Dave Haertzen: "Data Structures for Analytics" and "Modeling Your Customer"

Who is David Haertzen? David Haertzen is the author of The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics. In addition, he contributes to industry publications and blogs. 

He is an acknowledged trail blazer and thinker in the fields of data warehousing, business intelligence and analytics. He has aided a diverse set of organizations from start-ups to multinationals to utilize data for their advantage. David is known for his engaging teaching and speaking style (look out for challenging ideas!) and is a sought after presenter at industry conferences and events.

He is a graduate of the University of Minnesota and holds an MBA from the University of St. Thomas To learn more about his training and advisory services, visit his website DavidHaertzen.com and sign up for his Data and Analytic Insights Newsletter. David can be contacted at david@davidhaertzen.com. 

Topic Outlines

Topic 1: Data Structures for Analytics

The attendees will learn the fundamental ways of organizing and preparing data for analytics: advanced scheduling / logistics, social media analytics, visual analytics, regression analysis and market basket analysis.  The attendee will see how specific input data can be used to support machine learning which results in predictive analytics.

The session will cover data organizations and data preparation methods that support analytics.  Data organizations presented include: network / graph, tree, dimensional, normalized and flattened.  Each of these data organizations is suited to particular types of analysis:  advanced scheduling / logistics, visual analytics, regression analysis and market basket analysis.

Data Structure for Analytics Slides

I.  Overview of Analytic Applications

·         Waves of Analytic Applications

·         Analytic Methodology

·         Analytic Architecture

II. Graph Data

·         Graph Data

·         Scheduling Models

·         Affinity Analysis

·         Social Media Relationships

III. Visual Analytics

·         Visual Analytic Operations

·         Dimensional Modeling

IV. Predictive Analytics

·         Example Predictions

·         Data for Predictive Analytics

·         Developing Predictive Models

V. What If Analysis

·         Principles of What If Analysis

·         What If Architecture

Topic 2: Model Your Customer

In this topic, you will learn how to apply analytics to customers. Most organizations are heavily invested in creating and maintaining positive and profitable relationships with their customers.  Implemented customer data models can help to answer questions including:

·  What are the characteristics of our best customers?

·  Who are our best customers?

·   How can we better serve our customers?

·  Which customers are likely to leave?

·   Which customers are likely to respond to a new marketing campaign?

A practical framework is described for modeling the customer and using that information to increase sales while avoiding costs and risks.

Model Your Customer Slides

I.  Overview

• Who are we talking about?
• Customer Related Decisions
• Types of Customer Models
• Customer Data Models
• Customer Analytical Models

II. Segmenting Customers

• Segmentation Concepts
• Value Segmentation
• Behavior Segmentation
• Multi-dimensional Segmentation
• Clustering

III. Modeling the Opportunity

• Customer Profitability Model
• Profile of the Profitable Customer
• Growing Best Customers
• Identifying Responsive Customers
• Using the Best Message
  
  
• Modeling Costs

• Modeling Risks

V.  Networks of Customers

• Social Media Networks (B2C)

• Trading Networks (B2B)

Moneygram - St. Louis Park, MN