Data Mining in Marketing Real Project
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Part One: Planning the Data Mining
Section 1: Setting the Objective
-Defining the Goal
* Profile Analysis
* Segmentation
* Response
* Risk
* Activation
* Cross-Sell and Up-Sell
* Attrition
* Net Present Value
* Lifetime Value
-Choosing the Modeling Methodology
* Linear Regression
* Logistic Regression
* Neural Networks
* Genetic Algorithms
* Classification Trees
-The Adaptive Company
* Hiring and Teamwork
* Product Focus versus Customer Focus
-Summary
* Profile Analysis
* Segmentation
* Response
* Risk
* Activation
* Cross-Sell and Up-Sell
* Attrition
* Net Present Value
* Lifetime Value
-Choosing the Modeling Methodology
* Linear Regression
* Logistic Regression
* Neural Networks
* Genetic Algorithms
* Classification Trees
-The Adaptive Company
* Hiring and Teamwork
* Product Focus versus Customer Focus
-Summary
Section 2: Customers and Their Lifecycles
-Who is the Customer?
* Consumers
* Business Customers
* Customer Segments
-The Customer Lifecycle
* Stages of Lifecycle
* Major Lifecycle Events
* Data Appears at Different Times in the Lifecycle
-Targeting the Customers at the Right Time
* Budget Optimization
* Campaign Optimization
* Customer Optimization
* Consumers
* Business Customers
* Customer Segments
-The Customer Lifecycle
* Stages of Lifecycle
* Major Lifecycle Events
* Data Appears at Different Times in the Lifecycle
-Targeting the Customers at the Right Time
* Budget Optimization
* Campaign Optimization
* Customer Optimization
Section 3: Data Mining Methodology
-Two Styles of Data Mining
* Directed Data Mining
* Undirected Data Mining
-The Virtuous Cycle of Data Mining
-Identify the Right Business Problem
* Is the Data Mining Effort Necessary?
* Is there a Particular Segment or Subgroup That is Most Interesting?
* What Are the Relevant Business Rules?
* What about the Data?
* Verify the Opinion of Domain Experts
* Directed Data Mining
* Undirected Data Mining
-The Virtuous Cycle of Data Mining
-Identify the Right Business Problem
* Is the Data Mining Effort Necessary?
* Is there a Particular Segment or Subgroup That is Most Interesting?
* What Are the Relevant Business Rules?
* What about the Data?
* Verify the Opinion of Domain Experts
Section 4: Selecting the Data Sources
-Types of Data
-Sources of Data
* Internal Sources
* External Sources
-Selecting Data for Modeling
* Data for Prospecting
* Data for Customer Models
* Data for Risk Models
-Constructing the Modeling Data Set
* How big should my sample be?
* Sampling Methods
* Developing Models from Modeled Data
* Combining Data from Multiple Offers
-Summary
-Sources of Data
* Internal Sources
* External Sources
-Selecting Data for Modeling
* Data for Prospecting
* Data for Customer Models
* Data for Risk Models
-Constructing the Modeling Data Set
* How big should my sample be?
* Sampling Methods
* Developing Models from Modeled Data
* Combining Data from Multiple Offers
-Summary
Part Two: Data Mining Demonstration
Section 5: Preparing the Data for Modeling
-Accessing the Data
* Classifying Data
* Reading Raw Data
-Creating the Modeling Data Set
* Sampling
-Cleaning the Data
* Continuous Variables
* Categorical Variables
-Summary
-Project 1
* Classifying Data
* Reading Raw Data
-Creating the Modeling Data Set
* Sampling
-Cleaning the Data
* Continuous Variables
* Categorical Variables
-Summary
-Project 1
Section 6: Selecting and Transforming the Variables
-Defining the Objective Function
* Probability of Activation
* Risk Index
* Product Profitability
* Marketing Expense
-Deriving Variables
* Summarization
* Ratios
* Dates
-Variable Reduction
* Continuous Variables
-Categorical Variables
-Developing Linear Predictors
* Continuous Variables
* Categorical Variables
-Interactions Detection
-Summary
* Probability of Activation
* Risk Index
* Product Profitability
* Marketing Expense
-Deriving Variables
* Summarization
* Ratios
* Dates
-Variable Reduction
* Continuous Variables
-Categorical Variables
-Developing Linear Predictors
* Continuous Variables
* Categorical Variables
-Interactions Detection
-Summary
Section 7: Processing and Evaluating the Model
-Processing the Model
* Splitting the Data
* Method 1: One Model
* Method 2: Two Models— Response
* Method 2: Two Models— Activation
* Comparing Method 1 and Method
-Summary
* Splitting the Data
* Method 1: One Model
* Method 2: Two Models— Response
* Method 2: Two Models— Activation
* Comparing Method 1 and Method
-Summary
Section 8: Validating the Model
-Gains Tables and Charts
* Method 1: One Model
* Method 2: Two Models
-Scoring Alternate Data Sets
-Summary
-Project 2
* Method 1: One Model
* Method 2: Two Models
-Scoring Alternate Data Sets
-Summary
-Project 2
Section 9: Implementing and Maintaining the Model
-Scoring a New File
* Scoring In-house
* Outside Scoring and Auditing
-Implementing the Model
* Calculating the Financials
* Determining the File Cut -off
* Champion versus Challenger
* The Two -Model Matrix
-Model Tracking
* Back-end Validation
-Model Maintenance
* Model Life
* Model Log
-Summary
-Project 3
* Scoring In-house
* Outside Scoring and Auditing
-Implementing the Model
* Calculating the Financials
* Determining the File Cut -off
* Champion versus Challenger
* The Two -Model Matrix
-Model Tracking
* Back-end Validation
-Model Maintenance
* Model Life
* Model Log
-Summary
-Project 3
Part Three: Recipes for Every Occasion
Section 10: Understanding Your Customer: Profiling and Segmentation
-What is the importance of understanding your customer?
* Types of Profiling and Segmentation
-Profiling and Penetration Analysis of a Catalog Company's Customers
* RFM Analysis
* Penetration Analysis
-Developing a Customer Value Matrix for a Credit
-Card Company
* Customer Value Analysis
-Performing Cluster Analysis to Discover Customer Segments
-Summary
* Types of Profiling and Segmentation
-Profiling and Penetration Analysis of a Catalog Company's Customers
* RFM Analysis
* Penetration Analysis
-Developing a Customer Value Matrix for a Credit
-Card Company
* Customer Value Analysis
-Performing Cluster Analysis to Discover Customer Segments
-Summary
Section 11: Targeting New Prospects: Modeling Response
-Defining the Objective
* All Responders Are Not Created Equal
-Preparing the Variables
* Continuous Variables
* Categorical Variables
-Processing the Model
-Model Validation
-Implementing the Model
-Summary
-Project 4
* All Responders Are Not Created Equal
-Preparing the Variables
* Continuous Variables
* Categorical Variables
-Processing the Model
-Model Validation
-Implementing the Model
-Summary
-Project 4
Section 12: Avoiding High-Risk Customers: Modeling Risk
-Credit Scoring and Risk Modeling
-Defining the Objective
-Preparing the Variables
-Processing the Model
-Validating the Model
-Implementing the Model
* Scaling the Risk Score
-A Different Kind of Risk: Fraud
-Summary
-Defining the Objective
-Preparing the Variables
-Processing the Model
-Validating the Model
-Implementing the Model
* Scaling the Risk Score
-A Different Kind of Risk: Fraud
-Summary
Section 13: Targeting Profitable Customers: Modeling Lifetime Value
-What is lifetime value?
* Uses of Lifetime Value
* Components of Lifetime Value
-Applications of Lifetime Value
* Lifetime Value Case Studies
-Calculating Lifetime Value for a Renewable Product or Service
-Calculating Lifetime Value: A Case Study
* Case Study: Year One Net Revenues
* Lifetime Value Calculation
-Summary
* Uses of Lifetime Value
* Components of Lifetime Value
-Applications of Lifetime Value
* Lifetime Value Case Studies
-Calculating Lifetime Value for a Renewable Product or Service
-Calculating Lifetime Value: A Case Study
* Case Study: Year One Net Revenues
* Lifetime Value Calculation
-Summary
（更多资料和具体参加方法）
