R. Boire

Data Mining for Managers

How to Use Data (Big and Small) to Solve Business Challenges

• Gebundenes Buch

Produktbeschreibung

Big Data is a growing business trend, but there little advice available on how to use it practically. Written by a data mining expert with over 30 years of experience, this book uses case studies to help marketers, brand managers and IT professionals understand how to capture and measure data for marketing purposes.

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Produktdetails

• Verlag: Palgrave Macmillan / Palgrave Macmillan US / Springer Palgrave Macmillan
• Artikelnr. des Verlages: 978-1-137-40617-0
• 2014
• Seitenzahl: 242
• Erscheinungstermin: 30. Oktober 2014
• Englisch
• Abmessung: 244mm x 159mm x 2mm
• Gewicht: 502g
• ISBN-13: 9781137406170
• ISBN-10: 1137406178
• Artikelnr.: 41216867

Autorenporträt

Richard Boire's experience in database marketing and predictive analytics dates back to 1983, when he received an MBA from Concordia University in Finance and Statistics. His initial experience at organizations such as Reader's Digest and American Express allowed him to become a pioneer in the application of predictive modeling technology for all direct marketing programs. This extended to the introduction of models, which targeted the acquisition of new customers based on return on investment. With this experience, Richard formed his own consulting company back in 1994. Now called the Boire Filler Group, it has grown to become a Canadian leader in offering analytical and database services to companies seeking solutions to their existing predictive analytics or database marketing challenges. Richard is a recognized authority on predictive analytics and is among the top five experts in this field in Canada. This expertise has evolved into international speaking assignments and workshop seminars in the U.S., England, Eastern Europe, and Southeast Asia. He has also chaired numerous conferences on this topic within Canada and is the current Predictive Analytics World Conference Chair within Canada. Richard also writes numerous articles for industry publications and has worked closely with the Canadian Marketing Association in a number of areas including Education and the Database and Marketing Intelligence Committee.

Inhaltsangabe

1. Introduction 2. Growth of Data Mining and the Data Miner 3. Using Data Mining for CRM Evaluation 4. The Data Mining Process 5. Identification of the Business Problem or Challenge 6. Creation of the Analytical File 7. Creation of the Analytical File and Pivot Table 8. Using External Data Sources 9. Data Storage and Security 10. Privacy Concerns Regarding the Use of Data 11. Types and Quality of Data 12. Segmentation 13. Applying Data Mining Techniques 14. GAINS CHARTS 15. Using RFM as one Targeting Option 16. The use of Multivariate Analysis Techniques 17. Using data mining for identifying actions vs. targeted segments/lists 18. Tracking and Measuring 19. Implementation and Tracking 20. Developing an Overall Segmentation Strategy 21. Black Box Analytics 22. Digital Analytics- A Data Miner's Perspective 23. Data Mining Direction and Trends 24. Social Media Analytics 25. Embedding Predictive Analytics into the Corporate Culture

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1. Introduction 2. Growth of Data Mining and the Data Miner 3. Using Data Mining for CRM Evaluation 4. The Data Mining Process 5. Identification of the Business Problem or Challenge 6. Creation of the Analytical File 7. Creation of the Analytical File and Pivot Table 8. Using External Data Sources 9. Data Storage and Security 10. Privacy Concerns Regarding the Use of Data 11. Types and Quality of Data 12. Segmentation 13. Applying Data Mining Techniques 14. GAINS CHARTS 15. Using RFM as one Targeting Option 16. The use of Multivariate Analysis Techniques 17. Using data mining for identifying actions vs. targeted segments/lists 18. Tracking and Measuring 19. Implementation and Tracking 20. Developing an Overall Segmentation Strategy 21. Black Box Analytics 22. Digital Analytics- A Data Miner's Perspective 23. Data Mining Direction and Trends 24. Social Media Analytics 25. Embedding Predictive Analytics into the Corporate Culture

Rezensionen

"Business managers and decision makers have been in need of a book on data mining, and voila! This industry overview is unique in serving the needs of the consummate businessperson, differentiating it from the many introductions for would-be hands-on, technical practitioners. Boire has formed a conceptually rich and insightful compendium that delivers a pragmatic perspective on both the tactical and strategic value of data mining and predictive analytics." - Eric Siegel, founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

"Boire provides a straightforward and disciplined overview of the practice of data mining. Whether you are dealing with large or small data sets, tomorrow's business leaders will be the ones that extract the most value from their customer information. Boire leverages his extensive experience as a practitioner to help the reader take a measured approach while providing a unique view of data mining." - Bryan Pearson, President and Chief Executive Officer, LoyaltyOne

"Terms like 'analytics', 'data mining', and 'modeling' evoke fear in the heart of many a traditional marketer. Instead of focusing solely on techniques, Boire organizes his content around types of management decisions. In the process, he demystifies and explains the rationale and methods used in terms that anyone can understand. A must read for those getting started or looking to round out their expertise." - Kenneth B. Wong, Distinguished Professor of Marketing, Queens University, School of Business

"Data Mining for Managers offers a practical approach to finding data-driven solutions for real-life business challenges without intensive investment of money or resources. This is a must-have book for any business person who wants to use data more effectively to improve business results day in day out." - Anna Percey Dove, SVP Managing Partner, CRM & Customer Experience Cossette

"Boire's book, using practical examples, simplifies the complexity of interpreting vast amounts of data in a meaningful and practical approach through an easy to follow data mining framework." - Mary Lynn Manton, BTech, MEd, ISP, Co-Chair, School of Information and Communications Technology, Faculty of Applied Science and Engineering Technology, Seneca College of Applied Arts & Technology