document: Mining e-commerce data : the good , the bad , and the ugly Organizations conducting Electronic Commerce ( e-commerce ) can greatly benefit from the insight that data mining of transactional and clickstream data provides . Such insight helps not only to improve the electronic channel ( e.g. , a web site ) , but it is also a learning vehicle for the bigger organization conducting business at brick-and-mortar stores . The e-commerce site serves as an early alert system for emerging patterns and a laboratory for experimentation . For successful data mining , several ingredients are needed and e-commerce provides all the right ones ( the Good ) . Web server logs , which are commonly used as the source of data for mining e-commerce data , were designed to debug web servers , and the data they provide is insufficient , requiring the use of heuristics to reconstruct events . Moreover , many events are never logged in web server logs , limiting the source of data for mining ( the Bad ) . Many of the problems of dealing with web server log data can be resolved by properly architecting the e-commerce sites to generate data needed for mining . Even with a good architecture , however , there are challenging problems that remain hard to solve ( the Ugly ) . Lessons and metrics based on mining real e-commerce data are presented .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O