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Introduction
Improving the accuracy of Vivino's wine label recognition technology is a critical challenge that directly impacts user experience and the core value proposition of the app. This technology serves as the foundation for Vivino's ability to provide instant wine information, ratings, and recommendations to users. I'll approach this problem by first clarifying our current situation, then analyzing key user segments and their pain points. From there, I'll generate and prioritize solutions, and finally, propose metrics to measure our success.
Step 1
Clarifying Questions
Why it matters: This helps us understand the scale of our data and potential for machine learning improvements. Expected answer: Millions of users, hundreds of thousands of daily scans. Impact on approach: A large dataset would suggest focusing on AI/ML improvements, while a smaller one might indicate a need for more data collection strategies.
Why it matters: This establishes our baseline and helps set improvement targets. Expected answer: 85-90% accuracy rate. Impact on approach: A high accuracy rate might lead us to focus on edge cases, while a lower rate would suggest more fundamental improvements are needed.
Why it matters: This helps identify potential gaps in our recognition capabilities. Expected answer: Strong coverage in major wine-producing countries, some gaps in emerging markets. Impact on approach: Uneven coverage would suggest focusing on expanding our database in underrepresented regions.
Why it matters: This informs how we might improve the user experience around failed scans. Expected answer: Users often try rescanning or manually searching for the wine. Impact on approach: High manual search rates might suggest improving search functionality as a complementary solution.
Tip
Now that we've established some context, let's take a brief moment to organize our thoughts before moving on to user segmentation.
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