BlinkIt was started with the concept of a re-inventing the B2B marketplace in order to facilitate buyer and product discovery. To do this we created a single visual-based network of retailers and manufacturers that includes elements that are familiar to traditional and new generation buyers combined with an intuitive predictive model that “match-makes” between buyers and vendors; Speed Dating for Product Sourcing.

By matching preferences, needs, and locations based upon automated criteria and predictive modeling, the connection experience for both sides of the transaction is enhanced. Blinkit is a visual social network with elements of a dating site for vendors and buyers. To eliminate the white noise that exists with sites such as Alibaba.com and ETSY.com, BlinkIt uses a proprietary algorithm that intuitively matches products to retailers. The algorithm then filters the best matches to the buyers, i.e. acting as a virtual buying assistant.

This predictive matchmaking algori...
BlinkIt was started with the concept of a re-inventing the B2B marketplace in order to facilitate buyer and product discovery. To do this we created a single visual-based network of retailers and manufacturers that includes elements that are familiar to traditional and new generation buyers combined with an intuitive predictive model that “match-makes” between buyers and vendors; Speed Dating for Product Sourcing.

By matching preferences, needs, and locations based upon automated criteria and predictive modeling, the connection experience for both sides of the transaction is enhanced. Blinkit is a visual social network with elements of a dating site for vendors and buyers. To eliminate the white noise that exists with sites such as Alibaba.com and ETSY.com, BlinkIt uses a proprietary algorithm that intuitively matches products to retailers. The algorithm then filters the best matches to the buyers, i.e. acting as a virtual buying assistant.

This predictive matchmaking algorithm is the core of Blinkit. The system is based on research of the BlinkIt founders and utilizes Jungian archetypes to correlate design characteristics to the “personalities” of the retail companies, vendors, buyers and products. The algorithm then filters the best matches to the buyers, i.e. acting as a virtual buying assistant. Conversely, vendors are recommended buyers that have natural customer base for the vendor’s products. This product/buyer matching is the secret sauce for BlinkIt.
Unlike many predictive models, we refer to a “Baseline” for our typology. This means that the Blinkit system does not rely on historical or relational data but refers every decision to a master type. The importance of this is two-fold. One, the system can accurately filter from first use and two, we do not rely on “other people’s” choices. By baselining our Archetypes, we eliminate the erroneous cause and effect model of systems such as Amazon.

The applications of the BlinkIt algorithm goes beyond better filtering and predictive product recommendations in the B2B market. As Blinkit develops, it is the full intent to provide predictive sourcing to other services and industries to streamline the discovery process, specifically into intuitive advertising and predictive product placement in the B2C market, i.e. Google Ads.
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UpTech, Inc.
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UpTech, Inc. Top 30 in U.S. tech accelerator program for data-driven startups.