Matthew Ingram of GigaOm writes Wednesday about how Bloomberg L.P. uses data about its users to recommend news of interest on its Bloomberg.com and BusinessWeek.com sites.
Ingram writes, “In an interview with Stacey Higginbotham, the Bloomberg executive described how he built a predictive-analytics team within the media conglomerate that could use the behavior patterns and usage patterns of the 20 million users who visit Bloomberg.com or BusinessWeek.com (which Bloomberg acquired last year) to make better recommendations about what topics or articles they might be interested in.
“The approach that most traditional news services take, Krim said — in which editors select and present the news that they think matters most to a generic reader — ‘doesn’t really scale very well.’ But by using analytical tools on the data about those web visitors and their reading patterns and usage, Krim said that Bloomberg can ‘present 20 million different views of that information.’ The company is also trying to take into account the differences in how users want to receive their news during the day, including whether they want content as text they can read on their laptop or mobile, or video they can watch, and so on.
“The company now collects over 100 data points for every page a reader loads, based on what they interact with, what time of day it is, etc. — more than a terabyte of data every day in aggregate, Krim said — and the team has 15 different algorithms running in parallel to make recommendations for what that reader might want to see next.”
Read more here.
