Smart Wardrobe Matching Tool

1. Reasons for Recommendation and Brief Description

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2. Market Analysis

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3. User Experience Map and User Journey Map

1.Clothing Input - Users input clothing information through photos or imports, and the system categorizes and records the clothing.

Phase 2Matching Recommendation - The system recommends daily clothing combinations based on weather, schedule, and preferences, which users can view and modify.

Phase 3Wardrobe History Record - The system records each wardrobe history to help users avoid repeated outfits and provides suggestions for different occasions.

Phase 4Style Optimization - Based on user feedback, the system learns and optimizes the matching style, recommending more personalized combinations.

4. Monetization Model Design

Subscription services: Basic free services with advanced style recommendations for subscribers. Brand collaboration: Partnering with fashion brands to recommend the latest items through the platform. Additional features: Clothing management functions like maintenance reminders and cleaning suggestions.

5. User Incentive Mechanism Design

Users receive feedback scores for clothing matching suggestions, with high-scoring outfits entering the 'Today's Best Outfits List,' motivating users to try different styles. The system also offers discounts based on usage frequency to encourage long-term use.

6. Potential Risk Assessment

User stickiness: Attracting and retaining user interest, especially with rapidly changing fashion demands, is challenging. Technical challenges: Ensuring the matching algorithm reflects the latest trends and personalized needs is crucial.