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Strategic Analytics and SAS

Book Description

Use aggregate data to answer high-level business questions! Data miners, data scientists, analytic managers, and analysts who work in all industries will find the insights in Randy Collica's Strategic Analytics and SAS: Using Aggregate Data to Drive Organizational Initiatives invaluable in their work. This book shows you how to use your existing data at aggregate levels to answer high-level business questions. Written in a detailed, step-by-step format, the multi-industry use cases begin with a high-level question that a C-level executive might ask. Collica then progresses through the steps to perform the analysis, including many tables and screenshots to guide you along the way. He then ends each use case with the solution to the high-level question. Topics covered include logistic analysis, models developed from surveys, survival analysis, confidence intervals, text mining and analysis, visual analytics, hypothesis tests, and size and magnitude of analytic effects. Connect the dots between detailed data on your customers and the high-level business goals of your organization with Strategic Analytics and SAS!

Table of Contents

  1. Title Page
  2. Copyright
  3. About This Book
  4. About the Author
  5. Acknowledgments
  6. Chapter 1: Setting the Stage for Customer Strategic Analytics
    1. Introduction
    2. Basis for Aggregating Customer Data and Predictive Models
    3. The Difference between Tactical and Strategic Analytics
    4. Use Cases Described in This Book
    5. End Notes
  7. Chapter 2: Use Case 1: Loyalty Analytics via Promoter Surveys
    1. What Are Promoter Scores?
    2. Fusing Research Surveys with Customer Attributes
    3. Text Mining Call Center Account Notes/Chats
    4. Example: Turning Promoter Scores into an Analytic Predictive Model
    5. Deploying a Promoter Score Model on a Larger Customer Base
    6. Econometric Business Models from Promoter Score Aggregations
    7. Strategic Analytics from Econometric Models
    8. References for Further Reading
  8. Chapter 3: Use Case 2: Revenue Risk as a Customer Event
    1. Capturing Customer Revenue Patterns as Events
    2. Concepts of Customer Event Histories
    3. Survival – The Long and Short of It
    4. How to Set Up Survival Data Prep for Survival Mining
    5. Predicting Customer Probability of Revenue Risk
    6. Aggregation and Further Analytics
    7. Transform and Stationary
    8. References and Further Reading
  9. Chapter 4: Use Case 3: Health Care Adverse Events
    1. Overview of the Adverse Events Problem
    2. Exploratory Work with the VAERS Data
    3. Predicting Adverse Events
    4. Aggregation and Further Analysis
    5. Defining Strategic Outcomes
    6. References
  10. Chapter 5: Envisioning Strategic Analytics
    1. Understanding Data in a Visual Environment
    2. Envisioning Analytic Results for Strategic Planning
    3. Putting It All Together
    4. Summing Up and Further Work
    5. Reference
  11. Index
  12. Additional Resources