Mining Your Own Business In Banking Using Db2 Intelligent Miner For Data

The new challenge of integrated solutions is to get more knowledge from data in order to build the most valuable solutions. This IBM Redbooks publication is a solution guide to address the business issues in telecommunications by real usage experience and to position the value of DB2 Intelligent Miner for Data in a Business Intelligence architecture as an integrated solution. Typical telecoms issues are addressed in this book, such as: What are the characteristics of your customers? Can you predict the customers who are likely to leave? How do you discover the true value of your customers? This book also describes a data mining method to ensure that the optimum results are obtained. It details for each business issue: - What common data model to use - How to source the data - How to evaluate the model - What data mining technique to use - How to interpret the results - How to deploy the model Business users who want to know the payback on their organization when using the DB2 Intelligent Miner for Data solution should read the sections about the business issues, how to interpret the results, and how to deploy the model in the enterprise. Implementers who want to start using mining techniques should read the sections about how to define the common data model to use, how to source the data, and how to choose the data mining techniques.

The new challenge of integrated solutions is to get more knowledge from data in order to build the most valuable solutions. This IBM Redbooks publication is a solution guide to address the business issues in retail by real usage experience and to position the value of DB2 Intelligent Miner For Data in a Business Intelligence architecture. Typical retail issues are addressed in this book, such as: How can I characterize my customers from the mix of products that they purchase? How can I decide which products to recommend to my customers? How can I categorize my customers and identify new potential customers? This book also describes a data mining method to ensure that the optimum results are obtained. It details for each business issue: - What common data model to use - How to source the data - How to evaluate the model - What data mining technique to use - How to interpret the results - How to deploy the model Business users who want to know the payback on their organization when using the DB2 Intelligent Miner For Data solution should read the sections about the business issues, how to interpret the results, and how to deploy the model in the enterprise. Implementers who want to start using mining techniques should read the sections about how to define the common data model to use, how to source the data, and how to choose the data mining techniques.

This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

Through extensive case studies and examples, this book provides practical guidance on all aspects of implementing data mining: technical, business, and social. The book also demonstrates IBM's powerful new intelligent Miner tool and shows how it can be applied.