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
Zumper Instant Apply
PropTech
Zumper
The feature to simplify rental applications failed due to technical bugs and poor adoption by landlords.
WHAT WENT WRONG
Technical glitches in the application process
Limited landlord participation in the program
SIGNALS MISSED
High user complaints about failed application submissions
Low participation rates among landlords
HOW COULD THEY HAVE AVOIDED THIS
Fixing technical issues before scaling
Offering incentives to landlords to adopt the feature
TEAMS INVOLVED
Product, Engineering, Customer Success, Marketing
Redfin Concierge Service
PropTech
Redfin
Aimed to help sellers prepare homes for sale, but failed due to high costs and limited customer adoption.
WHAT WENT WRONG
Inefficient processes for home preparation
Poor marketing of the service’s benefits to homeowners
SIGNALS MISSED
Low adoption rates in pilot markets
Negative feedback about the service’s cost-effectiveness
HOW COULD THEY HAVE AVOIDED THIS
Streamlining processes to reduce operational costs
Highlighting case studies to demonstrate ROI for sellers
TEAMS INVOLVED
Product, Operations, Marketing, Sales
Opendoor Rental Platform
PropTech
Opendoor
Aimed to simplify the rental process but faced challenges due to poor property inventory management and tenant dissatisfaction.
WHAT WENT WRONG
Weak backend systems for managing rental inventory
Limited focus on tenant experience and support
SIGNALS MISSED
High tenant complaints about delayed responses and issues with listings
Feedback from landlords about poor visibility into rental activity
HOW COULD THEY HAVE AVOIDED THIS
Building robust inventory management tools
Creating a tenant-focused support team to resolve issues quickly
TEAMS INVOLVED
Product, Engineering, Customer Success, Operations
Knotel Property Matching Software
PropTech
Knotel
Aimed at matching businesses with flexible office spaces, the tool failed due to limited inventory and poor search functionality.
WHAT WENT WRONG
Lack of robust property database
Poor UX for businesses searching for spaces
SIGNALS MISSED
Low engagement rates during search queries
Feedback highlighting difficulty finding relevant spaces
HOW COULD THEY HAVE AVOIDED THIS
Expanding inventory partnerships before launching
Conducting UX testing to simplify the search process
TEAMS INVOLVED
Product, Engineering, Design, Sales
Airbnb Plus
PropTech
Airbnb
The premium property listing service aimed at high-end travelers struggled due to inconsistent quality standards and lack of clear differentiation from regular listings.
WHAT WENT WRONG
Poorly enforced quality control for premium properties
Confusion among users about the value of Airbnb Plus
SIGNALS MISSED
Complaints from travelers about properties not meeting promised standards
Low conversion rates for listings upgrading to Airbnb Plus
HOW COULD THEY HAVE AVOIDED THIS
Establishing stricter quality checks for Plus properties
Communicating the unique value of the service more effectively
TEAMS INVOLVED
Product, Operations, Marketing, Customer Success
Compass AI Pricing Tool
PropTech
Compass
The AI-powered tool to suggest property prices failed due to inaccurate predictions and lack of transparency in the model.
WHAT WENT WRONG
Weak AI model training and limited data inputs
Lack of explainability in how price suggestions were generated
SIGNALS MISSED
Agent complaints about irrelevant or misleading price suggestions
Declining trust among real estate professionals
HOW COULD THEY HAVE AVOIDED THIS
Training AI models with diverse, high-quality datasets
Adding transparency features to explain pricing logic
TEAMS INVOLVED
Product, AI, Data, Customer Success, Marketing