Netflix created the $1 million Netflix Prize in 2006 as a way to reward developers of a next-generation film-rating prediction algorithm.
The idea sounded seductively simple: If developers could predict how users would rate a film, they could use that prediction to decide whether it makes sense to recommend the film to them or not.
The winner would be the first person to develop an algorithm capable of predicting user ratings of films at least 10 percent more accurately than Netflix’s internally developed Cinematch algorithm. Three years and 43,000 entries later — from more than 5,100 teams in 185 countries — a winner appears to have emerged. The winner will be officially announced this month, and it’s down to two teams.
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Ecommerce marketers need to optimize their product recommendations strategy. Simply offering suggestions isn’t enough to lift order value if those recommendations aren’t personalized and relevant for each customer. See how an online wine retailer built increased relevance into their product recommendations by considering users’ browsing and buying habits as well as logistical considerations, such as geographic region. Today, 10% of the site’s sales come from these recommendations, and the average value of those orders is 15% higher.
by Darren Vengroff
Netflix
created the $1 million Netflix Prize
The idea sounded seductively simple: If developers could predict how users would rate a film, they could use that prediction to decide whether it makes sense to recommend the film to them or not.
by Darren Vengroff
Online product recommendations have been around for almost as long as e-commerce itself. Most online retailers today offer some type of product recommendations on their sites, with research proving that presenting shoppers with items that fit their interests boosts sales and increases customer satisfaction. But the truth is, many retailers are not doing all they can with recommendations; they plunk them down on product pages, do little to monitor performance, cross their fingers and just hope the recommendations work to boost sales a bit. Often, there is an initial spike in conversion, but performance tends to fizzle out after a few months.
However, there are simple ways to make recommendations work better–proven tactics that not only drive a greater initial lift than standard recommendations, but also create a lasting and sustainable lift over time.
Have you noticed that, when showing cross-sells and upsells, many ecommerce sites hijack you off the page you’re looking at to view the suggested item, often with no way back to the other page without hitting “back?” Surely this is not the most usable way to suggest products and improve merchandising conversion rate.
Continue reading at Get Elastic
http://www.getelastic.com/display-product-recommendations/