Gaining Customer Insight through DATA MINING

Gaining Customer Insight through DATA MINING

Tanggal
18-19 April 2011

Jam Pelaksanaan
8.30 – 17.00 WIB

Tempat
Hotel Ambhara Jakarta

Pembicara / Fasilitator
Salman Taufik: gaining experienced as banking professional since 1990, mastering in risk modeling and data analysis. He has background in statistics and has worked with large data set for economic and business research using SAS and SPSS. Now he is consultant and trainer in risk management and data mining for business intelligent.

Harga
Rp. 5,500,000,- per peserta
Rp. 15,500,000,- untuk tiga peserta dari perusahaan yang sama

Emphasizing practical skills as well as providing theoretical knowledge, this hands-on course covers segmentation analysis and profiling in the context of business data mining. Topics include the data mining modeling techniques and application : classification and prediction model, clustering, association and sequence.

SESSION-1: INTRODUCTION

• The Customer Relationship Management Strategy
• Data Mining in the CRM Framework
  
  • Customer Segmentation
  • Direct Marketing Campaigns
  • Market Basket and Sequence Analysis
• Supervised Modeling
  
  • Classification modeling: predicting Events, marketing application
  • Screening models
  • Prediction Model
• Unsupervised Modeling Techniques
  
  • Segmention with Clustering Techniques
  • Dimensionality of Data Reduction Techniques
  • Association or Affinity ModelingTechniques
  • Sequence Modeling Techniques
  • Record Screening Modeling Techniques
• Machine Learning/Artificial Intelligence vs. Statistical Techniques

SESSION-2: CUSTOMER SEGMENTATION
Customer Segmentation

• An Introduction to Customer Segmentation
  
  • Segmentation in Marketing
  • Segmentation Tasks and Criteria
• Segmentation Types in Consumer Markets
  
  • Value-Based Segmentation
  • Behavioral Segmentation
  • Propensity-Based Segmentation
  • Loyalty Segmentation
  • Socio-demographic and Life-Stage Segmentation
  • Needs/Attitudinal-Based Segmentation
• Segmentation methods
  
  • Behavioral Segmentation Methodology
  • Value-Based Segmentation Methodology

Data mining for segmentations

• Principal Components Analysis
  • Why consider PCA (Principle Component Analysis)
  • How Many Components Are to Be Extracted?
  • What Is the Meaning of Each Component?
  • Does the Solution Account for All the Original Fields?
  • Proceeding to the Next Steps with the Component Scores
  • Recommended PCA Options

• Clustering Techniques
  • Data Considerations for Clustering Models
  • Clustering with K-means
  • Clustering with the TwoStep Algorithm
  • Clustering with Kohonen Network/Self-organizing Map (SOM)

• Examining and Evaluating the Cluster Solution
  • The Number of Clusters and the Size of Each Cluster
  • Cohesion of the Clusters
  • Separation of the Clusters

• Understanding the Clusters through Profiling
  • Profiling the Clusters
  • Additional Profiling Suggestions

• Selecting the Optimal Cluster Solution
• Cluster Profiling and Scoring with Supervised Models
• An Introduction to Decision Tree Models
  • The Advantages of Using Decision Trees for Classification Modeling
  • One Goal, Different Decision Tree Algorithms: C&RT, C5.0, and CHAID

Exercise Case:
Segmentation Application in banking
Segmentation Application in Telecommunication

SESSION-3: DIRECT MARKETING CAMPAIGNS

• Approaches for direct marketing
• How’s data mining help in marketing campaign
• Scoring in RFM analysis
• Approach-1: Budget optimization
• Concept of lift and gains chart
• Approach-2: Optimizing the campaign
• Measuring the P/L
• Approach-3:Customer optimization
• Find the best model: regression, neural network, decision tree
• Confusion matrix
• Churn/attrition model
• Preventing Customer attrition
• Decision tree analysis

SESSION-4: MARKET BASKET AND SEQUENCE ANALYSIS
The RFM Analysis

• The RFM Segmentation Procedure
• RFM: Benefits, Usage, and Limitations
• Grouping Customers According to the Products They Buy

Wajib diikuti oleh
Anyone who wants to learn how to find meaningful segments in their customer data, focusing on practical business solutions as well as statistical rigor; business analysts, managers, marketers, programmers, and others can benefit from this course