Stylumia NXT Hackathon 2024

Context

The fashion retail industry is at a critical inflection point. With a global market size of $1.53 trillion in 2023 (Statista, 2024), it faces unprecedented challenges in predicting and meeting consumer demands:

• Speed to Market: Fashion cycles have evolved from traditional 4 seasons to as many as 50-plus micro-seasons per year (McKinsey State of Fashion 2023).
• Digital Transformation: E-commerce fashion sales grew by 29.5% in 2023 compared to 2019 (Statista, 2024).
• Sustainability Pressure: According to The Ellen MacArthur Foundation (2023), approximately 20% of produced fashion inventory goes unsold, contributing to $100B+ in annual waste.
• Consumer Volatility: McKinsey's Consumer Pulse Survey (2023) shows that 65% of consumers have changed their shopping behavior post-pandemic.

As a result of these industry shifts, fashion professionals face mounting pressure to:

• Predict trends quicker to adjust to rapidly evolving consumer demand and shorter season cycles.
• Track real-time market movements.
• Make multi-million dollar inventory decisions.
• Balance creativity with data-driven decisions.
• Reduce waste and improve sustainability.
  

Impact of Poor Trend Prediction:

• According to the UN Environment Programme (2023):
  • 10% of global carbon emissions come from the fashion industry
  • 20% of global wastewater is produced by the fashion industry
• The fashion industry loses $120B annually to markdowns (McKinsey State of Fashion 2023)
• The Boston Consulting Group reports that 40% of fashion products are sold at marked-down prices

Problem Statement: Design the future of Trend Discovery

With the fashion industry evolving at breakneck speed, how are professionals meant to stay ahead? Despite technological advances, the gap between available data and actionable
insights continue to widen.

Current State Challenges

Time & Efficiency Issues

• Fashion professionals spend countless hours weekly across multiple platforms hunting for trends
• Manual compilation of trends from 10+ different sources (social media, runway shows, street style, competitors, etc.)
• Scattered information requires constant switching between tools and platforms
• Critical time lost in administrative tasks rather than strategic analysis

Data Complexity

• Information overload from multiple unstructured sources
• Difficulty in distinguishing signals from noise
• No standardization in trend terminology or categorization
• Challenge in quantifying trend strength or relevance
• Limited ability to track trend evolution over time

• High-stakes decisions based on fragmented information
• Difficulty in validating trend predictions
• Limited ability to personalize trends for specific markets/audiences
• Gap between trend identification and practical application
• Risk of missed opportunities or overinvestment

• Siloed information across teams
• Inconsistent trend interpretation between departments
• No centralized platform for sharing insights
• Difficulty in maintaining trend knowledge base
• Limited ability to capture and share expert insights

1. Experience Transformation
   • From: Boring, overwhelming interfaces
   • To: Engaging, intuitive experiences that inspire creativity
2. Workflow Transformation
   • From: Complex, manual processes
   • To: Streamlined, automated workflows that enhance productivity
3. Decision Support Transformation
   • From: Fragmented, uncertain decisions
   • To: Confident, data-backed choices with a clear rationale
4. Collaboration Transformation
   • From: Siloed, inconsistent communication
   • To: Unified, transparent team collaboration 

1. Discovery
   • How might we make trend spotting more intuitive and efficient?
   • How can we combine automated insights with human expertise?
   • How might we ensure trends are relevant to specific business contexts? 
2. Analysis
   • How might we validate trend strength and potential?
   • How can we track trend evolution more effectively?
   • How might we quantify trend impact on business outcomes? 
3. Application
   • How might we bridge the gap between trend insights and practical decisions?
   • How can we personalize trends for different markets/categories?
   • How might we streamline trend implementation workflows?

• Lead design conceptualization for seasonal collections
• Analyze market trends and translate them into commercially viable designs
• Create tech packs and collaborate with product development
• Work with buyers and merchandisers to optimize collection balance
• Monitor competitor activities and identify market opportunities

• Trend interpretation and adaptation
• Collection development
• Technical specifications
• Color and fabric selection
• Sample review and modification

Assortment Planner

Role Overview

Optimizes product mix across categories, ensuring the right products are available at the right time and location.

Real Job Description

• Develop and maintain seasonal buy plans
• Analyze historical data and trends to forecast future demand
• Create assortment strategies across categories and channels
• Monitor stock levels and make in-season adjustments
• Partner with buying and merchandising teams on range planning

Key Responsibilities

• Sales forecasting
• Budget allocation
• Range architecture
• Stock level optimization
• Performance analysis

Buyer

• Identify and select merchandise for upcoming seasons
• Negotiate with vendors on prices, quantities, and delivery
• Track market trends and competitor offerings
• Analyze selling patterns and customer preferences
• Collaborate with planning to optimize buy quantities

• Product selection
• Vendor management
• Margin optimization
• Trend analysis
• Range building

1. WGSN
   • https://www.wgsn.com
   • Industry standard for trend forecasting
2. EDITED
   • https://edited.com
   • Retail analytics and intelligence platform
3. HEURITECH
   • https://www.heuritech.com
   • Retail analytics and intelligence platform
4. TRENDALYTICS
   • https://www.trendalytics.co
   • Consumer behavior and product trend analytics General Tools
5. Google Trends
   • https://trends.google.com
   • Search trend analysis tool Social Discovery Platforms
6. Pinterest
   • https://business.pinterest.com
   • Visual discovery and inspiration platform
7. Instagram
   • https://business.instagram.com
   • Social trends and influencer insights

• Product Images
• Product Descriptions
• Price Points
• Availability Status
• Launch Dates
• Discount History
• Brand Information
• Category/Department Details 

1. Trend Engine Architecture
2. Sample Trends
   

• PDF format
• Maximum 20 slides
• Include:
  • Problem understanding
  • User insights
  • Solution overview
  • Key features
  • Technical considerations
  • Future roadmap

• Figma link with viewing access
• All screens in organized frames
• Component documentation
• Prototype links

• PDF format
• Include:
  • Design Process
  • Key decisions
  • Technical notes
  • References

1. Problem Understanding & User Empathy (30%)

• Deep understanding of user motivations
  • Why users need trend insights
  • When and how they make decisions
  • What drives their choices
  • What success looks like for them
• Clear identification of user frictions
  • Current pain points and workarounds
  • Emotional and practical frustrations
  • Time and effort wastages
  • Decision-making challenges

• Understanding of industry context
• Clarity on business implications
• Recognition of workflow complexities
• Insight into user environments

• Aligns with user mental models
• Reduces cognitive load
• Simplifies complex tasks
• Enables quick decision making
• Supports natural work patterns

• Clear information hierarchy
• Intuitive navigation
• Meaningful visualizations

• Consistent design system
• Effective use of typography and color
• Clear visual hierarchy
• Responsive design considerations

• Smooth user flows
• Meaningful micro-interactions
• Clear feedback mechanisms
• Error prevention and handling
• Performance considerations