Our technology helps legacy retailers recover their main strength by providing professional digital advice.
Service quality or professional in-person assistance has been the biggest casualty of the rapid shift to digital shopping. Our technology helps legacy retailers to recover their main strength by providing professional digital advice. Recommendation Engines, AI-powered suggestions, and sizing/fitting tools have come a long way but still are lacking one thing: experience advice instead of guesswork. 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 …
- From what we know from your browsing history, this product most likely interests 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, these engines get so crazy, in the end, they lose 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 continuously decreasing attention span on your mostly mobile users, your recommendations need to be on point. Styleriser delivers the far missing element of “experience advice”, to come as close to personal advice as can be.
Our technology delivers color suggestions, based on the users themselves. 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 user's activity & purchase history. While this may reflect the user's taste regarding the type of clothing (i.e. liking sweaters better than hoodies) or preferences for certain brands, it can only reflect colors that the user thinks that fit.
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.
