About

Magnette embeds fashion retail experiences in digital media such as tablet magazines.

Its core functionality is a fashion recommendation engine to help female consumers find products that fit their style and price point. Digital magazines, location-based applications, video clips and other media would leverage Magnette by integrating its functionality into the medium's content.

Scenario

In this scenario Magnette is embedded within a digital fashion magazine on a tablet computer. Users browsing through a magazine can click on images of items that they like. Clicking on an image triggers Magnette, which then displays thumbnails of all identified items in the image. The user can then click on a thumbnail to get detailed product information.

Alternatives

Product information such as brand, description, colors and price are displayed to the user, along with the option to buy the product online. The price and style of many products found in magazines may not be accessible to the average consumer, e.g. a designer dress worn by a celebrity may be too expensive or risque.

Magnette provides alternative products that may be more accessible and have a similar style to the original item. These recommendations are based on the reference product, the fashion profile of the user, past buying habits and crowd-sourced information. By providing alternative products from retailers like H&M and Gap, the style of the original product becomes more accessible.

Wishlist

Products that a user likes can be easily added to a wishlist. Magnette allows for multiple wishlists so that saved items can be categorized. These wishlists can be shared with individuals or made public.

Consumer's research for clothing is often done online, while the the majority of purchasing happens in stores. Wishlists can help bridge the online research with the in-store experience. Wishlists can act as a repository to compare items and collate a product list for a shopping trip.

Features

Mapping

Wishlists can be used to generate a shopping map. This map highlights the stores that have the items in a wishlist and creates the most efficient shopping route.

Social Networking

Wishlists can be shared with individual friends, family or made public. By sharing wishlists friends can compare their personal styles, organize group shopping trips, and easily find gifts for loved ones.

Crowd Sourcing

The data from Magnette, such as products referenced, selected and purchased, is collected and fed into the recommendation engine to continuously fine tune the algorithms.

Research

We interviewed a range of female consumers to understand the activities and behaviors surrounding fashion shopping. During the prototyping stage we tested paper prototypes to gauge the usability and intuitiveness of the interface.

Insights

• Popular media (e.g. magazines) can play an influential role in the formation of young women's personal style.
• People prefer not to buy clothing online; the fit and the feel of the material are important factors that cannot be judged online. Preliminary research is often done online with final purchase happening in the store.
• Shopping for clothing can be an important social bonding experience for women.