Poor product data, rising returns push up e-commerce logistics costs

E-commerce and quick commerce platforms are using artificial intelligence to improve product data quality, reduce returns, and ease rising logistics costs amid growing customer expectations

A customer in Bareilly, Uttar Pradesh, recently ordered a set of office shirts from a leading e-commerce platform, only to receive products that varied in size and appeared darker than expected. Even though the product details were technically disclosed in the product description at the end, the case highlights the mismatch of not just size but also expectations. Consequently, the ordered items had to be returned. The customer finally received the product again after a week later, demonstrating how poor product data inconveniences customers and increases costs for e-commerce players.

 

According to GS1, a standards organisation set up by the ministry of commerce and industry, Indian e-commerce loses nearly Rs 5,000 crore annually due to poor product data quality, with fashion and apparel accounting for the highest share of return-related losses.

 

“Of the total revenue loss, Rs 2,000 crore translates directly into gross margin erosion, driven by lower conversion efficiency, suppressed listings, and slower sell-through resulting from incomplete or inaccurate product information. In parallel, product returns attributable to poor data quality generate an additional Rs 1,900 crore in direct return costs, reflecting higher reverse logistics, handling, and processing expenses,” the report added.

 

Corroborating the report's findings, the e-commerce enablement software-as-a-service platform, Unicommerce, stated that reverse logistics remains a major cost pressure in Indian e-commerce. It adds roughly 5-7 per cent to the order value as brands pay for pickup and return shipping-and-handling, in addition to the original delivery cost.

 

"Across the broader e-commerce ecosystem, RTOs (return-to origin) typically range between 15-25 per cent of total orders but customer-initiated returns across categories like fashion and apparel are generally higher and often record 20-25 per cent, largely due to size and fit issues, style preferences, and expectation gaps between product listings and the delivered product,” said Kapil Makhija, managing director and chief executive officer of Unicommerce. Notably, reverse logistics refers to the supply chain process of returning products from end users back through the supply chain.

 

Madhu Sudan Pahwa, managing director of e-commerce platform Womancart, also said that one of the most common drivers of returns is expectation mismatch. “Customers rely heavily on product images, descriptions, reviews, and specifications while making purchase decisions. If the  product data is incomplete or not clearly represented, the delivered product may not fully align with what the customer expected.” Pahwa added that reverse logistics can account for roughly 10-20 per cent of total fulfillment costs.

E-commerce players leverage AI

Amid mounting cost pressures from returns and customer inconvenience, e-commerce platforms say they are leveraging technology, including artificial intelligence (AI), to minimise such discrepancies.

 

According to a Flipkart spokesperson, the company does continuous innovations for its seller partners. "Significant AI integration has been introduced to make product listings easier and more accurate for sellers, this is in addition to the work of our in-house teams. This directly translates to a greater customer experience and a significantly lower impact of returns for our seller partners," the spokesperson said, adding that Flipkart has brought in various other solutions, including open box delivery.

 

With AI interventions which have been deployed in the last couple of years, the company said, the percentage of returns continue to decrease, bringing positive results.

 

E-commerce major Amazon also said it offers a customer-friendly return and exchange policy across all categories including fashion and apparel. “We continuously invest in features that help customers make confident purchase decisions and reduce the need for returns. These include detailed size charts and fit guidance, comprehensive product descriptions with multiple images, customer reviews and ratings and AI-powered shopping assistance through Rufus. We also work closely with our seller partners to ensure accurate product information and high-quality imagery,” Nikhil Sinha, director at Amazon Fashion India.

 

“One of the key steps the industry is adopting is implementing end-to-end order tracking and monitoring systems, where every stage of the order lifecycle — from dispatch to delivery and reverse pickup — is documented and recorded. This improves transparency across logistics partners and helps avoid disputes or discrepancies,” said Pahwa of Womancart.

Qcom players not behind

However, it’s not just e-commerce players that are turning to AI to streamline deliveries and reduce returns. Quick commerce (qcom) platforms, which are now expanding into instant delivery of fashion and apparel, are also leveraging the technology to minimise mismatches and improve the overall customer experience.

 

For instance, Devendra Meel, chief business officer at qcom firm Zepto said, “Our fashion category offers detailed product descriptions, accurate imagery, and clear sizing aligned with brand charts, giving users a complete view before purchase. This helps reduce friction and makes the shopping experience more reliable.” Meel added that in several categories, users can request returns or exchanges within a three-day window on eligible items.

 

Amazon Now, the qcom arm of Amazon, also allows customers to initiate returns through their account for eligible items within the specified return window.