Style Swipe

TL;DR: Amazon's current shopping experience overwhelms users with endless product lists, leading to choice paralysis. The proposed Amazon Style Swipe introduces a card-based interface for engaging and personalized fashion discovery.

Year
2024

Written By
By Abhishek Gorla

Context

Amazon Fashion's current shopping experience relies on traditional scrolling and filtering, which can be overwhelming and tedious for users as they navigate through long lists of items to find what they like. This design often leads to choice paralysis, especially for users browsing without specific intent, and results in missed opportunities for product discovery.

Hypothesis

A swipe-based shopping interface will transform fashion discovery into a faster, more engaging, and personalized experience, driving increased engagement, higher conversions, and greater customer satisfaction.

What can we learn from competitors?

Swipe-based interfaces have proven highly effective in engaging users across industries. Platforms like Tinder and Bumble have shown how gesture-based navigation simplifies decision-making, while fashion apps like SHEIN have adopted similar principles for product discovery. These examples highlight the potential of a swipe-first approach to create a fun and sticky user experience.

User Cohorts

Our target audience includes:

  • Fashion-forward shoppers aged 18–45.

  • Mobile-first users who value intuitive, gesture-based interactions.

  • Time-pressed individuals seeking quick browsing options.

  • Customers who enjoy discovering new styles and trends through exploration.

User Painpoints

What are the pain points that these users need addressed?

  • Overwhelming number of options leads to decision fatigue.

  • Discovering styles aligned with personal preferences is time-consuming.

  • Shopping feels transactional, lacking engagement or fun.

  • Navigating through tabs for details and similar items interrupts the flow.

User Journey

Problem Statement

The current shopping flow is linear and lacks excitement or personalization, making the process cumbersome and less engaging.

Goal Statements

  • Streamline product discovery using an intuitive swipe-based interface.

  • Create a fun, engaging experience that encourages repeat visits.

  • Personalize recommendations to reduce decision fatigue and improve conversion rates.

Feature Prioritization and MVP Definition

High Priority (MVP):

Swipe interface with four gestures:

  • Right swipe: Add to wishlist.

  • Left swipe: Remove from recommendations.

  • Up swipe: Add to cart.

  • Down swipe: Show similar items.

  1. Initial style quiz to establish preferences.

  2. Basic machine learning recommendations using swipe behavior and purchase history.

  3. Product cards with key information (image, price, rating, size, Prime eligibility).

Future Enhancements:

  • Advanced personalization filters (occasion, brand preferences, color preferences).

  • Social features like shared collections and friend feeds.

User Stories

  • Monet Goode

    “I used to spend hours scrolling through clothes I didn’t like. With Style Swipe, I found three outfits I love in under 10 minutes!”

  • Emmett Marsh

    “The style quiz nailed my preferences. Now I swipe through tailored recommendations, and it’s fun!”

  • Eleanor Parks

    “As a busy mom, I appreciate how quickly I can find what I need. Swiping up to add to my cart is gamechanger!”

Solution

The product will be built as a feature in the Amazon Fashion app, leveraging the following technologies:

  • Frontend: A swipe-based interface using React Native for smooth gesture-based interactions.

  • Backend: Recommendation engine powered by Amazon Personalize, incorporating user behavior, purchase history, and demographics.

  • Product Cards: Dynamically generated using Amazon’s existing product database, ensuring relevant, high-quality visuals.

  • Integration: A/B testing for adoption metrics before a phased rollout.

Risks and Tradeoffs

  • Risk 1: Resistance from users who prefer traditional browsing.

    Mitigation: Provide an opt-out feature for swipe-based browsing and monitor user engagement closely.

  • Risk 2: Poor recommendation quality may frustrate users.

    Mitigation: Continuously improve algorithms using feedback and behavioral data.

  • Tradeoff: High initial investment in building and testing the feature.

    Mitigation: Focus on a lightweight MVP with iterative improvements.

Measuring Success

  • Engagement metrics: Increase in session duration and swipes per user.

  • Conversion metrics: Growth in wishlist additions, cart additions, and purchases.

  • Retention metrics: Higher repeat usage of the Style Swipe feature.

  • Qualitative metrics: Positive feedback from surveys and app store reviews.

Launch and GTM Strategy

  • Beta Launch: Roll out to a small segment of Prime users for feedback and refinement.

  • Awareness Campaign:

    • Highlight the feature in Amazon app banners and emails.

    • Use influencers and fashion bloggers to demonstrate the interface.

  • Gradual Rollout: Expand to a wider audience while collecting feedback to iterate quickly.

  • Partnerships: Collaborate with brands to showcase curated collections optimized for Style Swipe.

Final Thoughts

This feature has the potential to make Amazon the go-to platform for fashion discovery by combining personalization, ease, and engagement.