By Daniel S. Putler
Customer and company Analytics: utilized facts Mining for company selection Making utilizing R explains and demonstrates, through the accompanying open-source software program, how complicated analytical instruments can tackle quite a few enterprise difficulties. It additionally supplies perception into a number of the demanding situations confronted while deploying those instruments. widely classroom-tested, the textual content is perfect for college kids in shopper and company analytics or utilized information mining in addition to pros in small- to medium-sized corporations. The booklet bargains an intuitive figuring out of ways varied analytics algorithms paintings. the place valuable, the authors clarify the underlying arithmetic in an obtainable demeanour. each one strategy awarded incorporates a designated educational that permits hands-on event with genuine information. The authors additionally talk about concerns frequently encountered in utilized information mining tasks and current the CRISP-DM method version as a pragmatic framework for organizing those tasks. exhibiting how facts mining can increase the functionality of businesses, this e-book and its R-based software program give you the abilities and instruments had to effectively strengthen complex analytics capabilities. Read more...
Customer and enterprise Analytics: utilized information Mining for company determination Making utilizing R explains and demonstrates, through the accompanying open-source software program, how complex analytical instruments can handle a number of company difficulties. It additionally supplies perception into the various demanding situations confronted whilst deploying those instruments. largely classroom-tested, the textual content is perfect for college kids in buyer and company analytics or utilized facts mining in addition to pros in small- to medium-sized corporations. The booklet deals an intuitive figuring out of the way diverse analytics algorithms paintings. the place precious, the authors clarify the underlying arithmetic in an obtainable demeanour. every one process awarded incorporates a distinctive educational that permits hands-on event with actual info. The authors additionally speak about matters usually encountered in utilized information mining initiatives and current the CRISP-DM method version as a pragmatic framework for organizing those tasks. exhibiting how info mining can increase the functionality of corporations, this ebook and its R-based software program give you the abilities and instruments had to effectively advance complex analytics features
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Extra info for Customer and Business Analytics : Applied Data Mining for Business Decision Making Using R
Properly done, modeling improves our understanding, our ability to communicate, and our decision-making. 2 Classes of Data Mining Methods Ultimately, data mining uses a set of methods that originated in either statistics or machine learning to summarize the available data. These different methods fall into two broad classes, grouping methods and predictive modeling methods. Within each of these two classes fall literally hundreds of different specific methods (also known as algorithms). In this section we will only mention the most commonly used methods for each of the two classes.
In their book The One to One Future, Peppers and Rogers (1993) provide the following definition of a customer database: A Customer Database is an organized collection of comprehensive data about individual customers or prospects that is current, accessible, and actionable for such marketing purposes as lead generation, lead qualification, sale of a product or service, or maintenance of customer relationships. In turn, Peppers and Rogers (1993) define database marketing in the following way: Database Marketing and Data Mining 5 Database Marketing is the process of building, maintaining, and using customer databases and other databases for the purposes of contacting and transacting.
For instance, a life insurance company that targets one of its current term life insurance policy holders in an effort to move that customer to a whole life policy would be engaged in an up-selling activity. Market basket analysis involves examining the composition of items in customers’ “baskets” on single purchase occasions. Given its nature, market basket analysis is most applicable to retailers, particularly traditional brick and mortar retailers. The goal of the analysis is to find merchandising opportunities that could lead to additional product sales.
Customer and Business Analytics : Applied Data Mining for Business Decision Making Using R by Daniel S. Putler