1st Edition
Data Mining and Market Intelligence for Optimal Marketing Returns
294 Pages
by
Routledge
296 Pages
by
Routledge
Also available as eBook on:
The authors present a practical and highly informative perspective on the elements that are crucial to the success of a marketing campaign. Unlike books that are either too theoretical to be of practical use to practitioners, or too soft to serve as solid and measurable implementation guidelines, this book focuses on the integration of established quantitative techniques into real life case studies that are immediately relevant to marketing practitioners.
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
• 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
Biography
Susan Chiu, Domingo Tavepson
