The Growing Challenge of Returns for e-Retail

And how AI is solving it.

Author

Mradul Sharma
April 17, 2025

Returns are a mounting crisis for brands worldwide, especially in e-commerce. The numbers tell a staggering story:

  • In the United States, the average online purchase return rate is 16.5%, costing brands over $212 billion annually.

  • Fashion and apparel sectors are hit even harder, with return rates exceeding 30%.

  • In India, overall return rates fluctuate between 15.8% and 30%, with festive seasons spiking returns up to 40%.

  • Fashion returns in India are even more concerning, reaching 49%.

High return rates create multiple layers of challenges for brands:

  • Profit Margin Erosion: Processing a return can cost brands between $10 to $20 per item in the US when factoring in logistics, repackaging, and restocking.

  • Inventory Distortion: Frequent returns create "phantom inventory" issues where products are in transit or held for inspection, making real-time inventory management complex.

  • Operational Bottlenecks: Handling high volumes of returns strains warehousing, customer support teams, and reverse logistics operations.

  • Cash Flow Pressure: Refunds delay revenue realization and complicate cash flow planning, particularly for DTC brands heavily dependent on seasonal sales.

  • Environmental Impact: In the US alone, returned goods generate about 5 billion pounds of landfill waste and 15 million metric tons of carbon emissions annually.

  • Customer Trust Erosion: Frequent return experiences can signal product quality or sizing inconsistencies, hurting brand loyalty.

Addressing these challenges is critical for sustaining profitability and customer trust in a highly competitive e-commerce environment.

How AI Can Solve the Return Crisis

Facing such challenges, brands are increasingly turning to AI agents. Here’s how AI can significantly reduce returns, improve operations, and enhance customer satisfaction:

1. Personalized Recommendations

AI uses customer behavior, purchase history, and return patterns to personalize shopping experiences.

  • In fashion, size prediction models powered by AI have cut sizing-related returns by up to 25%.

  • Major players like Amazon (US) and Myntra (India) leverage AI to recommend better-fit products and improve order accuracy.

2. Smarter Product Descriptions

AI enhances product listings with dynamic descriptions tailored to different audiences.

  • Accurate, AI-generated descriptions and visuals lead to better customer expectations, reducing "product not as expected" returns.

  • Brands with enriched product content see up to 20% lower return rates.

3. Virtual Try-Ons and Fit Prediction

AI-powered AR (augmented reality) tools allow customers to "try before they buy."

  • Companies like True Fit (US) and Myntra Try and Buy (India) have reduced apparel returns by 20-30%.

  • AI-driven virtual try-ons extend to footwear, eyewear, and home decor.

4. Predictive Analytics

AI can forecast the likelihood of a return before the product even ships.

  • Brands use predictive models to flag high-risk orders and proactively offer sizing advice, alternative recommendations, or targeted promotions.

  • This not only cuts down returns but also optimizes inventory and logistics.

5. Optimized Return Logistics

AI streamlines the reverse logistics process by optimizing routing, warehousing, and reselling decisions.

  • In the US, returns generate over 5 billion pounds of landfill waste annually. Smarter, AI-driven logistics reduce environmental impact and costs.

  • Indian logistics companies are increasingly adopting AI in smart warehousing and faster inspections.

6. Conversational Agents (Chatbots)

AI chatbots assist customers pre- and post-purchase.

  • Clarifying questions like "Does this fit true to size?" reduces purchase uncertainty.

  • Brands using AI-powered chatbots have seen 15-20% fewer returns.

  • These agents also gather real-time feedback that feeds into product improvements.

Final Thoughts

As global e-commerce surges toward $2 trillion by 2026, brands that fail to manage returns will see profitability and customer trust erode.

AI is a proactive strategy for sustainable growth. Early adopters of AI-powered personalization, predictive analytics, and customer support could dominate the future of retail, both in the US and India.