WantList App Brings Tinder-Like Experience To Mobile Shopping

Styloko, a London-based online fashion retailer, launched the new iOS app WantList yesterday (June 4), providing users with a Tinder-like experience when it comes to their decision to buy or not.

In the company’s announcement, the app is described as playing to “the strengths of each device, presenting products in a simple, intuitive way. Proprietary visual product matching technology also frees the user from having to describe her shopping hunt – it’s as simple as swiping through items that look similar.”

On both the iPhone and iPad, the app recognizes a user’s swipes (either left or right), to personalize the shopping experience by analyzing tastes and preferences and then presenting them with relevant alternatives. Users can follow the WantLists of others and track specific sales and deals.

The app takes on a stripped-down affect on the Apple Watch, presenting only the product photos, which can then be swiped according to the customer’s preference. Sales alerts and other notifications will also be introduced. Styloko’s approach to its WantList app for Apple Watch may open the door to other retailers getting creative about how their customers can potentially utilize the watch for shopping.

“The launch of The WantList marks a strategic increase in focus for the company toward helping shoppers find what they are looking for wherever they are and on whatever device,” the release continued.

Styloko reports that mobile makes up more than 50 percent of the traffic coming to its site and the sites of its merchant partners. Creating an app that can work seamlessly on various mobile devices may put the retailer in a good position to capitalize on potential for users to circle back to the desktop site after using the app.

“In general, the most exciting thing for us is that we have been noticing a user journey that goes from discovery on mobile to the ultimate purchase on desktop,” Styloko co-founder Ivailo Jordanov explained to TechCrunch. “Where we think we are different is the fact that the user decides what they like and what they don’t on the go, so they never need to look at the same products again, but that also feeds our recommendation algorithms and enables us to recommend more relevant products each time,” he said.

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