Data Mining for Managers - R. Boire

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Résumé :
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.

Biographie:
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....

Sommaire:
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