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Monday, January 23, 2006

The Recommendation Engines & Ecommerce Success

The NYTimes has an excellent article about the recommendation engines and ecommerce success. Amazon.com extensively uses collaborative filtering and association software that helps the system to make recommendations to customers based on purchase/interests shown by the customer. Web technology capable of compiling vast amounts of customer data now makes it possible for online stores to recommend items tailored to a specific shopper's interests. Companies are finding that getting those personalized recommendations right - or even close - can mean significantly higher sales. For consumers, a recommendation system can either represent a vaguely annoying invasion of privacy or a big help in bringing order to a sea of choices. Walmart.com had a toughtime when its recommendation system made wrong recommendation – all based on bruteforce matching. Wal-Mart's trouble stemmed not from the aggressive use of advanced cross-selling technology, but from the near lack of it.
Companies with more nuanced strategies have avoided embarrassing linkages. NetFlix claims that roughly two-thirds of the films rented were recommended to subscribers by the site. Between 70 and 80 percent of NetFlix rentals come from the company's back catalog of 38,000 films rather than recent releases. NetFlix's recommendation system collected more than two million ratings forms from subscribers daily to add to its huge database of users' likes and dislikes. The system assigns different ratings to a movie depending on a particular subscriber's tastes. The company credits the system's ability to make automated yet accurate recommendations as a major factor in its growth from 600,000 subscribers in 2002 to nearly 4 million today. Netflix says that a key driver to their growth will be the superiority of their website design and proprietary algorithms. The personalization of their site is really what makes their service so unique. At this point Netflix has now collected over 1 billion ratings for moves. They use these ratings to make recommendations of longtail content for their consumersSimilarly, Apple's iTunes online music store features a system of recommending new music as a way of increasing customers' attachment to the site and, presumably, their purchases. Recommendation engines, which grew out of the technology used to serve up personalized ads on Web sites, now typically involve some level of "collaborative filtering" to tailor data automatically to individuals or groups of users. Liveplasma.com, an online site for music and, more recently, movies, graphically "maps" shoppers' potential interests. Interestingly technology is not the leveler here - large online stores are having success through recommendations, smaller web sites are having a more difficult time using the technology to their advantage –partly because one needs a lot of customer data to find patterns that can help in making right recommendations to customers. As I wrote earlier, heightened competition, mass usage and a variety of services all open up new range of offerings and opportunities in this increasingly felt experience economy.

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