AI start-ups tackle retail's 'silent killer': costly online returns

A surge of AI start‑ups is targeting one of retail’s most persistent profit drains: online returns. Industry data show returns remain a multibillion‑dollar challenge for retailers, prompting a wave of technology solutions that aim to reduce reverse logistics and margin erosion.

Start‑ups are concentrating on two practical levers: augmented reality and machine‑learning‑driven virtual try‑on, and AI models that predict correct sizing from purchase and returns history. Pilots from major marketplaces and brands report lower return rates where these tools are implemented, while new vendors offer modular APIs to speed retailer adoption.

The market impact is direct. High return rates raise operating and processing costs and often force markdowns for resold inventory, squeezing gross margins. National-level reports underscore that online purchases return at notably higher rates than in‑store sales, making prevention via front‑end technology a more attractive ROI than solely optimising reverse logistics.

Beyond profitability, the trend intersects with sustainability goals: fewer returns mean less transport, packaging waste and lower carbon from reverse shipments. Retailers argue that investments in accurate fit recommendations and immersive product previews yield both financial and environmental benefits through reduced waste and smarter inventory use.

Analysts expect technology budgets to prioritise return‑reduction tools and better customer experience features in the near term. The winners will be solutions that combine model accuracy with easy integration into commerce stacks; successful scale‑ups could deliver measurable margin recovery for retailers while lowering the sector’s environmental footprint.