Try | Clothes Before Buying

: 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.

: 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. try clothes before buying

: 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 : Papers like those found on Semantic Scholar

: 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

Research highlights that allowing customers to physically or virtually test clothing before committing to a purchase addresses several key psychological and logistical barriers:

: 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.

According to literature and industry analysis, there are two main ways this "try before buying" promise is fulfilled: