December 11, 2015
E-commerce is currently booming but, despite that, it can sometimes be a tad complicated, with the overwhelming amount of possibilities and options that are available these days. Looking for a given product can prove to be almost impossible, as it can have multiple variations, it can be found in multiple outlets with prices floating a lot, and so on.
However, this can be something from the past, as a recently launched tool promises to give users a much improved online shopping experience. Developed by Searchub Inc., Searchub.com is an innovative e-commerce platform that uses machine learning algorithms to offer users a truly personalized shopping experience.
Using the power of recommendation and Big Data, Searchub is able to connect retailers to customers, personalizing results according to each user’s tastes, friends and style. The platform learns such information from a mix of sources, which consist in the users’ social profiles, an assortment of machine learning algorithms and the shopping behaviors manifested by the users.
This new platform empowers users with a functional, easy to use and visually appealing way of shopping online for their items, being accessible from any platform with an Internet connection. Launched on this year’s Cyber Monday, one of the biggest shopping days of the year, it has been tremendously successful thus far.
“Our engine uses high-level aggregate data about our users to match them with the products they really want,” said Mohamed Bennani, co-founder of Searchub. “If you like Kim Kardashian or red pumps, that data suggests you are also likely to like a certain type of products and brands. If not, then our machine learning powered engine will remember this for your next search.”
With a shopping index containing over 300 million products (from several different categories such as books or women clothing) across 10,000 stores, Searchub is built to help users in the task of finding the best deals, especially by making it simple to compare prices, coupons and tax information among different brands and stores.
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