You probably navigated to a home page that featured a dozen or so items, only one or two of which you may have actually been interested in.

Why would I want to buy another style of winter jacket if I already bought one for the season?

Luckily Breinify, an e-commerce recommendation engine launching at TechCrunch Disrupt SF 2016, thinks they have a better way.

Instead of offering static recommendations based on things like past purchases and browsing behavior, Breinify combines a user s demographics preferences, current interests and immediate intents to figure out exactly what they want to buy and when they want to buy it.

The first part is based on user behavior and comes from actions like which products they view and which links they click on a retailer s website.

The second part, data from across the web, is collected by Breinify crawling the web for a shopper s different social identities on sites like Linkedin and Twitter.

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