by Darren Vengroff
Online product recommendation systems have been around for almost as long as e-commerce. They all share the common goal of recommending items a particular person is most likely to be interested in at a given time. In aiming for this common goal, however, recommendation systems take a wide variety of different approaches.
It’s Amazon’s golden goose: an aureate treasure chest perched on the top of almost every page. The feature, called the “Gold Box,” is a collection of items on sale. It’s one of the most trafficked areas on the site and consistently brings many shoppers back to Amazon every day.
Employees say that what they like about working at Richrelevance is the collegial yet challenging environment that management fosters.
The 4-year-old company ranked No. 1 for employers of 25 to 50 workers and No. 4 overall as a workplace.
Fast-Growing Provider of Personalized Recommendations to World’s Largest Online Retailers Places First in 25-50 Employee Category
E-retailers are better than ever at identifying the type of customer visiting an e-commerce site by browsing and purchasing behavior. And, by comparing her behavior with that of many others, that means they can serve up ever-more-specific, personalized recommendations of products likely to prompt her to click and buy.
BestBuyEyeglasses.com uses the product recommendation engine of vendor Richrelevance, which allows the retailer to determine which items are most likely to appeal to a customer, and then adjust how those recommendations are presented according to what the retailer most wants a consumer to do.
Product recommendation is one of the most elusive–and potentially profitable–forms of merchandising online, where consumer behavior can bring in reams of data about the way people shop. But figuring out what to do with that data, and how to present what you learn, is a psychological challenge all its own.
Most e-commerce retailers are familiar with the science of product recommendations. richrelevance has created an addition to its SaaS platform called myrecs, which uses more data to recommend more products per page, and updates customer information to recommend those products in real time.