Data Mining for Managers: How to Use Data (Big and Small) to Solve Business Challenges
- 4h 11m
- Richard Boire
- Palgrave Macmillan Ltd
- 2014
In today's business and marketing worlds, there's big talk about big data.
As companies' capacities to amass information continue to grow and improve, the process of mining the data has become more and more vital. All are abuzz about data mining's importance and potential—and for good reason—but the field is still in its infancy, and there's an urgent need to spread and grow the skills, know-how, and strategies to best optimize data mining's results.
In Data Mining for Managers, industry-veteran Richard Boire provides streamlined insights and techniques for making the most of the masses of information and mining techniques that technology has enabled. Chock-full of engaging stories and case studies involving some of the world's top companies, Data Mining for Managers sets itself apart in more ways than one. A guide for business managers who need to understand the concepts of data mining as well as the potential it has for providing strategic guidance, Boire delivers a uniquely simple, 4-step process for identifying when data mining is the appropriate tool and then designing, implementing, and measuring your mining. Through hands-on analysis of best practices, Data Mining for Managers demonstrates how to interpret your results into actionable learning and target your mining to achieve appropriate business solutions—solutions that lead directly to optimized customer ROI and other tangible results. Boire also takes pains to outline the common pitfalls of data mining and detail vital approaches for sidestepping them. Among other warnings, he advises managers about the investment in intellectual capital required for effective data mining, urging them against focusing solely on technology and unenlightened, de-contextualized numbers analysis.
Data Mining for Managers is a book for marketers, IT professionals, analysts, and anyone else who wants to ride the revolution of big data—not just get swept along by it. It's an invaluable handbook for those looking to learn more about how to convert data mining into actionable insights and business solutions.
About the Author
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.
In this Book
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Introduction
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Growth of Data Mining—An Historical Perspective
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Data Mining in the New Economy
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Using Data Mining for CRM Evaluation
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The Data Mining Process: Problem Identification
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The Data Mining Process: Creation of the Analytical File
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Data Mining Process: Creation of the Analytical File with External Data Sources
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Data Storage and Security
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Privacy Concerns Regarding the Use of Data
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Types and Quality of Data
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Segmentation
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Applying Data Mining Techniques
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Gains Charts
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Using RFM as One Targeting Option
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The Use of Multivariate Analysis Techniques
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Tracking and Measuring
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Implementation and Tracking
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Value-Based Segmentation and the Use of Chaid
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Black Box Analytics
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Digital Analytics: A Data Miner's Perspective
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Organizational Considerations: People and Software
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Social Media Analytics
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Credit Cards and Risk
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Data Mining in Retail
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Business-to-Business Example
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Financial Institution Case Study
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Using Marketing Analytics in the Travel/Entertainment Industry
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Data Mining for Customer Loyalty: A Perspective
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Text Mining: The New Data Mining Frontier
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Analytics and Data Mining for Insurance Claim Risk
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Future Thoughts: The Big Data Discussion and the Key Roles in Analytics
