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
Zalando Wardrobe Assistant
FashionTech
Zalando
A virtual closet feature allowing users to manage their wardrobe failed due to poor usability and lack of integration with Zalando’s main shopping platform.
WHAT WENT WRONG
Clunky interface for uploading and organizing wardrobe items
Limited connection to product recommendations on Zalando
SIGNALS MISSED
User complaints about time-consuming setup processes
Low conversion rates for recommended products
HOW COULD THEY HAVE AVOIDED THIS
Simplifying wardrobe setup through automation tools
Integrating personalized recommendations seamlessly
TEAMS INVOLVED
Product, Design, Engineering, Marketing
Net-a-Porter Style Bot
FashionTech
Net-a-Porter
A chatbot designed to assist customers with styling advice failed due to limited conversational abilities and inaccurate suggestions.
WHAT WENT WRONG
Poor natural language processing capabilities
Limited data on individual customer preferences
SIGNALS MISSED
User complaints about irrelevant or generic style suggestions
Low engagement rates with the chatbot feature
HOW COULD THEY HAVE AVOIDED THIS
Improving NLP with fashion-specific datasets
Training the bot on customer feedback to enhance personalization
TEAMS INVOLVED
Product, AI, Engineering, Customer Success
Stitch Fix Freestyle
FashionTech
Stitch Fix
The standalone shopping experience outside their core subscription model failed to attract users due to poor curation and limited inventory.
WHAT WENT WRONG
Weak personalization algorithms compared to the subscription model
Inventory limitations preventing a broader product offering
SIGNALS MISSED
Low engagement rates for the new feature
Feedback from users citing repetitive or irrelevant recommendations
HOW COULD THEY HAVE AVOIDED THIS
Gradually expanding inventory to support personalization
Conducting A/B tests to refine the shopping experience
TEAMS INVOLVED
Product, AI, Operations, Marketing
Boohoo Sustainability Tracker
FashionTech
Boohoo
A transparency tool aimed at showcasing sustainable practices failed due to limited data and poor credibility among users.
WHAT WENT WRONG
Insufficient data to back sustainability claims
Poor communication of the tool’s purpose and impact
SIGNALS MISSED
User skepticism over vague sustainability metrics
Low usage rates among environmentally conscious shoppers
HOW COULD THEY HAVE AVOIDED THIS
Partnering with third-party auditors to validate claims
Clearly communicating sustainability efforts through the tool
TEAMS INVOLVED
Product, Data, Customer Success, Marketing
Moda Operandi Trunk Show App
FashionTech
Moda Operandi
An app designed to showcase exclusive runway collections failed due to technical glitches and poor user engagement.
WHAT WENT WRONG
Frequent app crashes during live trunk shows
Limited engagement features for high-end customers
SIGNALS MISSED
Customer complaints about technical issues during live events
Low app retention rates among luxury shoppers
HOW COULD THEY HAVE AVOIDED THIS
Stress-testing the app for live event stability
Adding engagement features like chat or exclusive previews
TEAMS INVOLVED
Product, Engineering, Marketing, QA
Glossier Community Commerce Platform
FashionTech
Glossier
A platform aimed to blend social media with e-commerce but failed to gain traction due to limited functionality and technical bugs.
WHAT WENT WRONG
Poorly executed social commerce features
Lack of seamless integration with Glossier’s main e-commerce platform
SIGNALS MISSED
Feedback from users citing difficulty navigating the platform
Low conversion rates compared to direct website traffic
HOW COULD THEY HAVE AVOIDED THIS
Partnering with influencers to drive early adoption
Iterating the platform based on real-world user feedback
TEAMS INVOLVED
Product, Engineering, Design, Marketing