Expand users’ purchasing potential by educating them about their individual best colors

When comparing brick & mortar to online-stores, there’s clearly one thing missing in the shopping experience: shopping assistance, personal consultation, styling advice. Recommendation Enginges, AI-powered suggestions and sizing/fitting-tools have come a long way, but still are lacking one thing: experience advice instead of guess-work. Stylerisers tech is here to fill this gap and complete the “digital shopping assistant”

There have been a lot of efforts replicating the personal advice of the shopping assistant online:

  • You may also like:(random products)
  • Other users, who bought this, were also interested in that …
  • Based on your purchase history, we think, you’re interested in this or that …
  • What we know from your browsing history, this products most likely interrest you …
  • We’ve curated the best accessory fitting to this dress …

These suggestions, being technically sophisticated or not, trying to predict & assume what the user wants. In the end, it’s still a guess. Sometimes, this engines get so crazy, in the end they loose relevance for the users situation: i.e. imagine seeing ads for a product you already bought days ago.

Styleriser brings a big leap towards the “virtual shopping assistance”.

Shoppers nowadays want more than just AI guessing their preferences. With a continously decreasing attention span on your mostly mobile users, your recommendations need to be on point. Styleriser delievers the far missing element of “experience advice”, to come as close to a personal advice as can be.

Our technology delivers color-suggestions, based on the users themselfes. From a selfie, being uploaded by the user, certain sample pixels are taken and analyzed based on a scientific approach and years of experience in color consultancy. This real data from a real user, opening up a whole new world for recommendations & advice.

The best of all worlds

The area of 'recommended colors' is especially difficult for AI-engines: The recommendations are based on the users activity & purchase history. While this may reflect the users taste regarding type of clothing (i.e. liking sweaters better then hoodies) or preferences for certain brands, it can only reflect colors that the user thinks that fit.

Styleriser solution suggestions facial analysis

Creating a different shopping experience

We developed a digital shopping assistant using facial analysis (AI) to generate highly relevant product suggestions for apparel e-commerce.