Returns are no longer a back-office chore—they’ve become one of the most emotionally charged moments in the customer journey. This was the central theme of a recent Moxie-hosted webinar that brought together retail technology experts to unpack how AI is reshaping the post-purchase experience across the industry.
From predictive modeling to conversational AI to next-generation return routing, the session highlighted both the urgency and opportunity facing retailers today. Here are the biggest insights.
Returns Have Become a Loyalty Moment
The discussion opened with a grounding reality: returns now represent up to $850 billion annually for retailers, with some segments—especially fashion—seeing return rates exceeding 30%.
As guest speaker Suparana Shokeen emphasized, returns aren’t just operational anymore—they’re reputational:
The panelists agreed: a slow or confusing return doesn’t just sour the transaction—it can permanently erode trust.
From Reactive to Predictive: The AI Advantage
Traditionally, retailers wait for customers to initiate a return, absorbing both the cost and the hit to loyalty. But AI is changing that mindset.
Ameena Kulsum described the shift:
Predictive AI can identify products with high return likelihood, detect customer segments prone to certain return reasons, and even suggest better size or style recommendations at the point of purchase. This proactive approach not only prevents returns but strengthens confidence in the brand.
Smarter Logistics Through Data and Computer Vision
Even when returns do occur, AI can drastically reduce cost and cycle time.
Ramesh Patnaik explained how computer vision and automation reshape the reverse-logistics workflow:
From routing decisions to resale eligibility to detecting fraud patterns, data-driven models now optimize what used to be a slow, manual, and expensive process.
AI-Enhanced Customer Experience
Speed matters—but empathy matters more.
With conversational AI now capable of recognizing sentiment, guiding customers through next steps, and escalating to human specialists at the right moment, retailers can defuse emotional friction and turn a negative experience into a loyalty-building one.
Examples discussed included:
- Personalized exchange recommendations instead of refunds
- Loyalty-based return windows
- Size/fit intelligence to reduce bracketing
- Dynamic policy adjustments during peak seasons
The result: happier customers and reduced refund leakage.
A Real-World Transformation: Luxury Glassware Retailer
The panel shared a compelling case study involving a high-end glassware and home décor brand struggling with fragile-product returns, seasonal spikes, and costly logistics.
AI solutions helped the retailer:
- Predict which items were likely to be returned
- Suggest better alternatives before purchase
- Automate condition assessment via computer vision
- Accelerate refunds
- Improve customer satisfaction and operational stability
The outcome was measurable: faster processing, fewer returns, and a stronger brand perception.
Where Returns Are Headed Next
The team closed by looking toward the future—one marked by general intelligence, unified insights, and more sustainable operations.
Key trends include:
- AI models that understand the entire retail journey, not just isolated tasks
- Efficient AI, reducing both compute waste and carbon footprint
- Dynamic personalization, adjusting policies and experiences in real time
- Return-data-driven product design, improving sizing, materials, and forecasting
Ultimately, returns data is becoming a strategic driver across the retail lifecycle.
Final Thoughts
The message was clear: returns hold immense untapped potential. With the right AI strategy, retailers can:
- Reduce costs
- Improve customer satisfaction
- Predict and prevent avoidable returns
- Enhance product quality
- Build lasting loyalty
Most importantly, they can transform a historically painful process into a signature brand experience.
If you’re looking to modernize your returns and exchanges workflow, this webinar delivered a roadmap for what’s possible—and what’s next.
