For a dropshipping company in the fashion industry, we developed an object detection and outfit transfer MVP that offers a personalized online shopping experience. The solution enables users to visualize outfits on their bodies, simulating how items will fit and appear, which addresses the common issue of unmet expectations upon delivery. This approach reduces return rates and enhances user engagement by making the online shopping experience more realistic and user-centered.
The technology stack included:
Creating this feature for the client required addressing key challenges:
Our team built a proof-of-concept (POC), which was then refined and deployed as a full feature on the client’s website. The implementation improved the shopping experience and achieved a 5% reduction in product return rates due to better alignment between customer expectations and product fit.
Fashion – specifically focused on enhancing e-commerce experiences in the dropshipping sector.
This solution positions our client as a forward-thinking brand in fashion e-commerce by addressing a key customer pain point: confidence in fit before purchase. Compared to traditional e-commerce sites, this feature promotes trust and reduces post-purchase dissatisfaction, giving our client a competitive edge.
Our remote team worked closely with the client to ensure the product’s success, contributing to a better user experience and lower return rates. This project demonstrates our ability to develop user-focused, data-driven features that align with both business goals and consumer needs.