Redefining Outfit Selection

Choiced empowers style choices with real people, not algorithms.

What is this about?

By harnessing the power of crowdsourcing, Choiced is an app that simplifies and humanises outfit selection. First, users upload outfit photos and details about the occasion; then, they receive real-time feedback and suggestions from a global community of fashion enthusiasts.

A person holding assorted clothes in wooden hanger

Why is it interesting?

Choiced's social-media-driven approach provides diverse perspectives and reflects a shift toward collaborative decision-making in the digital age. VML's 'The Future 100:2024' report states, "Community and connection at scale are essential, with 67% of people agreeing that community is more important than one individual and 76% believing that technology helps bring people together."

Empowering users to make confident fashion choices with the support of a vibrant and engaged community places Choiced within our Future Code, Joyning. And its opportunity to Be a Companion - a space where brands are finding IRL and digital ways to become a catalyst for social connection.

So What?

The power of AI lies in its ability to aggregate solutions based on what's trending and most popular. However, it also lacks the human context and real-life expertise found through lived experiences. Tapping into real people's insights provides diverse experiences and expertise in a world dominated by bots, where genuine human reviews and recommendations reign supreme.

Just Imagine ...

A near future:

Where Nandos creates a platform where users can submit their recipe ideas for various chicken dishes, other users can vote on their favourite recipes, and the most popular ones will land on its national menu.

A mid future:

Where TRIP offers a national poll where consumers can suggest flavours, carbonation levels, and packaging designs, creating a crowd-curated lineup of fizzy and flavourful drinks.

A far future:

Where Hackney Gelato launches a pop-up ice-cream truck that travels around London and uses face recognition to identify the flavour's need state - happy, relaxed - to recommend to customers.