Introduction
Enhancing Zalando's virtual try-on feature to make it more accurate and user-friendly is a critical initiative that could significantly impact user experience and conversion rates. This improvement aligns with the growing trend of augmented reality in e-commerce and addresses the persistent challenge of fit and style uncertainty in online fashion shopping. I'll approach this problem by first clarifying the context, then analyzing user segments and pain points, before proposing and evaluating solutions.
Step 1
Clarifying Questions
Why it matters: Helps prioritize areas for improvement and benchmark our performance. Expected answer: Our feature has good engagement but lower conversion impact compared to leaders. Impact on approach: Would focus on improving accuracy and user trust to drive conversions.
Why it matters: Determines the scope of potential improvements and technical constraints. Expected answer: Using AI-driven 3D modeling with limitations in fabric physics and body diversity. Impact on approach: Would prioritize improvements in these specific areas.
Why it matters: Influences the extent of personalization possible and potential ethical considerations. Expected answer: Collecting basic body measurements and style preferences, adhering to GDPR. Impact on approach: Would explore ways to enhance personalization while maintaining privacy.
Why it matters: Affects the scope of improvements and potential for brand-specific enhancements. Expected answer: Limited brand involvement, mostly providing product images and measurements. Impact on approach: Would explore ways to integrate brand-specific data and styling recommendations.
Tip
Now that we've clarified the context, let's take a brief moment to organize our thoughts before moving on to user segmentation.
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