Company Overview
UrbanStyle is a contemporary fashion retailer catering to young professionals, operating 8 stores in major metros along with a thriving online business. Their fast-fashion model means new collections every 6-8 weeks, creating constant pressure on inventory management.
The Challenge
Fashion retail is unforgiving. A style that's hot in September is worthless by November. UrbanStyle was struggling with the industry's core dilemma: buy too much, you're stuck with dead inventory; buy too little, you miss the trend window.
High Fashion Write-offs
End-of-season clearances were painful. Styles that didn't sell at full price were marked down 50%, then 70%, and still sat on shelves. Annual write-offs exceeded ₹2.5Cr — inventory that was eventually donated or destroyed.
Poor Trend Prediction
The buying team relied on fashion shows, social media intuition, and past experience. Sometimes they nailed it; sometimes they bought heavily into trends that never caught on in their market.
Slow Markdown Response
Markdowns happened on a fixed calendar (mid-season sale, end-of-season sale) regardless of actual inventory health. Fast-moving styles got discounted when they didn't need to be; slow movers weren't marked down early enough.
"In fashion, timing is everything. You have maybe 8 weeks to sell a style at full price. Wait too long to markdown, and you're giving it away. Go too early, and you're throwing away margin."
— Ananya Reddy, Merchandising Director, UrbanStyle
The Solution
UrbanStyle implemented Decisio with a fashion-specific approach:
1. Trend-Aware Forecasting
Orion incorporated fashion-specific signals that traditional forecasting ignores:
- Social media trend detection (Instagram, Pinterest style tracking)
- Initial velocity analysis (how fast styles sell in first 2 weeks)
- Similar style performance (using visual AI to find pattern matches)
- Regional preferences (metro vs tier-2 style differences)
2. Dynamic Markdown Optimization
Astra recommended markdown timing and depth based on actual sell-through curves, not calendar dates:
- Early warning for underperforming styles (week 3-4 alerts)
- Optimal markdown depth to maximize recovery value
- Channel-specific recommendations (stores vs online)
3. Buying Recommendations
For future seasons, Decisio analyzed what worked and what didn't, providing input for buying decisions:
- Style attribute analysis (what colors, patterns, fits performed)
- Price point optimization by category
- Quantity recommendations based on predicted sell-through
Markdown Strategy Transformation
- • Mid-season: 30% off everything
- • End-of-season: 50% off remaining
- • Final clearance: 70% off dead stock
- • Style-specific timing based on velocity
- • Optimal discount depth per SKU
- • Continuous vs batch markdowns
Results After 6 Months
Inventory Health
- Write-offs reduced by 32% — from ₹2.5Cr to ₹1.7Cr
- Full-price sell-through improved by 18%
- End-of-season carryover inventory down 40%
Revenue Recovery
- ₹80L in additional revenue from optimized markdowns
- Average selling price improved despite more discounting
- Gross margin improved 2.1 percentage points
Process Improvement
- Early trend identification — spot winners and losers in week 3
- Buying team uses AI insights for order quantity decisions
- Store managers get style-specific markdown recommendations
"The biggest mindset shift was accepting that not everything needs to follow the same markdown calendar. A slow-moving dress should be discounted in week 4, not week 10. That single insight saved us crores."
— Ananya Reddy, Merchandising Director, UrbanStyle
Key Insights for Fashion Retail
- Velocity is destiny: First 2-week performance predicts final sell-through
- One-size-fits-all markdowns fail: Each style needs its own strategy
- Early markdown beats deep markdown: 20% off in week 4 > 50% off in week 10
- Channel matters: Online can handle different markdown strategies than stores