Framework
You've built a feature. Shipped it. Used resources. Now decide: Keep investing or kill it?
The trap: "We've already spent $500K. We should get ROI."
But sunk cost is sunk. Future invest decision should be based on future value, not past cost.
Actionable Steps
1. Separate Past and Future
- Past: "We spent $500K building this"
- Future: "Should we spend $50K/quarter maintaining this?"
Decisions should only consider future value, not past cost.
2. Compare to Alternatives
Would $50K/quarter spent on feature X be better spent on feature Y?
If yes, kill X and do Y.
3. Recognize Sunk Cost Fallacy
It feels "wasteful" to kill something you invested in. That feeling is sunk cost fallacy. Ignore it. Make the forward-looking decision.
Key Takeaways
- Past cost is irrelevant to future decisions. Only future value matters.
- Products and features die. That's normal. Legacy code and old features get replaced. Don't let sunk cost prevent good decisions.
- Recognize the sunk cost feeling, then override it. Bias awareness is the first step.
The Sunk Cost Trap in Product Management
You have built a feature called "Collaboration AI." Here's the story:
Q1-Q2: 4 engineers, 6 months, $400K spent. It's now live. Q3: Usage is 2% of users. Engagement metrics flat. Support burden high (AI sometimes breaks). Q4: Question: Keep maintaining? Kill? Pivot?
The sunk cost trap logic:
- "We spent $400K. If we kill this, that's $400K wasted."
- "If we keep it for 2 more years, maybe it pays off."
- "We should try harder to make it work."
- Decision: Keep investing another $50K/quarter hoping it catches on.
The forward-looking logic:
- "Past $400K is gone either way. Can't get it back."
- "Question: Is $50K/quarter on this feature better than $50K/quarter on something else?"
- "Usage is 2%. Is there a path to 10%+?"
- "If not, kill it. Use the $50K on something with higher potential."
The difference: $400K sunk cost vs. $50K forward cost.
The 3-Option Decision Framework: Kill, Pivot, or Persevere
When a feature/product isn't hitting targets, you have 3 paths:
Option 1: Kill (Stop All Investment)
When to kill:
- Usage is 1-3%+ with no upward trend
- Support burden > value delivered
- Alternative solutions exist (built-in or third-party)
- Future investment unlikely to change trajectory
- Capital better deployed elsewhere
Example: "Collaboration AI" isn't driving adoption. Kill it.
Execution:
- Timeline: 3-month wind-down (don't abandon users)
- Communication: "We're focusing on core features. Here's the transition path."
- Migration: Help users export data / move to alternatives
- Lessons: "We tried AI-first collaboration. Turned out users prefer explicit actions. Lesson noted."
Costs: $20K migration + PR risk. Total cost to kill: $20K. Benefit: $50K/quarter freed up for other priorities × 8 quarters/year = $400K/year saved.
Net: $400K saved vs. $20K cost. Clear win.
Option 2: Pivot (Change Direction Without Killing)
When to pivot:
- Core idea has merit, but execution/positioning is wrong
- 5-15% usage (signal of demand, but small)
- Customer feedback suggests different use case than intended
- Small investment could unlock bigger opportunity
Example: "Collaboration AI" isn't used for real-time collab. But power users use it for "summarize meeting notes."
Pivot approach:
- "We're repositioning this from collaboration to note-taking."
- Reduce scope: Kill real-time collab features. Focus on summary generation.
- Re-launch with new narrative.
- Investment: $30K to redesign. Not $400K, but targeted spend to test new direction.
Results (after pivot):
- Usage increases from 2% to 8%
- Still not blockbuster, but viable
- Decision: "Keep as secondary feature. Revisit in 12 months."
Option 3: Persevere (Double Down)
When to persevere:
- Usage is 15%+, trending up
- Customer feedback is overwhelmingly positive
- You're seeing viral adoption (not marketing-driven)
- Market conditions favor this feature
- Competitors are doing it worse
Example: Not "Collaboration AI" (only 2% usage). But imagine usage was 20% and growing 10%/month.
Persevere approach:
- "This is working. Invest more. Make it best-in-class."
- $100K/quarter (2x current spend) to accelerate growth
- Expand roadmap: integrations, performance, advanced features
Expected outcome:
- Usage 20%+ → 40%+
- Becomes strategic differentiator
- 12+ month payback
The Kill/Pivot/Persevere Decision Matrix
Use this to decide:
| Factor | Kill | Pivot | Persevere |
|---|---|---|---|
| Usage | <2% | 5-15% | 15%+ |
| Trend | Flat or declining | Flat but signal present | Growing |
| Customer feedback | "Not useful" | "Interesting but needs..." | "Love this" |
| Future potential | None obvious | Possible with direction change | High |
| Support burden | High | Medium | Manageable |
| Alternative exists? | Yes, better one | Partial | No |
| Market dynamics | Shifting away | Neutral | Growing market |
Decision rule:
- If 3+ factors point to Kill → Kill
- If 3+ factors point to Pivot → Pivot
- If 3+ factors point to Persevere → Persevere
Real-World Case Study: Kill, Pivot, or Persevere in Action
Company: Productivity SaaS (50K users)
Q2 Review: Three Features at Crossroads
Feature A: "Smart Scheduling"
- Usage: 1.2%
- Customer feedback: "Not accurate for my calendar"
- Support tickets: 200+ per month about broken scheduling
- Market: Calendly, Fantastical already dominate
- Decision matrix: 5/7 factors = Kill
- Action: KILL
- Cost to kill: $10K (migration, communication)
- Savings: $60K/quarter maintenance + engineering
- Lesson: "Scheduling is not our core. Users already have solutions. We don't need to be better."
Feature B: "Time Blocking Templates"
- Usage: 7%
- Customer feedback: "Helpful, but needs more templates"
- Support tickets: 20/month (manageable)
- Market: Growing interest in time-blocking
- Decision matrix: 4/7 factors = Pivot
- Action: PIVOT
- New direction: "Make it template library + sharing." Not about scheduling logic.
- Re-launch as "Workflow templates" (generic use case)
- Investment: $30K to redesign
- Expected outcome: Usage 7% → 15%+
Feature C: "AI Meeting Notes"
- Usage: 18%, growing 12% MoM
- Customer feedback: "Saves hours each week"
- Support tickets: 10/month
- Market: Recording apps (Otter, Fireflies) growing but niche
- Decision matrix: 6/7 factors = Persevere
- Action: PERSEVERE
- Investment: $100K/quarter to expand
- Roadmap: Integrations with Slack, email, CRM
- Expected outcome: Usage 18% → 40%+ within 12 months
Financial outcome (Q2-Q4):
- Kill Smart Scheduling: -$10K spend, +$180K saved = $170K benefit
- Pivot Time Blocking: -$30K spend (relaunch), +$10K saved (lower maintenance now that focused) = -$20K net
- Persevere Meeting Notes: +$100K/quarter spend = -$300K cost, but expected $500K ARR in 12 months
- Net: $170K - $20K - $300K = -$150K for Q2-Q4, but foundation for +$500K ARR by end of year
Anti-Pattern: "Optimism Bias" (Persevere When You Should Kill)
The Problem:
- Feature launched 18 months ago with hopes of 20% adoption
- Currently at 3% usage
- Team says: "Give it one more quarter. It might take off."
- One more quarter, still 3%
- Two years later, still maintaining dead feature
The Fix:
- Set success criteria upfront: "If usage isn't 10% by Q3, we kill it."
- Enforce the criteria
- Bias check: "Is this hope or data?"
Prodinja Connection
Key Takeaways (Expanded)
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Sunk cost is a cognitive bias. $400K spent is gone. Future decisions should only consider future value.
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Use the Kill/Pivot/Persevere matrix to decide. Don't rely on intuition alone. Let data guide you.
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Set success criteria upfront. "If usage isn't 10% by Q3, we kill this." Then stick to it.
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Killing features is normal and healthy. Great products kill 30-50% of features over time. That's evolution, not failure.
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Communication matters for kills. "We're focusing on core features" is better than silence. Users need migration path.