Phase 1: Business Framing
Week 1Deep dive into Myntra's monetization, AARRR funnel, and strategic principles (Discovery, Creator Commerce, Personalization).
Myntra Concept: A 1-Tap Outfit Builder to Drive AOV
A provisional UX research and concept strategy case study exploring how eliminating styling anxiety and 'AI slop' could drive multi-item purchases.
The Problem
The Wishlist Graveyard
Myntra is an absolute powerhouse for fashion discovery. But while it is incredibly easy to buy a single t-shirt or a pair of jeans, buying a complete, coordinated outfit is where the user journey breaks.
My research revealed a massive friction point: users consistently abandon items in their wishlists because they suffer from heavy 'styling anxiety'. They simply cannot visualize how a piece pairs with other items, leading to high wishlist-to-cart drop-offs.
User Research
The Exhausting Multi-App Dance
To figure out what to wear, shoppers are doing the exhausting job of a personal stylist. I conducted Reddit validation research and 6 in-depth user interviews to map out their actual behavior. The truth? They leave the app.
JTBD & Synthesis
What users are actually hiring Myntra to do
After affinity mapping the raw interview notes, I translated the core frustrations into Jobs-To-Be-Done (JTBD) to anchor our product strategy.
Product Strategy
From Single Units to Instant Outfits
Myntra's business model thrives on marketplace commissions and fast fulfillment. If we can eliminate the visual uncertainty that causes users to jump back and forth between different apps, the provisional hypothesis suggests we could significantly improve the UX and potentially increase the Average Order Value (AOV) through multi-item cross-purchasing.
Visual Craft
Designing for Trust & Fluidity
Because fashion is inherently visual, the UI needed to step out of the way and let the clothing shine. I focused heavily on fluid micro-interactions, component scalability, and establishing a high-trust visual language.
Concept Feature
1-Tap Outfit Builder & RealFit Studio
To bridge the gap between discovery and purchase, I conceptualized a feature that pairs algorithmic outfit curation with community-backed, real-body visualization. This concept aims to remove off-platform friction and test the potential for driving multi-item cross-purchasing.
Research Timeline
Phase 2: Empathy & Validation
Week 2Conducted Reddit analysis and 1-on-1 user interviews to uncover the multi-app styling loop and the backlash against synthetic AI models.
Phase 3: Synthesis & Ideation
Week 3Clustered raw data via Affinity Mapping to formulate the core Functional and Emotional Jobs-to-be-Done (JTBD).
Hypothesized Business Impact
Projected AOV Lift
A provisional hypothesis projecting an increase in Average Order Value by removing friction for multi-item purchases.
Potential Drop in Returns
Estimating a reduction in 'trial and error' return rates by providing accurate, real-body visualization rather than synthetic guesswork.
Key Takeaways
AI must build trust, not break it. Just because we can generate mathematically perfect AI models doesn't mean we should. Users demand authenticity and want to see how clothes drape on real humans with absolute identity preservation, not beautified synthetic avatars.
Design for the emotional job. Shoppers aren't just buying fabric; they are buying the confidence that they won't look silly when they step out the door. Solving styling anxiety serves as a powerful theoretical conversion lever.