DATA MINING AND MARKET INTELLIGENCE FOR OPTIMAL MARKETING RETURNS
To order this title, and for more information, click here
By 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
Description 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.
Audience
Primary audience: Marketing and sales executives; marketing researchers
Secondary audience: Marketing specialty MBA students
Contents 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
Books and book related electronic products are priced in US dollars (USD), euro (EUR), and Great Britain Pounds (GBP). USD prices apply to the Americas and Asia Pacific. EUR prices apply in Europe and the Middle East. GBP prices apply to the UK and all other countries.
Home | Elsevier Sites | Privacy Policy | Terms and Conditions | Feedback | Site Map | A Reed Elsevier Company