Imported from majiayu000/claude-skill-registry (skills/data/tradeoff-evaluation/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry --skill tradeoff-evaluation. Copyright stays with the author.

Trade-off Evaluation

Purpose

Make informed decisions when improving one metric harms another, or when A/B test results are mixed. Avoids binary thinking (ship vs. rollback) by exploring nuanced approaches that preserve gains while addressing losses.

When to Use This Skill

Activate automatically when:

• A/B test shows some metrics up, others down
• Feature launched with mixed performance
• Optimizing for one goal reduces another
• Stakeholders disagree on ship/no-ship decision
• Redesign shifted user behavior unexpectedly
• tradeoff-decision workflow evaluates mixed results
• metrics-definition workflow identifies counter-metrics
• Need to choose between competing priorities

When NOT to use:

• All metrics clearly positive (ship it)
• All metrics clearly negative (don't ship)
• Harm is minor and acceptable (make call quickly)
• Decision is time-sensitive and stakes are low

Core Principles

1. Default to "It Depends"

Avoid binary yes/no thinking:

• ❌ "Should we ship this? Yes or No"
• ✓ "Should we ship this to everyone, some segments, or iterate first?"

Segmentation thinking:

• New users vs. established users
• Power users vs. casual users
• Free tier vs. paid tier
• Geographic segments
• Use case segments

2. Long-Term Strategic Value > Short-Term Gains

When in doubt, choose the option that:

• Aligns with company strategic positioning
• Builds sustainable competitive advantages
• Strengthens core value proposition
• Supports mission beyond just revenue

Example (Snapchat):

• Short-term: Time on site ↑ (good for ads)
• Long-term: Messages sent ↓, stories shared ↓ (breaks social flywheel)
• Decision: Long-term strategic value (social/camera app) > short-term time-on-site gains

3. Mitigation Before Rollback

Avoid immediate reversions:

• Understand WHY metrics moved
• Identify which specific changes caused issues
• Explore partial fixes that preserve gains
• Test iterations before full rollback

Staged problem-solving:

1. Analyze: Which parts of change are beneficial vs. harmful?
2. Hypothesize: What modifications could fix the harm?
3. Test: Try mitigation strategies
4. Only then: Consider full rollback if mitigation fails

Decision Frameworks

Framework 1: User Lifecycle Segmentation

Principle: Different metrics matter at different user stages

Example: Facebook News Feed

Question: Show ad after every 10th post OR show "People You May Know" widget?

Answer: It depends on user lifecycle stage

New Users (< 50 friends):

• Priority: Build friend network for long-term retention
• Decision: Show "People You May Know"
• Rationale: Need friends to find value; too early to monetize
• Trade-off: Sacrifice short-term ad revenue for long-term user retention

Established Users (> 2000 friends):

• Priority: Monetization (already retained)
• Decision: Show ads
• Rationale: Friend suggestions have diminishing returns; time to generate revenue
• Trade-off: Already has network, ad value exceeds friend value

Gradient Approach:

• 0-50 friends: 100% PYMK
• 50-500 friends: 70% PYMK, 30% ads
• 500-2000 friends: 30% PYMK, 70% ads
• 2000+ friends: 10% PYMK, 90% ads

Framework 2: Strategic Positioning Alignment

Principle: Choose option that reinforces company's core identity

Example: Snapchat Redesign

Situation:

• Time on site: ↑ 15% (good for ads)
• Messages sent: ↓ 12% (bad for social engagement)
• Stories shared: ↓ 8% (bad for social engagement)

Strategic Question: Is Snapchat a social/camera app or content consumption app?

Analysis:

• Content consumption positioning:

  • Competes with TikTok, YouTube, Instagram Reels
  • Market saturated with strong players
  • Not Snapchat's core competency
  • Time-on-site gains are short-term

• Social/camera app positioning:

  • Unique value proposition in market
  • Peer-to-peer engagement drives retention flywheel
  • Messages/stories create notifications → app opens
  • Sustainable long-term competitive advantage

Decision: Prioritize messaging/stories over time-on-site

• Reason: Aligns with core strategic positioning
• Trade-off: Accept lower time-on-site to strengthen social flywheel
• Mitigation: Explore ways to integrate creator content without harming peer-to-peer

Framework 3: Flywheel Impact Analysis

Principle: Preserve metrics that drive virtuous cycles

Identify flywheel components:

1. What user behavior creates notifications/triggers?
2. What brings users back to the product?
3. What creates value for other users (network effects)?
4. What reinforces habit formation?

Example: Snapchat Social Flywheel

Stories shared → Friends receive notifications → Friends open app → 
Friends send messages → Original user gets notification → Opens app → 
Shares more stories → [LOOP]

Breaking the flywheel:

• Reduced story sharing = Fewer notifications sent
• Fewer notifications = Less app opening
• Less opening = Less engagement
• Less engagement = Reduced retention

Preservation priority:

• Stories shared: CRITICAL (triggers loop)
• Messages sent: CRITICAL (reciprocal engagement)
• Time on content: NICE-TO-HAVE (doesn't drive loop)

Decision: Protect flywheel metrics even at cost of other metrics

Framework 4: Revenue Impact (Short vs. Long-Term)

Principle: Evaluate both immediate and future revenue implications

Short-term revenue metrics:

• Immediate ad impressions
• This quarter's subscription conversions
• Current transaction volume

Long-term revenue metrics:

• User retention (LTV)
• Network effects (platform value)
• Brand strength
• Competitive positioning

Example: Facebook Dating

Scenario: Dating feature increases MAU but reduces News Feed engagement

Short-term analysis:

• News Feed ads: Slightly down (users spending time in dating)
• Immediate revenue: Minor negative

Long-term analysis:

• Overall platform value: Up (new use case)
• User retention: Up (more reasons to use Facebook)
• Competitive positioning: Better (catch up to Tinder/Bumble)
• LTV: Up (stronger platform lock-in)

Decision: Ship dating feature despite News Feed cannibalization

• Long-term strategic value > short-term ad revenue
• Counter-metric (News Feed engagement) acceptable trade-off

Mitigation Strategies Catalog

Strategy 1: Segmented Rollout

When to use: Impact varies by user type

Approach:

• Ship to segments where metrics are net positive
• Don't ship to segments where metrics are net negative
• Iterate on problematic segments separately

Example:

• Feature works for power users, not casual users
• Ship to power users immediately
• Redesign for casual users based on learnings

Strategy 2: Targeted Modifications

When to use: Specific element causing harm

Approach:

• Analyze which specific change caused negative impact
• Keep beneficial parts, modify harmful parts
• Test modified version

Example (Snapchat):

• Problem: Creator content tab reducing peer engagement
• Keep: Creator content (time-on-site benefit)
• Modify: Re-integrate friend stories in main tab
• Add: Prompts to message friends after viewing content

Strategy 3: Compensation Mechanisms

When to use: Trade-off is necessary but painful

Approach:

• Accept metric decline in one area
• Boost complementary features to compensate
• Create new paths to value

Example:

• Feature reduces click rate but improves user satisfaction
• Accept: Lower clicks (engagement metric)
• Compensate: Better retention (satisfaction metric)
• Net result: Lower short-term engagement, higher long-term LTV

Strategy 4: Gradual Rollout with Monitoring

When to use: Uncertain about long-term effects

Approach:

• 10% rollout → Monitor for 2 weeks
• 25% rollout → Monitor for 2 weeks
• 50% rollout → Monitor for 1 month
• Maintain holdback group indefinitely

Watch for:

• Delayed negative effects (retention drops after initial boost)
• Segment-specific issues that emerge at scale
• Interaction effects with other features

Strategy 5: A/B/C Testing

When to use: Multiple approaches possible

Approach:

• Test original (A) vs. full change (B) vs. modified version (C)
• Evaluate trade-offs across all three
• Choose best balance

Example:

• A: Current experience
• B: New design with creator content prominent
• C: New design with balanced creator/peer content
• Evaluate: Time-on-site, messages, stories across all three

Workflow Steps

1. Identify the Conflict

Ask:

• Which metrics went up?
• Which metrics went down?
• What's the magnitude of each change?
• Are changes statistically significant?

Document the trade-off clearly.

2. Apply Strategic Lens

Ask:

• Which metric aligns with company strategic positioning?
• Which metric drives long-term competitive advantage?
• Which metric supports the core value proposition?
• Which metric feeds important flywheels?

Rank metrics by strategic importance.

3. Segment the Analysis

Ask:

• Does trade-off vary by user segment?
• Are some segments net positive, others net negative?
• Which segments are most valuable long-term?

Analyze trade-offs by segment.

4. Identify Mitigation Options

Brainstorm:

• Can we modify to reduce harm?
• Can we segment the rollout?
• Can we compensate with other features?
• What's the best of both worlds approach?

List 3-5 mitigation strategies.

5. Make Recommendation

Choose:

• Ship fully: Net positive across segments, aligned with strategy
• Ship to segments: Positive for some users, not others
• Iterate first: Promising but needs modification
• Rollback: Net negative, no clear mitigation path

Document rationale and risks.

Common Mistakes

Anti-Rationalization Blocks

Success Criteria

Trade-off evaluation succeeds when:

• Conflicting metrics clearly identified and quantified
• Strategic importance of each metric assessed
• User segments analyzed for differential impact
• 3-5 mitigation strategies brainstormed
• Decision framework applied (lifecycle, strategic, flywheel, revenue)
• Recommendation made with clear rationale
• Risks of chosen path documented
• Plan for monitoring outcomes established

Real-World Examples

Example 1: Snapchat Redesign - Time vs. Social

Situation:

• Redesign launched: Separate social from media
• Time on site: ↑ 15% (watching creator content)
• Messages sent: ↓ 12%
• Stories shared: ↓ 8%

Initial question: Net positive or net negative?

Strategic analysis:

• Content app positioning: Time-on-site gains matter

  • But: Saturated market (TikTok, YouTube, Instagram)
  • Snapchat not core competency

• Camera/social app positioning: Messaging/stories matter

  • Unique value proposition
  • Flywheel: Stories → Notifications → Messages → Retention
  • Sustainable competitive advantage

Decision: Net negative despite time-on-site gains

Rationale:

• Social flywheel breaking outweighs time-on-site benefit
• Long-term retention risk from reduced peer engagement
• Strategic positioning as camera/social app is core identity

Mitigation approach (not immediate rollback):

1. Analyze traffic distribution: Where are users spending time?
2. Test modifications: Can we show friend stories in creator tab?
3. Add prompts: Encourage messaging after content viewing
4. Monitor: Track if mitigation restores messaging/stories

Only if mitigation fails: Consider full rollback

Example 2: Facebook Dating Cannibalization

Situation:

• Dating feature launched
• Weekly active dating users: ↑ (new feature adoption)
• News Feed engagement: ↓ 2% (time shifting to dating)
• Overall Facebook MAU: → (no change)

Trade-off: Dating adoption vs. News Feed engagement

Strategic analysis:

Short-term:

• Ad impressions: Slightly down (less News Feed time)
• Revenue: Minor negative

Long-term:

• Platform value: Up (new use case, competitive with dating apps)
• User retention: Up (more reasons to use Facebook)
• Strategic positioning: Better (full-service social platform)
• LTV: Up (dating users more engaged overall)

Segmentation analysis:

• New users: Dating is additional value (no cannibalization)
• Established users: Some shift from Feed to Dating (acceptable trade-off)
• Dating-age demographic: Much higher engagement overall

Decision: Ship dating feature widely

Rationale:

• Long-term strategic value > short-term ad revenue
• Counter-metric (Feed engagement) within acceptable range
• Dating-age users show net positive engagement
• Competitive necessity (catch up to Tinder/Bumble)

Mitigation:

• Show dating profile prompts in News Feed (drive discovery)
• Dating → Feed suggestions (create cross-product engagement)
• Monitor for further cannibalization (have thresholds for concern)

Example 3: Facebook Feed - Ads vs. Friend Building

Situation:

• Feed space allocation decision
• Option A: Show ad after every 10th post
• Option B: Show "People You May Know" widget

Trade-off: Immediate revenue vs. long-term network building

User lifecycle segmentation approach:

Implementation:

• Dynamic per-user allocation based on friend count
• Maximize LTV by balancing retention (friends) and revenue (ads)

Monitoring:

• New user retention rates by PYMK exposure
• Revenue per user by PYMK/ad mix
• Friend connection rates by allocation

Example 4: Uber Driver Quality vs. Availability

Situation:

• Strict driver quality standards proposed (4.8+ rating required)
• Driver quality: ↑ (removing low-rated drivers)
• Driver availability: ↓ (fewer drivers in network)
• Wait times: ↑ (supply reduction)

Trade-off: Quality experience vs. availability

Strategic analysis:

Option A: Strict standards (4.8+ only)

• Pro: Premium experience, higher rider satisfaction
• Con: Longer wait times, reduced coverage
• Risk: Riders switch to Lyft due to availability

Option B: Moderate standards (4.5+ rating)

• Pro: Good quality, maintained availability
• Con: Some poor experiences persist
• Balance: Acceptable quality + good availability

Option C: Segmented approach

• Premium tier: 4.8+ only (UberBlack)
• Standard tier: 4.5+ (UberX)
• Pro: Choice for customers
• Complex: More tiers to manage

Decision: Option B with active quality improvement

Rationale:

• 4.5-4.8 drivers often improvable with training
• Availability is competitive necessity
• Quality can improve through coaching, not just removal

Mitigation:

• Driver quality program: Training for 4.5-4.8 drivers
• Real-time feedback: Help drivers improve
• Graduated warnings before deactivation
• Monitor: Track if 4.5-4.8 drivers improve to 4.8+

Example 5: YouTube Homepage Redesign

Situation:

• New homepage emphasizes algorithm recommendations
• Video clicks from homepage: ↑ 20%
• New genre discovery: ↑ 15%
• Time spent per video: ↓ 10% (more browsing, less watching)

Trade-off: Discovery/clicks vs. watch time depth

Strategic analysis:

Primary North Star: Total watch time (ad impressions)

• More clicks but less time per video
• Net impact: Need to calculate (clicks × time per video)

Mission alignment: "Organize world's information" / "Broaden interests"

• New genre discovery: ✓ Supports mission
• Suggests users finding valuable content

Flywheel impact:

• More discovery → More interests → More returning
• Could be positive for long-term retention

Decision: Keep redesign but monitor retention

Rationale:

• Discovery aligns with mission
• May improve long-term engagement (more interests = more reasons to return)
• Time-per-video could stabilize as users settle on new content

Mitigation:

• Optimize recommendation quality (prevent spam clicks)
• Balance familiar vs. new content
• Monitor: 30-day retention and long-term watch time trends

Success criteria:

• 30-day retention ↑ (discovery driving habit)
• Total watch time stable or ↑ (volume compensates)
• User satisfaction surveys positive

Related Skills

• north-star-alignment: Identifies which metric aligns with company strategy
• funnel-metric-mapping: Identifies where in funnel the trade-off occurs
• proxy-metric-selection: Creates counter-metrics to detect trade-offs
• root-cause-diagnosis: Diagnoses why metrics moved before deciding
• tradeoff-decision (workflow): Orchestrates systematic trade-off evaluation
• metrics-definition (workflow): Uses trade-off evaluation to identify counter-metrics

Integration Points

Called by workflows:

• metrics-definition - Step 4: Identify counter-metrics and trade-offs
• tradeoff-decision - Steps 2-3: Evaluate mixed A/B test results
• dashboard-design - Step 4: Include counter-metrics to detect trade-offs
• goal-setting - Step 3: Evaluate cost of aggressive vs. conservative targets

May call:

• north-star-alignment to assess strategic importance
• funnel-metric-mapping to understand where trade-off occurs
• root-cause-diagnosis to understand why metrics moved