Making online shopping a more social experience to aid discovery is a problem that many startups are busy trying to solve. Meet Nuji, a London-based startup and Seedcamp winner that launched their social shopping discovery service at this year’s LeWeb.
The idea behind Nuji is to fully tap into your social graph to discover products that you actually like, as apposed to products that your friends are pushing virally, either to tip a Groupon-style deal or for an affiliate kickback (see loved.by). It does this by asking users to connect to their Facebook account to start sharing products they love and then algorithmically recommends similar products based on matching tastes.
The service is available both on the web and for mobile devices. It basically allows you to tag products via a bookmarklet in your browser or by scanning a barcode or taking a photo of the product on the go. All items are synced in your personal stream of items and are shared with friends in real-time.
The interface is both slick and appealing. By building on top of a user’s Facebook-based social graph, the company is clearly counting on a viral effect and distribution via the user’s Facebook feed. At the moment Nuji is entirely free of charge and most probably they’ll work out a business model that entails featured products or by placing product recommendations for which they charge companies.
And unlike loved.by, the lack of an affiliate monetization model could make Nuji less biased. Services like loved.by have the potential to become flooded with “fake recommendations” because there’s the opportunity of a quick buck waiting if your Facebook buddy buys the product you’ve recommended.
