Customer and Business Analytics : Applied Data Mining for by Daniel S. Putler
By Daniel S. Putler
Annotation
Customer and company Analytics: utilized information Mining for enterprise choice Making utilizing R explains and demonstrates, through the accompanying open-source software program, how complex analytical instruments can handle a variety of enterprise difficulties. It additionally provides perception into the various demanding situations confronted whilst deploying those instruments. generally classroom-tested, the textual content is perfect for college students in patron and company analytics or utilized info mining in addition to execs in small- to medium-sized enterprises. The booklet bargains an intuitive knowing of the way diversified analytics algorithms paintings. the place worthwhile, the authors clarify the underlying arithmetic in an obtainable demeanour. each one process awarded contains a precise educational that permits hands-on event with genuine facts. The authors additionally speak about matters usually encountered in utilized info mining initiatives and current the CRISP-DM approach version as a realistic framework for organizing those tasks. exhibiting how facts mining can increase the functionality of agencies, this publication and its R-based software program give you the abilities and instruments had to effectively enhance complicated analytics capabilities. Read more...
summary: Annotation
Customer and company Analytics: utilized information Mining for enterprise selection Making utilizing R explains and demonstrates, through the accompanying open-source software program, how complicated analytical instruments can tackle a number of company difficulties. It additionally offers perception into the various demanding situations confronted whilst deploying those instruments. largely classroom-tested, the textual content is perfect for college kids in purchaser and company analytics or utilized info mining in addition to pros in small- to medium-sized businesses. The e-book deals an intuitive knowing of ways various analytics algorithms paintings. the place precious, the authors clarify the underlying arithmetic in an obtainable demeanour. each one strategy provided encompasses a special instructional that permits hands-on event with genuine info. The authors additionally speak about matters usually encountered in utilized info mining initiatives and current the CRISP-DM procedure version as a realistic framework for organizing those initiatives. displaying how info mining can increase the functionality of organisations, this booklet and its R-based software program give you the abilities and instruments had to effectively enhance complex analytics features
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Extra info for Customer and Business Analytics : Applied Data Mining for Business Decision Making Using R
Example text
Because of this twin heritage, a large number of different definitions of data mining have been put forward. Probably the most widely used definition of data mining comes from The Gartner Group (Krivda, 1996): Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of 10 Customer and Business Analytics data stored in repositories and by using pattern recognition technologies as well as statistical and mathematical techniques. This definition of data mining flows more from the database and machine learning tradition.
Before doing this, however, we provide some historical context on data mining process models in general and CRISP-DM in particular. 1 History and Background Business-oriented data mining only started to become something of an organized field in the early to mid-1990s. Very quickly, organizations developing internal database marketing and data mining capabilities, data mining consultants, and software vendors selling data mining tools came to realize there was a need to systematically organize the process of data mining.
The intervening 40 years has seen an ongoing revolution in the information systems used by companies. The lowering of computing and data storage costs have been the driving force behind this, making it economically feasible for firms to implement transactional databases, data warehouses, customer relationship management systems, point of sales systems, and the other software and technology tools needed to gather and manage customer information. In addition, a large number of firms have created loyalty and other programs that their customers gladly opt into that, in turn, allows these firms to track the actions of individual customers in a way that would otherwise not be possible.