Intelligent Returns Management
AI-powered system that predicts, prevents, and processes returns, saving 40% in reverse logistics costs and recovering 60% more value.
Executive Summary
A fashion retailer deployed an AI returns platform that predicts return probability at checkout, grades item condition from photos, and routes returns optimally. The system reduced reverse logistics costs by 40% and recovered 60% more value from returned items.
Background & Context
E-commerce returns have become a $800 billion annual problem. Online apparel return rates can exceed 30%, with most items losing significant value in the reverse logistics process. The average returned item takes 2-3 weeks to get back online—by which time it often requires markdown. Meanwhile, return fraud costs retailers billions annually. The companies that can optimize the returns lifecycle—predicting, preventing, processing, and recovering value—gain significant margin advantages.
The Challenge
Returns were a massive profit leak for a fashion retailer, costing them 15% of total revenue. The process was slow, manual, and frustrating for customers.
Most returned items were sold to liquidators for pennies on the dollar because the retailer couldn't inspect and restock them fast enough.
Return fraud was rising, with bad actors exploiting lenient policies to return worn or counterfeit items.
Our Approach
We built an end-to-end returns optimization platform. At checkout, the system predicts return probability and triggers interventions like sizing prompts. When returns occur, computer vision grades item condition from photos. Smart routing sends items to the optimal destination—store, DC, or liquidation—based on value, condition, and demand. Fraud detection identifies serial returners and suspicious patterns.
Solution Workflow
The diagram below shows how our Returns Management platform predicts, prevents, and processes returns through the entire lifecycle.
The Solution
Syvoq implemented an Intelligent Returns Management platform that optimizes the entire lifecycle of a return.
- Return Probability Scoring: Predicts the likelihood of a return at the point of cart addition, triggering interventions like sizing prompts or live chat.
- Automated RMS: Self-service portal that instantly approves returns and issues labels or QR codes.
- Smart Routing: Directs returns to the optimal location (store, DC, or liquidation) based on item value, condition, and demand.
- Fraud Detection: Identifies serial returners and suspicious patterns to block fraudulent requests.
Key Technologies
Training & Deployment
The fraud model was trained on thousands of confirmed fraud cases. The routing logic uses a digital twin of the supply chain to calculate the most profitable path for every item.
Computer vision models were trained to grade item condition from user-uploaded photos, allowing for instant refunds or credit.
Technical Architecture
Computer Vision Grading
Assesses damage and wear from photos to determine resale viability.
Decision Logic
Determines whether to restock, repair, recycle, or donate.
Logistics Integration
Connects with carriers for real-time tracking and label generation.
The Impact
Turning returns from a cost center into a loyalty driver and value recovery engine.
Financial Impact
- •Reduced reverse logistics costs by 40%.
- •Recovered 60% more value by restocking items faster.
- •Reduced return rate by 5% through proactive sizing help.
Customer Experience
- •Refund speed improved from 10 days to instant (for trusted customers).
- •NPS for the returns process jumped by 15 points.
- •Customer lifetime value increased for customers who had a smooth return experience.
Value Recovery
Speed is value. Getting a returned item back online in 2 days instead of 20 means selling it at full price instead of markdown.
Key Takeaway
A great returns experience buys you the customer's trust for the next purchase. Optimizing the backend ensures you don't lose your shirt doing it.
Stop losing money on returns.
Discover how our Intelligent Returns Management can save costs and boost loyalty.