Likelihood: 2/5 | Ease: 3/5 | PRIORITY: 18
    Rationale: Brand trust issues; social listening check

  3. Traffic source quality degraded (bot traffic, low-intent users)
    Impact: 4/5 | Likelihood: 3/5 | Ease: 4/5 | PRIORITY: 48
    Rationale: Same visitors but different intent; segment by source

CATEGORY: USER BEHAVIOR
─────────────────────────────────────────────────────────

  1. User cohort shift (fewer high-intent visitors)
    Impact: 4/5 | Likelihood: 3/5 | Ease: 3/5 | PRIORITY: 36
    Rationale: Audience composition changed; cohort analysis needed

  2. Seasonal pattern or holiday effect
    Impact: 3/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 45
    Rationale: Calendar effects on purchase intent; compare to historical

CATEGORY: DATA QUALITY
─────────────────────────────────────────────────────────

  1. Conversion tracking pixel/code broke
    Impact: 5/5 | Likelihood: 3/5 | Ease: 5/5 | PRIORITY: 75
    Rationale: False alarm - conversions happening but not tracked; validate raw orders

  2. Conversion definition changed in analytics
    Impact: 5/5 | Likelihood: 2/5 | Ease: 5/5 | PRIORITY: 50
    Rationale: Metric calculation modified; check analytics configuration

═══════════════════════════════════════════════════════════
PRIORITIZATION MATRIX (Impact × Likelihood)
═══════════════════════════════════════════════════════════

                HIGH IMPACT

                    ↑

     ─────────────────────────────────

     │                    │           │

HIGH │   #1 (Payment)     │  #2, #17  │

LIKELI-│ #5 (Pricing) │ #6, #18 │
HOOD │ #9 (Platform) │ (Medium) │
│ CHECK FIRST │ │
├────────────────────┼───────────┤
│ #3 (Load Time) │ #7, #13 │
│ #8 (New Fields) │ #15 │
LOW │ #11 (Geo) │ (Lower │
│ #14 (Traffic) │ Priority)│
─────────────────────────────────
→
HIGH LIKELIHOOD

═══════════════════════════════════════════════════════════
INVESTIGATION SEQUENCE
═══════════════════════════════════════════════════════════

⚡ WAVE 1 (5-7 minutes total)
─────────────────────────────────────────────────────────

→ #9: Platform-specific issue
Action: Segment conversion rate by device (Mobile vs Desktop) for past 14 days
Time: 30 seconds
Info gain: If drop isolated to one platform, narrows scope by 50%

→ #17: Conversion tracking validation
Action: Compare tracked conversions vs raw order/transaction count
Time: 1 minute
Info gain: Rules out false alarm - confirms real vs measurement issue

→ #5: Pricing or promotion change
Action: Check product changelog and pricing history for past 7 days
Time: 2 minutes
Info gain: Temporal correlation with known business change

→ #1: Payment gateway errors
Action: Check payment gateway dashboard for success rate and error codes
Time: 1 minute
Info gain: Direct conversion blocker with clear yes/no answer

→ #6: Checkout flow changes
Action: Review production deployments to checkout/payment pages
Time: 2 minutes
Info gain: Identifies UI/UX changes that correlate temporally

⏱️ WAVE 2 (10-15 minutes)
─────────────────────────────────────────────────────────

→ #2: JavaScript errors - Check error monitoring (Sentry, Rollbar) for checkout pages
→ #8: New friction points - Review form field changes and funnel drop-off by step
→ #11: Geographic segmentation - Compare conversion by top countries/regions
→ #14: Traffic source quality - Segment conversion by source/medium/campaign
→ #3: Page performance - Check Core Web Vitals and load time metrics

🔍 WAVE 3 (If needed)
─────────────────────────────────────────────────────────

→ #4, #7, #10, #12, #13, #15, #16, #18 - Lower priority hypotheses

═══════════════════════════════════════════════════════════
DATA REQUESTS (Priority Order)
═══════════════════════════════════════════════════════════

Request 1: Platform Segmentation
“Show me conversion rate by device type (Mobile, Desktop, Tablet) for past 14 days vs previous 14 days”
Time: 30 seconds | Rules in/out: Platform-specific bug or UX issue

Request 2: Data Quality Validation
“Compare tracked conversion events vs actual orders/transactions from database for past 7 days”
Time: 1 minute | Rules in/out: Tracking failure (false alarm)

Request 3: Pricing/Promotion Timeline
“Show all pricing changes, promotions started/ended, or discount modifications in past 7 days”
Time: 2 minutes | Rules in/out: Business-driven conversion impact

Request 4: Payment Gateway Health
“What is payment success rate, error codes, and latency (p95) for past 7 days vs previous 7 days?”
Time: 1 minute | Rules in/out: Technical blocker at payment step

Request 5: Deployment History
“Show all production deployments affecting checkout, cart, or payment pages in past 7 days”
Time: 2 minutes | Rules in/out: Product/UI change correlation

Request 6: Funnel Drop-off Analysis
“Show conversion funnel completion rates by step (cart → checkout → payment → confirmation) for past 14 days”
Time: 3 minutes | Rules in/out: Identifies which specific step degraded

Request 7: Traffic Source Segmentation
“Show conversion rate by traffic source/medium for past 14 days vs previous 14 days”
Time: 2 minutes | Rules in/out: Traffic quality degradation

═══════════════════════════════════════════════════════════
INTERVIEW TALKING POINTS
═══════════════════════════════════════════════════════════

“I’ve generated 18 hypotheses across 5 MECE categories: Technical, Product,
Platform/Segmentation, External, and Data Quality. To investigate efficiently,
I’m using a three-dimensional prioritization framework scoring each hypothesis
on Impact, Likelihood, and Ease-to-check.

My Wave 1 approach (5-7 minutes total) focuses on maximum information gain:

• Platform segmentation (30s) - If the entire drop is on mobile, I’ve eliminated
all desktop-related hypotheses and can focus investigation

• Data validation (1min) - First rule out a false alarm by confirming tracked
conversions match actual orders

• Pricing/promotion check (2min) - Known business changes are fastest to verify
and often highest impact

• Payment gateway health (1min) - Direct conversion blocker with instant
dashboard visibility

• Deployment timeline (2min) - Temporal correlation between code changes and
metric shifts is highly diagnostic

These five checks maximize information gain per minute invested. For example,
if platform segmentation shows mobile conversion dropped 10% while desktop is
flat, I’ve narrowed the problem space by 50% in 30 seconds and can focus all
subsequent investigation on mobile-specific issues.

The key principle is sequential investigation with logical dependencies—each
check informs which hypothesis to explore next, rather than exploring all 18
randomly.”

View all examples for Hypothesis Prioritization Matrix

About This Skill

Generate structured hypothesis prioritization matrices for PM interview root cause analysis with MECE categorization, scoring, and investigation sequences.

View Skill Details

More Examples

Mobile App Revenue Drop (Android-Specific)

Complex scenario with constraints: 25% revenue drop in 2 days, Android-specific, no recent deployments. Shows how the skill adapts hypotheses to platform-specific issues and considers non-deployment changes like A/B tests.

B2B SaaS Sign-ups Drop (Incomplete Information)

Edge case with vague input: 'dropped significantly' without specific numbers. Demonstrates how the skill handles incomplete information by stating explicit assumptions, providing clarifying questions, and still delivering a complete analysis.

View all examples