Data Mining and Market Intelligence for Optimal Marketing Returns

Chapter 1: Introduction to Strategic Importance of Metrics, Marketing Research and Data Mining in Today's Marketing World * The Role of Metrics * The Role of Research * The Role of Data Mining * An Effective Eight-Step Process for Incorporating Metrics, Research and Data Mining into Marketing Planning and Execution - Step One: Identifying Key Stakeholders and their Business Objectives - Step Two: Selecting Appropriate Metrics tp Measure Marketing Success - Step Three: Assessing the Market Opportunity - Step Four: Conducting Competitive Analysis - Step Five: Deriving Optimal Marketing Spending and Media Mix - Step Six: Leveraging Data Mining for Optimization and Getting Early Buy-In and Feedback from Key Stakeholders - Step Seven: Tracking and Comparison of Metric Goals and Results - Step Eight: Incorporating the Learninng into the Next Round of Market Planning * Integration of Market Intelligence and Databases * Cultivating Adoption of Metrics, Research and Data Mining in the Corporate Structure Chapter 2 Market Spending Models and Optimization * Marketing Spending Model - Static Models - Dynamic Models * Marketing Spending Models and Corporate Finance - A Framework for Corporate Performance Marketing Effort Integration Chapter 3: Metrics Overview * Common Metrics for Measuring Returns and Investments * Developing a Formula for Return on Investment * Common ROI Tracking Challenges * Process for Identifying Appropriate Metrics * Differentiating Return Metrics from Operational Metrics Chapter 4: Multi-channel Campaign Performance Reporting and Optimization * Multi-channel Campaign Performance Reporting * Multi-channel Campaign Performance Optimization - Uncovering Revenue-Driving Factors Chapter 5: Understanding the Market through Market Research * Market Opportunities * Basis for Market Segmentation * Target-Audience Segmentation * Understanding Route to Market and Competitive Landscape by Market Segment * Overview of Marketing Research * Research Report and Results Presentation Chapter 6: Data and Basic Statistics * Data Types * Overview of Statistical Concepts - Population, Sample and the Central Limit Theorem - Random Variables - Probability, Probability Mass, Probability Density, Probability Distribution and Expectation - Mean, Median, Mode and Range - Variance and Standard Deviation - Percentile, Skewness and Kurtosis - Probability Density Functions - Independent and Dependent Variables - Covariance and Correlation Coefficient - Tests of Significance - Experimental Design Chapter 7: Introduction to Data Mining * Data Mining Overview * An Effective Step by Step Data Mining Thought Process - Step One: Identification of Business Objectives and Goals - Step Two: Determination of the key Focus Business Areas and Metrics - Step Three: Translation of Business Issues into Technical Problems - Step Four: Selection of Appropriate Data Mining Techniques and Software Tools - Step Five: Identification of Data Sources - Step Six: Conduction of Analysis - Step Seven: Translation of Analytical Results into Actionable Business Recommendations * Overview of Data Mining Techniques The following data mining techniques are discussed in this chapter. - Basic Data Exploration - Linear Regression Analysis - Cluster Analysis - Principal Component Analysis - Factor Analysis - Discriminant Analysis - Correspondence Analysis - Analysis of Variance - Canonical Correlation Analysis - Multi-Dimensional Scaling Analysis - Time Series Analysis - Conjoint Analysis - Logistic Regression - Association Analysis - Collaborative Filtering Chapter 8: Audience Segmentation * Case Study #1: Behavior and Demographics Segmentation * Case Study #2: Value Segmentation * Case Study #3: Response Behavior Segmentation * Case Study #4: Customer Satisfaction Segmentation Chapter 9: Data Mining for Customer Acquisition, Retention and Growth: * Case Study #1 Direct Mail Targeting for Customer Acquisition * Case Study #2 Attrition Modeling for Customer Retention * Case Study #3 Customer Growth Model Chapter 10: Data Mining for Cross-Selling and Bundled Marketing: * Case Study #1: E-Commerce Cross-Sell * Case Study #2 Online Advertising Promotions Chapter 11: Web Analytics: * Web Analytics Overview * Web Analytic Reporting Overview - Brand or Product Awareness Generation - Web Site Content Management - Lead Generation - E-Commerce Direct Sales - Customer Suuport and Service - Web Syndicated Research Chapter 12: Search Marketing Analytics * Search Engine Optimization Overview - Site Analysis - SEO Metrics * Search Engine Marketing Overview - SEM Resources - SEM Metrics * Onsite Search Overview - Visitor Segmentation and Visit Scenario Analysis