Data Mining and Market Intelligence for Optimal Marketing ReturnsBy
- Susan Chiu, Director of Business Intelligence Center at Ingram Micro, Inc.
- Domingo Tavella, Adjunct Professor at Berkeley's Haas School of Business, Masters in Financial Engineering program; president of Octanti Associates
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.
Primary audience: Marketing and sales executives; marketing researchersSecondary audience: Marketing specialty MBA students
Published: May 2008
Imprint: Butterworth Heinemann
âThis book is a must read. It shows you how you can transform data into winning marketing strategies. The trend towards marketing science is certain and this book provides a systematic framework for firms to bring science into marketing decisions.â Teck H. Ho, Professor of Marketing, Haas School of Business, University of California, Berkeley "Susan Chiu and Domingo Tavella 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." Mike Milligan, Vice President, Marketing Communications, The Xerox Corporation âThis book is an excellent no-frills one stop shop for proven approaches to quantitative marketing and should be a valuable reference to practitioners who subscribe to the notion that data-driven decisions are critical to mounting successful marketing campaigns in todayâs crowded marketplace. The authorsâ emphasis on practical application of analytics and detailed discussions of the relevant business issues through real-world business examples make this book a useful and immediately applicable resource for tackling todayâs quantitative marketing challenges.â Albert Thong, Director, Business Marketing Operations, Cisco Systems
- Chapter 1: Introduction to Strategic Importance of Metrics, Marketing Research and Data Mining in Today's Marketing Worldâ¢ The Role of Metricsâ¢ The Role of Researchâ¢ The Role of Data Miningâ¢ 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 Planningâ¢ Integration of Market Intelligence and Databasesâ¢ Cultivating Adoption of Metrics, Research and Data Mining in the Corporate Structure Chapter 2 Market Spending Models and Optimizationâ¢ Marketing Spending Model - Static Models - Dynamic Modelsâ¢ Marketing Spending Models and Corporate Finance - A Framework for Corporate Performance Marketing Effort Integration Chapter 3: Metrics Overviewâ¢ Common Metrics for Measuring Returns and Investmentsâ¢ Developing a Formula for Return on Investmentâ¢ Common ROI Tracking Challengesâ¢ Process for Identifying Appropriate Metricsâ¢ Differentiating Return Metrics from Operational Metrics Chapter 4: Multi-channel Campaign Performance Reporting and Optimizationâ¢ Multi-channel Campaign Performance Reportingâ¢ Multi-channel Campaign Performance Optimization - Uncovering Revenue-Driving FactorsChapter 5: Understanding the Market through Market Research â¢ Market Opportunitiesâ¢ Basis for Market Segmentationâ¢ Target-Audience Segmentationâ¢ Understanding Route to Market and Competitive Landscape by Market Segmentâ¢ Overview of Marketing Researchâ¢ Research Report and Results PresentationChapter 6: Data and Basic Statistics â¢ Data Typesâ¢ 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 DesignChapter 7: Introduction to Data Mining â¢ Data Mining Overviewâ¢ 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 Recommendationsâ¢ 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 Segmentationâ¢ Case Study #1: Behavior and Demographics Segmentationâ¢ Case Study #2: Value Segmentationâ¢ Case Study #3: Response Behavior Segmentationâ¢ Case Study #4: Customer Satisfaction SegmentationChapter 9: Data Mining for Customer Acquisition, Retention and Growth: â¢ Case Study #1 Direct Mail Targeting for Customer Acquisition â¢ Case Study #2 Attrition Modeling for Customer Retentionâ¢ Case Study #3 Customer Growth ModelChapter 10: Data Mining for Cross-Selling and Bundled Marketing: â¢ Case Study #1: E-Commerce Cross-Sellâ¢ Case Study #2 Online Advertising PromotionsChapter 11: Web Analytics: â¢ Web Analytics Overviewâ¢ 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 ResearchChapter 12: Search Marketing Analyticsâ¢ Search Engine Optimization Overview - Site Analysis - SEO Metricsâ¢ Search Engine Marketing Overview - SEM Resources - SEM Metricsâ¢ Onsite Search Overview - Visitor Segmentation and Visit Scenario Analysis