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Chapter 7 of 9

Chapter 7: Post-Launch - Pivot, Persevere, or Kill

Analyzing failure, the decision framework, types of pivots.

Playbook Chapters
What You'll Learn By the end of this chapter, you'll know why most MVPs fail, master the Persevere/Pivot/Kill decision framework, and understand the four types of pivots that can save your startup.

The Moment of Truth

You've launched. Users have tried your product. Data is coming in. Now comes the hardest part of entrepreneurship: honestly interpreting the results.

This is where most founders fail—not because they can't build, but because they can't let go. They fall in love with their solution and ignore the signals telling them to change course.

The Core Insight

The goal isn't to be right about your original hypothesis. The goal is to find product-market fit as fast as possible—even if that means admitting your first idea was wrong.

Why MVPs Fail: The Real Reasons

Understanding why MVPs fail is crucial for interpreting your own data. The top reasons are rarely technical:

42%

No Market Need

The product works, but no one cares. This indicates failure of assumption mapping and customer discovery.

29%

Ran Out of Cash

Too long building, not enough runway to iterate. This indicates failure of prioritization and scope.

23%

Wrong Team

Execution failure or ignored feedback. Sticking to the vision despite negative data.

The Pattern

Notice that 42% of failures are from building the wrong thing—not building it wrong. Technical execution failures are far less common than market failures. This is why validation before building matters so much.

Bug #1: Ignoring the Data

The most dangerous bias is confirmation bias. Founders see what they want to see, interpret neutral signals as positive, and dismiss negative feedback as "edge cases."

The Bug

"The numbers are low, but people love the concept. We just need more features."

This is the classic denial pattern. If retention is zero, adding features won't help. If NPS is negative, more marketing won't help. The product itself is wrong.

The Fix

Use pre-defined decision criteria.

Before launching, define exactly what "success" and "failure" look like in numbers. When data comes in, compare against the criteria—not against your hopes.

The Persevere / Pivot / Kill Framework

After a defined period (typically 6-8 weeks of beta), review your North Star and Actionable Metrics against your hypothesis. There are only three outcomes:

Persevere

The hypothesis is validated. Metrics meet or exceed success criteria. Retention is stable. NPS is positive.

Action: Double down. Optimize the funnel. Prepare for scale. Add features that improve retention.

Pivot

The hypothesis is partially valid, or a new insight has emerged. Something works, but not what you expected.

Action: Change one meaningful variable while keeping others constant. Execute a strategic pivot.

Kill

The hypothesis is invalidated. Users are indifferent. Retention is zero. Feedback is apathetic, not negative.

Action: Shut down the project. Return resources. Celebrate the "fast fail."

The Decision Scorecard

Use this scorecard to make the decision objectively:

Persevere / Pivot / Kill Scorecard

Signal Persevere Pivot Kill
Week 4+ Retention >20% 5-20% <5%
NPS Score >30 0-30 <0
User Feedback Tone Enthusiastic Interested with reservations Apathetic
Feature Requests Incremental improvements Fundamental changes None (they don't care)
Willingness to Pay Yes, at target price Yes, but price sensitive No interest

The Four Types of Strategic Pivots

A pivot isn't a random change. It's a strategic decision to change one meaningful variable while keeping others constant. Here are the four most common pivot types:

Zoom-In Pivot

One feature becomes the whole product.

Your users love one specific feature and ignore everything else. Cut the rest and focus entirely on what works.

Example: Flickr started as a chat room for a game. The photo-sharing tool was the only thing people used. They killed the game and became Flickr.

Zoom-Out Pivot

The MVP becomes one feature of a larger product.

Your product works, but it's too narrow to be a standalone business. It becomes a feature in a larger platform.

Example: A standalone PDF tool becomes part of a full productivity suite that solves a broader problem.

Customer Segment Pivot

Right problem, wrong customer.

The product solves a real problem—just not for the users you originally targeted. A different segment is a better fit.

Example: Slack started as an internal tool for a gaming company. They realized developers everywhere needed it, not just gamers.

Customer Need Pivot

Right customer, wrong problem.

The target customer is right, but the problem you're solving isn't burning enough. A different problem for the same customer is more compelling.

Example: During customer interviews, you discover they don't care about your solution—but they keep complaining about something else. Pivot to that.

The Pivot Trap

A pivot is NOT "let's try a bunch of random things." Each pivot should be a structured hypothesis based on specific learning from the previous iteration. If you don't know why you're pivoting, you're just flailing.

Bug #2: The "Just One More Feature" Trap

When metrics are bad, the temptation is to add features. "If only we had X, users would stick around." This is almost always wrong.

The Bug

"Retention is low because we don't have notifications/dashboards/integrations."

If users don't return after trying your core value proposition, it's because the core value proposition is wrong—not because you're missing features.

The Fix

Ask: "Is the core loop working?"

If users complete the core action and still don't return, adding features won't help. You need to pivot the value proposition, not add to it.

The Post-Mortem Ritual

Whether you persevere, pivot, or kill—always run a post-mortem. Document what you learned so the next experiment is smarter.

Post-Mortem Template

Original Hypothesis:What did we believe?
Experiment Run:What did we actually do?
Key Metrics:What did the data show?
Qualitative Feedback:What did users say?
Biggest Surprise:What didn't we expect?
Decision Made:Persevere / Pivot / Kill
Next Hypothesis:What will we test next?

Celebrating the Fast Fail

Killing a project isn't failure—it's success. You've successfully avoided wasting months or years on something that wouldn't work.

The Real Failure

The real failure isn't killing a project that isn't working. The real failure is persisting with something that clearly isn't working because you can't let go. Every month you spend on a dead product is a month you're NOT spending on the next idea that might actually succeed.

Key Takeaways

Remember These Truths
  1. 42% of startups fail from no market need. Most MVPs fail because they built the wrong thing, not because they built it wrong.
  2. Pre-define success criteria. Know what "good" looks like in numbers before you launch.
  3. Use the scorecard. Retention, NPS, feedback tone, and willingness to pay tell you which path to take.
  4. Pivot strategically. Change one variable at a time based on specific learning.
  5. Celebrate fast fails. Killing a project quickly is a success—it frees you to try something that might actually work.

Congratulations! You've completed the MVP & Solution Design playbook. You now have the frameworks to build products that learn, not products that fail.

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Works Cited & Recommended Reading
RAT vs MVP Philosophy
  • 1. Ries, E. (2011). The Lean Startup. Crown Business.
  • 2. "Why RAT (Riskiest Assumption Test) beats MVP every time." LinkedIn
  • 3. "Pretotyping: The Art of Innovation." Pretotyping.org
  • 6. "Continuous Discovery: Product Trio." Product Talk
  • 7. "MVP Fidelity Spectrum Guide." SVPG
Minimum Lovable Product
  • 8. Olsen, D. (2015). The Lean Product Playbook. Wiley.
  • 9. "From MVP to MLP: Why 'Viable' Is No Longer Enough." First Round Review
  • 10. "Minimum Lovable Product framework." Amplitude Blog
Hypothesis-Driven Development
Assumption Mapping
  • 15. Bland, D. & Osterwalder, A. (2019). Testing Business Ideas. Wiley.
  • 16. "Risk vs. Knowledge Matrix." Miro Templates
  • 17. "Identifying Riskiest Assumptions." Intercom Blog
User Story & Impact Mapping
  • 20. Patton, J. (2014). User Story Mapping. O'Reilly Media.
  • 21. Adzic, G. (2012). Impact Mapping. Provoking Thoughts.
  • 22. "Jobs-to-Be-Done Story Framework." JTBD.info
  • 23. "The INVEST Criteria for User Stories." Agile Alliance
  • 24. "North Star Metric Framework." Amplitude
  • 25. "Opportunity Solution Trees." Product Talk
  • 26. Torres, T. (2021). Continuous Discovery Habits. Product Talk LLC.
Pretotyping Techniques
Prioritization Frameworks
Build vs Buy & No-Code
Metrics & Analytics
Launch Operations & Analysis

This playbook synthesizes methodologies from Lean Startup, Design Thinking, Jobs-to-Be-Done, Pretotyping, and modern product management practices. References are provided for deeper exploration of each topic.