Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.
The leading introductory book on data mining, fully updated andrevised! When Berry and Linoff wrote the first edition of Data MiningTechniques in the late 1990s, data mining was just starting tomove out of the lab and into the office and has since grown tobecome an indispensable tool of modern business. This newedition—more than 50% new and revised— is asignificant update from the previous one, and shows you how toharness the newest data mining methods and techniques to solvecommon business problems. The duo of unparalleled authors shareinvaluable advice for improving response rates to direct marketingcampaigns, identifying new customer segments, and estimating creditrisk. In addition, they cover more advanced topics such aspreparing data for analysis and creating the necessaryinfrastructure for data mining at your company. Features significant updates since the previous edition andupdates you on best practices for using data mining methods andtechniques for solving common business problems Covers a new data mining technique in every chapter along withclear, concise explanations on how to apply each techniqueimmediately Touches on core data mining techniques, including decisiontrees, neural networks, collaborative filtering, association rules,link analysis, survival analysis, and more Provides best practices for performing data mining using simpletools such as Excel Data Mining Techniques, Third Edition covers a new datamining technique with each successive chapter and then demonstrateshow you can apply that technique for improved marketing, sales, andcustomer support to get immediate results.
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.
"This book provides an in-depth analysis of attrition modeling relevant to business planning and management, offering insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytic tools"--Provided by publisher.
Special Features: · Best-in-class data mining techniques for solving critical problems in all areas of business· Explains how to pick the right data mining techniques for specific problems· Shows how to perform analysis and evaluate results· Features real-world examples from across various industry sectors· Companion Web site with updates on data mining products and service providers About The Book: Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with mining or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of database marketing managers.
Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult—often, because it’s so difficult to integrate new and legacy data sources. In Beyond Big Data, five of IBM’s leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM’s enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels. Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends
Increase profits and reduce costs by utilizing this collectionof models of the most commonly asked data mining questions In order to find new ways to improve customer sales and support,and as well as manage risk, business managers must be able to minecompany databases. This book provides a step-by-step guide tocreating and implementing models of the most commonly asked datamining questions. Readers will learn how to prepare data to mine,and develop accurate data mining questions. The author, who hasover ten years of data mining experience, also provides actualtested models of specific data mining questions for marketing,sales, customer service and retention, and risk management. ACD-ROM, sold separately, provides these models for reader use.
Companies and financial institutions are employing operational information systems in an efficient way. While they have consolidated a strong level of knowledge in management information systems, there is still a lack of knowledge on the right way to apply customer relationship management (CRM) systems under a business perspective. Most of the companies are still having problems in evaluating how CRM can meet with the expected results. The level of complexity is perceived both under a technological and organizational point of view. A complete innovation process and heavy change management initiatives should be ensured in order to have effective and successful systems. This book offers a solid theoretical and practical perspective on how to face CRM projects, describing the most appropriate technologies and organizational issues that have to be considered. Some explaining cases have been included as well.