A new recommendation engine promises to boost e-tailers’ sales by asking shoppers what they are looking for, rather than making suggestions based on their browsing history.

For example, users looking for black dresses are asked if they prefer a cocktail or day dress, or if they want sleeves. Then they receive three choices fitting their stated parameters. Shoppers prompted to use the quiz generated 22% more sales, according to Shopping Quizzes, which built the engine.

While investing in these site features can be costly for e-tailers, such investments could dramatically pay off. Nearly half of consumers are more likely to shop a site that offers personalized recommendations. With 49% of U.S. retail sales taking place online, those recommendations can have a major impact on a company’s bottom line.

The North Face is one retailer recognizing that fact. The brand created its own AI commerce platform last year, a savvy investment that helped it gain the number-two spot in L2’s Digital IQ Index: Activewear. Like the Shopping Quizzes engine, the platform helps site visitors discover products based on their responses to a series of questions; 80% of testers said they would use it again.

The Personal Shopper

Buoyed by that success, the brand built a mobile app version that simulates a conversation with an in-store associate using the device’s microphone – the first retail mobile app powered by IBM Watson. As such efforts become increasingly sophisticated, providing consumers with the personal shopping experience they might be unable to attain in stores, e-commerce could become a more personalized alternative to physical retail.

 

 

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