: Papers like those found on Semantic Scholar and ScienceDirect argue that TBYB programs (like Amazon's Prime Wardrobe) decrease functional, physical, and financial risks for consumers.
: Studies indicate that AI-driven virtual fitting rooms improve size accuracy and purchase confidence, which can significantly reduce fashion return rates for brands.
: Research on ResearchGate notes that trust and the ability to return items for refunds are critical "guarantees" that influence whether a customer will choose online shopping over physical stores. Key TBYB Implementation Models try clothes before buying
Research highlights that allowing customers to physically or virtually test clothing before committing to a purchase addresses several key psychological and logistical barriers:
Academic and industry papers exploring the "try before you buy" (TBYB) concept primarily focus on reducing and the "product-fit uncertainty" (PFU) that typically plagues online apparel shopping . Academic Perspectives on TBYB : Papers like those found on Semantic Scholar
: Tools like Google Shopping Try-On or the experimental Doppl app use generative AI to show how clothes look on a digital version of the user's actual body, rather than a generic model.
: Customers order multiple items (e.g., different sizes or styles) at no upfront cost, keep them for a trial period (typically 7 days), and are only charged for what they keep. This is extensively discussed as a strategy to mitigate PFU at no cost of shipping. This is extensively discussed as a strategy to
According to literature and industry analysis, there are two main ways this "try before buying" promise is fulfilled: