A Vertical Machine Learning approach provides a competitive moat around your business
VERTICAL MACHINE LEARNING IN FASHION
We at the Fashion Taste API follow a Vertical Machine Learning approach to fashion. We believe in the unique advantage of having a profound understanding of the consumer problems, end to end.
With a focus on the consumers, we’ve applied machine learning to automate the two processes that we’ve considered game-changing:
- Capturing shopper’s taste data (including what clothes they have in their closet);
- Building the data infrastructure that allows us to understand input automatically, and to deliver personalized output also automatically.
THE ADVANTAGE OF FASHION RETAILERS
We believe that fashion retailers have a clear advantage over newcomers: they are the domain experts. The specific benefits, however, need to be captured:
- Fashion retailers have a profound understanding of the fashion consumer;
- Shoppers generate a unique body of data around the retailers properties. Retailers can choose to start capturing that intelligence and build their moat;
- Physical stores are a unique layer of experimentation to test omnichannel solutions and receive fast feedback.
NEWCOMERS WILL SOON CATCH UP
At some point, newcomers will offer In-Bedroom Fashion Stylist or Smart Mirrors to end consumers.
Apart from the increase in market share that this new category will generate, In-Bedroom Fashion Stylists will have two very relevant consequences:
- Data: The amount of incoming taste data will grow exponentially;
- Learning: The speed of learning will accelerate thanks to better shopper feedback.
And the current competitive moat around traditional fashion retailers will be gone.
THE VIRTUOUS CYCLE OF DATA
The more digitally powered experiences you offer, the more taste data you will capture. The more data you capture, the better you can automatically serve your shoppers. The better you serve your shoppers, the more adrenaline you generate in your teams, the more ambitious they become.