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Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

Book Description

This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. Today’s businesses increasingly use data to drive decisions that keep them competitive. Especially with the influx of big data, the importance of data analysis to improve every dimension of business cannot be overstated. Data analysts are therefore in demand; however, many hires and prospective hires, although talented with respect to business and statistics, lack the know-how to perform business analytics with advanced statistical software. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner is a beginner’s guide with clear, illustrated, step-by-step instructions that will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. This book is part of the SAS Press program.

Table of Contents

  1. About This Book
  2. About the Author
  3. Chapter 1: Defining the Business Objective
    1. Introduction
    2. Setting Goals
    3. Descriptive Analyses
      1. Customer Profile
      2. Customer Loyalty
      3. Market Penetration or Wallet Share
    4. Predictive Analyses
      1. Marketing Models
      2. Risk and Approval Models
      3. Predictive Modeling Opportunities by Industry
    5. Notes from the Field
  4. Chapter 2: Data Types, Categories, and Sources
    1. Introduction
    2. The Evolution of Data
    3. Types of Data
      1. Nominal Data
      2. Ordinal Data
      3. Continuous Data
    4. Categories of Data
      1. Demographic or Firmographic Data
      2. Behavioral Data
      3. Psychographic Data
      4. Data Category Comparison
    5. Sources of Data
      1. Internal Sources
      2. Storage of Data
      3. External Sources
    6. Notes from the Field
  5. Chapter 3: Overview of Descriptive and Predictive Analyses
    1. Introduction
    2. Descriptive Analyses
      1. Frequency Distributions
      2. Cluster
      3. Decision Tree
    3. Predictive Analyses
      1. Linear Regression
      2. Logistic Regression
      3. Neural Networks
    4. Modeling Process
      1. Define the Objective
      2. Develop the Model
      3. Implement the Model
      4. Maintain the Model
    5. Notes from the Field
  6. Chapter 4: Data Construction for Analysis
    1. Introduction
    2. Data for Descriptive Analysis
    3. Data for Predictive Analysis
      1. Prospect Models
      2. Customer Models
      3. Risk Models
    4. External Sources of Data
    5. Notes from the Field
  7. Chapter 5: Descriptive Analysis Using SAS Enterprise Guide
    1. Introduction
    2. Project Overview
    3. Project Initiation
    4. Exploratory Analysis
      1. Importing the Data
      2. Viewing the Data
      3. Exploring the Data
    5. Segmentation and Profile Analysis
    6. Correlation Analysis
    7. Notes from the Field
  8. Chapter 6: Market Analysis Using SAS Enterprise Guide
    1. Introduction
    2. Project Overview
    3. Market Analysis
      1. Project Initiation
      2. Data Preparation
      3. Penetration and Share of Wallet
      4. Results
    4. Notes from the Field
  9. Chapter 7: Cluster Analysis Using SAS Enterprise Miner
    1. Introduction
    2. Project Overview
    3. Cluster Analysis
      1. Initiate the Project
      2. Input the Data Source and Assign Variable Roles
      3. Transform Variables
      4. Filter Data
      5. Build Clusters
      6. Build Segment Profiles
      7. Analyze Clusters and Recommend Marketing or Product Development Actions
    4. Notes from the Field
  10. Chapter 8: Tree Analysis Using SAS Enterprise Miner
    1. Introduction
    2. Project Overview
    3. Decision Tree Analysis
      1. Initiate the Project
      2. Input the Data Source
      3. Create Target Variable
      4. Partition the Data
      5. Build the Decision Tree
      6. View the Decision Tree Output
      7. Interpret the Findings
      8. Alternate Uses for Tree Analysis
    4. Notes from the Field
  11. Chapter 9: Predictive Analysis Using SAS Enterprise Miner
    1. Introduction
    2. Select
      1. Initiate the Project
      2. Select the Data
    3. Explore
      1. StatExplore
      2. MultiPlot
    4. Modify
      1. Replace Missing Values via Imputation
      2. Partition Data into Subsamples
      3. Manage Outliers
      4. Transform the Variables
    5. Model
      1. Decision Tree
      2. Neural Network
      3. Regression
    6. Assess
    7. Notes from the Field
  12. References