Post-Mortem Analysis: Experiment 2

A detailed reflection on the development, challenges, and lessons from building a web-based task management tool.

Premise

The experiment aimed to develop a web-based task management tool, hypothesizing that AI-driven prioritization would increase user productivity by 30%. The tool was designed to address inefficiencies in task organization for small businesses, with an expected outcome of streamlined workflows and higher task completion rates.

Process and Analysis

1. Design Phase

Objective: Create a user-centric design to address task management inefficiencies.

What Was Done: Used Figma to create wireframes and prototypes, focusing on a minimalistic interface with drag-and-drop functionality. Conducted user interviews to validate layout.

What Went Well: Intuitive drag-and-drop feature received positive feedback in early testing.

What Went Wrong: Overloaded dashboard with analytics, confusing novice users, similar to Wesabe’s cluttered UX.

Evidence: Usability tests scored 7.5/10; 60% of users reported navigation issues.

Lesson from Famous Post-Mortem: Wesabe’s Marc Hedlund emphasized that complex designs alienate users. Mint’s simple UX drove retention, guiding our focus on clarity.

2. Color Selection Phase

Objective: Choose a color scheme to enhance usability and brand trust.

What Was Done: Selected a blue-green palette using Adobe Color, aligned with productivity branding. Tested contrast for accessibility.

What Went Well: High-contrast colors improved readability for 90% of users.

What Went Wrong: Bright green accents distracted users during long sessions.

Evidence: A/B tests showed a 5% engagement drop with vibrant accents, echoing Wesabe’s visual overload.

Lesson from Famous Post-Mortem: Wesabe’s post-mortem highlighted that cohesive, calm visuals build trust, as Mint’s aesthetic demonstrated.

3. Team Building Process

Objective: Assemble a lean team to execute the experiment efficiently.

What Was Done: Hired a product manager, two developers, a designer, and a sales lead. Used Slack for communication and Jira for task tracking. Operated remotely.

What Went Well: Clear role assignments led to a prototype in 3 weeks.

What Went Wrong: Overhired a second developer, causing task overlap and 15% rework, similar to Fast’s inefficiencies.

Evidence: Team feedback noted redundant efforts; project hit 80% of milestones on time.

Lesson from Famous Post-Mortem: Fast’s Domm Holland warned that overhiring without clear roles creates chaos. Moz’s Rand Fishkin stressed aligning team size with validated demand.

4. Coding Phase

Objective: Build a functional task management tool based on design specs.

What Was Done: Developed using React and Node.js, following Agile sprints. Implemented AI prioritization and task sorting.

What Went Well: Achieved 98% uptime and fast load times under 2 seconds.

What Went Wrong: AI algorithm bugs delayed launch by 2 weeks, mirroring Fast’s technical debt.

Evidence: Bug reports showed 10 critical issues; user tests confirmed latency in AI features.

Lesson from Famous Post-Mortem: Fast’s rushed coding led to costly fixes. Moz’s disciplined sprints and testing prevented technical debt.

5. Identifying Market Size and Market Segments

Objective: Define the target market and estimate its potential.

What Was Done: Conducted surveys and analyzed competitors like Asana. Targeted small businesses with 5-50 employees.

What Went Well: Identified a $150M market for task management tools.

What Went Wrong: Overestimated adoption; only 10% of surveyed businesses showed interest, akin to Moz’s miscalculation.

Evidence: Survey data indicated low willingness to switch tools.

Lesson from Famous Post-Mortem: Moz’s Rand Fishkin noted unvalidated market assumptions led to misaligned strategies. Rigorous research ensures realistic sizing.

6. Identifying Personas and Creating User Stories

Objective: Develop personas and user stories to guide development.

What Was Done: Created personas via 10 user interviews, including “Small Business Owner” and “Freelancer.” Wrote 15 user stories.

What Went Well: Personas aligned with 80% of target users.

What Went Wrong: Missed budget constraints for freelancers, similar to Wesabe’s persona errors.

Evidence: Feedback showed freelancers needed cheaper plans.

Lesson from Famous Post-Mortem: Wesabe’s Marc Hedlund noted misaligned personas led to irrelevant features. Validated personas ensure relevant development.

7. Sales Process: Finding the First 100 Customers

Objective: Acquire 100 early customers to validate the product.

What Was Done: Used LinkedIn outreach, Google Ads, and a freemium model. Offered $10/month subscriptions post-trial.

What Went Well: Acquired 30 customers via LinkedIn with 25% conversion.

What Went Wrong: High $10/month pricing led to 70% trial churn, echoing Moz’s pricing errors.

Evidence: Churn rate hit 70%; feedback cited cost as a barrier.

Lesson from Famous Post-Mortem: Moz’s Rand Fishkin noted high early pricing alienated users. Wesabe’s lack of differentiation lost market share. Affordable pricing builds traction.

8. Iterating with User Interviews

Objective: Refine the product based on user feedback.

What Was Done: Conducted 20 user interviews post-launch, focusing on onboarding and AI features.

What Went Well: Simplified onboarding based on feedback, boosting satisfaction by 15%.

What Went Wrong: Limited interview diversity missed freelancer pain points, similar to Fast’s errors.

Evidence: 65% of users found AI complex; fixes were delayed.

Lesson from Famous Post-Mortem: Fast’s Domm Holland admitted sparse feedback misaligned features. Regular, diverse interviews ensure user-driven development.

9. Iterating Product to Find Product-Market Fit

Objective: Achieve product-market fit through iterative improvements.

What Was Done: Simplified AI features and reduced pricing to $5/month.

What Went Well: Engagement rose from 12% to 18%.

What Went Wrong: Broad feature set diluted value, mirroring Moz’s focus issues.

Evidence: Retention stayed below 30% target.

Lesson from Famous Post-Mortem: Moz’s Rand Fishkin highlighted that chasing too many features delayed fit. Focused iterations are key.

10. Outcome: Failure to Find Product-Market Fit

Summary: The experiment failed to achieve product-market fit due to high pricing, complex AI features, and unvalidated market assumptions. Competitors like Asana offered simpler, cheaper solutions.

Key Indicators: 70% churn rate, 18% engagement, and only 30 customers retained.

Reflection: Poor pricing strategy and overcomplicated features misaligned with user needs, similar to Fast’s board-driven errors and Moz’s overfunding.

Lesson from Famous Post-Mortem: Fast’s misalignment and Moz’s premature scaling highlight the need for lean teams, clear leadership, and validated strategies.

Learnings

• Simplicity in UX is critical, as Wesabe’s loss to Mint showed; complex designs lose users.
• Lean team structures prevent inefficiencies, as Fast’s overhiring and Moz’s scaling issues demonstrated.
• Early sales require affordable pricing and clear differentiation, as Moz’s high prices and Wesabe’s lack of clarity proved costly.
• Validate market and personas early, as Moz and Wesabe’s missteps highlight the risk of assumptions.

Next Steps

• Test UX simplicity in the design phase, inspired by Wesabe, to ensure user-friendly interfaces.
• Define clear team roles and limit hiring until market validation, learning from Fast and Moz.
• Launch sales with low-cost pilots and targeted outreach, as Moz’s pricing issues suggest.
• Conduct weekly user interviews during iteration, as Fast’s limited feedback proved detrimental.