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
Walmart Scan & Go
RetailTech
Walmart
A mobile checkout system failed due to low consumer adoption and challenges with theft prevention.
WHAT WENT WRONG
Poor UX and usability in stores
Lack of robust security measures to prevent misuse
SIGNALS MISSED
Low engagement during pilot tests
Feedback about difficulty scanning items in busy environments
HOW COULD THEY HAVE AVOIDED THIS
Improving UX through customer testing and feedback loops
Adding theft-prevention mechanisms like automated auditing
TEAMS INVOLVED
Product, Engineering, Customer Success, Operations
Amazon Dash Buttons
RetailTech
Amazon
Physical buttons for instant product reordering failed due to limited consumer adoption and the rise of voice assistants like Alexa.
WHAT WENT WRONG
Poor scalability as consumer habits shifted toward voice and app-based shopping
Limited use cases for multiproduct households
SIGNALS MISSED
Low engagement rates compared to app usage
Consumer confusion about managing multiple buttons
HOW COULD THEY HAVE AVOIDED THIS
Focusing on app-based alternatives or voice integrations
Conducting deeper consumer research on shopping behaviors
TEAMS INVOLVED
Product, Marketing, Operations, Design
CoStar Tenant Analysis Tool
PropTech
CoStar Group
A tool designed for commercial real estate tenant insights failed due to inaccurate data and lack of actionable insights.
WHAT WENT WRONG
Incomplete and outdated data sources
Weak analytics tools for generating insights
SIGNALS MISSED
Complaints from brokers about irrelevant or incorrect data
Poor renewal rates among enterprise customers
HOW COULD THEY HAVE AVOIDED THIS
Partnering with reliable data providers for better accuracy
Adding actionable recommendations for tenants and brokers
TEAMS INVOLVED
Product, Data, Engineering, Customer Success
eBay Now
RetailTech
eBay
A same-day delivery service failed due to logistical inefficiencies and high operational costs.
WHAT WENT WRONG
Poor scalability of delivery logistics
Limited geographic reach and product selection
SIGNALS MISSED
High operational costs reported in pilot cities
Limited retailer participation in key markets
HOW COULD THEY HAVE AVOIDED THIS
Partnering with third-party logistics providers
Testing scalability in smaller regions before expanding
TEAMS INVOLVED
Product, Operations, Engineering, Marketing
SmartRent Energy Management
PropTech
SmartRent
A tool for managing energy consumption in rental properties failed due to technical glitches and limited adoption by property managers.
WHAT WENT WRONG
Bugs in energy tracking and reporting
Poor integration with existing smart home systems
SIGNALS MISSED
Feedback about inconsistent energy tracking results
Low adoption rates among property managers
HOW COULD THEY HAVE AVOIDED THIS
Resolving technical issues before launch
Building partnerships with smart home system providers
TEAMS INVOLVED
Product, Engineering, Customer Success, Sales
RentPath AI Recommendations
PropTech
RentPath
AI-driven rental recommendations failed to deliver relevant matches, leading to user frustration and churn.
WHAT WENT WRONG
Poor training of AI models for diverse user preferences
Weak feedback loops to improve recommendation accuracy
SIGNALS MISSED
Complaints about irrelevant rental recommendations
Low click-through rates on recommended properties
HOW COULD THEY HAVE AVOIDED THIS
Refining AI models with real-world user behavior data
Building feedback loops to learn from user interactions
TEAMS INVOLVED
Product, AI, Data, Engineering