In October 1995, the phrase “fashionista” burst onto the national scene in an Associated Press article calling fashionistas “the fashion mafia”—people who fly to New York each year for Fashion Week.In a follow up story days later, The Washington Post added “(A fashionista is) the cranky son-of-a-gun in the Gucci loafers who wields clout and attitude like a silver stiletto.”
As women around the world started connecting with each other using social networks such as Facebook and Pinterest, the influence around fashion became more democratic.
Women began to share their own opinions and could now follow and inspire one another.
The term “fashionista” grew to be more positive—and reflected anyone who influenced fashion through sharing their own style and opinions. “Fashionista” was mentioned 4 times in 1995 in major media, and more than 2,600 times so far in 2012 (according to Factiva).
Algorithms vs. Inspiration
In some areas of our life (what movies to watch, or books to read, electronics to buy…), technology or algorithms have helped us make decisions, but in the fashion world algorithms don’t seem to be enough. Fashionistas want to inspire and be inspired by others.
Startups enabling the fashion discovery have a new way of thinking. Instead of focusing resources on building the perfect algorithm (to say “if similar users like item X you might also like item Y because you have similar preferences”) they focus on providing the infrastructure so that users are empowered to find their natural recommenders. The platform provides the means, users get the goal.
With these new platforms, people and data are classified naturally by the social network mechanisms (followers, leaders, popularity ranking, automatic categories, user-created categories like Pinterest boards, etc.) and rewarding schemas for active users.
The 4 Groups Of Social Fashion Startups Enabling Discovery
Internet entrepreneurs have largely ignored the fashion industry, but this has started to change. As fashion brands release more online metadata of their products (images, descriptions, tags, prices and links to buy the products), it is becoming more obvious that fun and useful services can be built on top of that data.
These new services are raising people’s scarce attention and beginning to change the way we make decisions. Some examples are:
1. Personal inspiration: At Chicisimo.com we are building a social fashion community of girls that inspire each other, sharing pictures of what they wear daily. Using gamification techniques and different types of categorization, we try to enable you to find the people who will inspire you. Go Try It On and Fashism also operate in this space. Fashion bloggers have proven to be an incredibly effective fashion recommendation tool.
2. Aggregation and curation: Another interesting group of startups, like Lyst.com, Stylight.de or Nuji.com, combine automatic aggregation and user curation. Here, users can interact with previously aggregated products and build their profile of favourite products. Their friends can then discover new fashion products that other users like.
3. User-generated content: Other startups (such as Polyvore or Snapette) follow a similar approach, but content is submitted by the community, instead of automatically aggregated. In most cases, here and in the examples above, the pattern that you can see is the birth of a new cataloguing system: user-catalogued. It does not matter how a company catalogues content, what matters is to be part of the catalogues (boards, sets…) created by users in their respective social networks.
4. Discovering young designers: A fourth type of startups is represented by Look.com and Muuse.com, who help fashion lovers discover young, emerging designers. Designers publish their new design and then the discovery process takes place.
Others in the fashion industry use algorithm-based traditional recommendation vendors, that have proven effective in other sectors, see a Nordstrom product page as an example. Even some startups are taking the algorithm approach. Chic Engine for example has developed a visual search engine to help you find “similar items.”
In fashion more than in other sectors, people look at people to receive inspiration and discover new products, instead of trusting machines or industry experts. And you… what other fashion startups do you think are changing the way we make decisions?
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