What Went Wrong...
Examining the missteps of various software products across industries reveals common pitfalls that can derail even the most promising innovations. From inadequate market research and poor user experience design to insufficient testing and failure to adapt to technological advancements, these challenges underscore the importance of thorough planning and execution. The following section outlines specific cases, offering insights into how these factors contributed to their downfall and the lessons that can be gleaned to inform future endeavors.
Available Lessons:
200
WeWork Space Management Software
PropTech
WeWork
Aimed at helping companies manage office spaces, the software failed due to a lack of integration with other property management tools and limited usability.
WHAT WENT WRONG
Insufficient integration with industry-standard tools
Poor user experience for facility managers
SIGNALS MISSED
Low adoption rates among enterprise clients
Feedback highlighting feature gaps compared to competitors
HOW COULD THEY HAVE AVOIDED THIS
Prioritizing integration with widely used tools in PropTech
Conducting user testing with property managers
TEAMS INVOLVED
Product, Design, Engineering, Sales
HighQ Workflow Automation
LegalTech
HighQ (Thomson Reuters)
The workflow automation tool failed to scale effectively for large firms due to technical bugs and poor user experience.
WHAT WENT WRONG
Weak backend performance during large-scale deployments
Limited customization for complex workflows
SIGNALS MISSED
Customer complaints about bugs during implementation
Low satisfaction scores from large enterprise clients
HOW COULD THEY HAVE AVOIDED THIS
Stress-testing for scalability before launching
Improving customization options for large firms
TEAMS INVOLVED
Product, Engineering, QA, Customer Success
Relativity Contract Analysis Tool
LegalTech
Relativity
The tool struggled with adoption due to its complex setup and lack of user-friendly document review features.
WHAT WENT WRONG
Overcomplicated interface for non-technical users
Poor integration with existing document management systems
SIGNALS MISSED
User feedback highlighting steep learning curves
High churn rates among early adopters
HOW COULD THEY HAVE AVOIDED THIS
Simplifying workflows for faster onboarding
Building integrations with popular document management tools
TEAMS INVOLVED
Product, Design, Engineering, Customer Success
Zillow Offers (iBuying)
PropTech
Zillow
The iBuying feature that aimed to purchase homes directly from sellers collapsed due to inaccurate property valuations and high financial losses.
WHAT WENT WRONG
Poor algorithmic accuracy in predicting home prices
Overaggressive expansion without adequate testing
SIGNALS MISSED
Early reports of significant discrepancies in valuation predictions
Rising costs and declining profit margins flagged internally
HOW COULD THEY HAVE AVOIDED THIS
Testing valuation algorithms extensively with real-world data
Scaling cautiously with pilot programs in select markets
TEAMS INVOLVED
Product, Data, Operations, Marketing, CEO
Everlaw Predictive Coding (Early Versions)
LegalTech
Everlaw
Early predictive coding tools for document review failed due to inaccuracies in prioritizing relevant documents.
WHAT WENT WRONG
Poor machine learning model accuracy
Limited feedback mechanisms for users to improve results
SIGNALS MISSED
Feedback from beta users highlighting irrelevant document prioritization
Low adoption rates among law firms
HOW COULD THEY HAVE AVOIDED THIS
Iterating AI models with real-world feedback
Providing intuitive tools for users to refine results
TEAMS INVOLVED
Product, AI, Engineering, Customer Success
DoNotPay Legal Chatbot (Initial Rollout)
LegalTech
DoNotPay
The chatbot, designed to automate legal advice, struggled with providing relevant and accurate responses to complex queries.
WHAT WENT WRONG
Overpromised AI capabilities for nuanced legal issues
Poor handling of jurisdiction-specific variations
SIGNALS MISSED
Early reports of irrelevant or incorrect advice
Negative feedback about chatbot limitations in real cases
HOW COULD THEY HAVE AVOIDED THIS
Limiting scope to simpler legal tasks initially
Training AI on jurisdiction-specific data
TEAMS INVOLVED
Product, AI, Compliance, Legal, Customer Success