Furkat Kasimov’s fix for e-commerce returns: Sell confidence, not guesswork

For ecommerce operators, returns have become a structural profit leak that hits in three ways: shipping and processing costs, inventory stuck in limbo, and the long-term damage to trust that lowers repeat purchase rates. The scale is significant. Retailers estimate that 15.8 percent of annual sales will be returned in 2025, totaling approximately $849.9 billion, according to the National Retail Federation and Happy Returns.

Furkat Kasimov, founder of GetSelene.ai, argues that many returns are created upstream by marketing that forces customers to guess. His company builds AI-driven one-to-one personalization for ecommerce remarketing and retention, using customer signals to adjust what each shopper sees in emails and advertisements.

Kasimov notes that customers rarely return items because they are defective. Instead, returns often occur when the product does not match the mental image marketing created.

When shoppers cannot judge fit with confidence, many resort to “bracketing,” ordering the same item in multiple sizes and returning those that do not work. For merchants, this effectively turns fulfillment into a paid fitting room: inventory is tied up, refunds strain cash flow, and returned goods often return to the market at a discount.

This behavior is far from a niche. NRF reporting identifies bracketing as a meaningful and growing contributor to returns.

Most brands rely on a default “hero” image: a young model wearing a sample size. While this image captures attention, it does little to help returning customers judge fit. When the creative does not align with the shopper’s reality, customers compensate by bracketing or guessing, both of which increase returns.

Kasimov is direct. Showing a size-Large customer a model wearing a size-Small removes the shopper’s ability to assess fit. The outcome is predictable: the customer either buys the wrong size or brackets to reduce risk. Both paths drive returns and reduce lifetime value.

Kasimov frames the challenge as a problem of data and creative orchestration. Repeat customers provide the strongest signal: purchase history, including the size they kept. When combined with broad attributes, such as estimated age range, brands can automatically tailor remarketing creative.

The result is simple but difficult to execute manually at scale. Shoppers see imagery and messaging that mirrors their own experience. A size-Large shopper within a particular age bracket sees a model in that age range wearing a Large. According to Kasimov, this clarity reduces bracketing and lowers returns.

Fewer preventable returns also mean less inventory trapped in reverse logistics, fewer forced discounts, and more repeat purchases from customers who trust the brand’s representation.

Kasimov’s central argument is that most brands treat returns as a logistics problem, attempting to patch the issue with faster processing or cheaper labels. These measures may help at the margins, but they do not stop returns from occurring. The leverage point lies earlier, at the customer’s decision point.

In a market where acquisition costs are rising and loyalty is increasingly difficult to secure, preventing avoidable returns is one of the few improvements that simultaneously boosts multiple metrics: margin, inventory health, and lifetime value. The brands that succeed will be the ones that give customers fewer reasons to return in the first place.

This article was written in cooperation with Furkat Kasimov