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Executive Education
Clariden Leadership Institute
Advanced Predictive Analytics (SG)
Richard Boire
Environics Analytics
Richard Boire, Environics Analytics
  • Top five experts in the analytics field in Canada
  • CMA Board Chair 2009-2013: Customer Insights and Analytics Council (Canadian Marketing Association)
  • Program Advisory Committee Member
    • University of Toronto Continuing Education
    • Seneca College-Toronto
    • Centennial College –Toronto
    • George Brown College-Toronto
  • Authored book on “Data Mining for Managers: How to Use Data (Big and Small) to Solve Business Problems” in 2014, published by Palgrave Macmillian and available on Amazon

Richard Boire's experience in database marketing and predictive analytics dates back to 1983, when he received an MBA from Concordia University in Finance and Statistics. Richard is a recognized authority on predictive analytics and is among a very few, selected top five experts in this field in Canada. He gives seminars on segmentation and predictive analytics for organizations such as Canadian Marketing Association (CMA), Direct Marketing News, Direct Marketing Association Toronto and the Association for Advanced Relationship Marketing (AARM). His articles have appeared in numerous Canadian publications such as Direct Marketing News, Strategy Magazine, and Marketing Magazine. He has taught applied statistics, data mining and database marketing at a variety of institutions across Canada which include University of Toronto, George Brown College, Seneca College, etc. He has also co-authored white papers on the following topics: ‘Best Practices in Data Mining’ as well as ‘Customer Profitability:  The State of Evolution among Canadian Companies’.  In 2014, he authored a book entitled in “Data Mining for Managers- How to Use Data (Big and Small) to Solve Business Problems”.


Richard is currently the Chair at the CMA’s Customer Insight and Analytics Committee and currently sits on the CMA’s Board of Directors. His initial experience at organizations such as Reader’s Digest and American Express allowed him to become a pioneer in the application of predictive modeling technology for all direct marketing programs. This extended to the introduction of models which targeted the acquisition of new customers based on return on investment.

Program Summary

Led by Richard Boire, an international thought leader sitting in University of Toronto's advisory committee and author of highly acclaimed book on data mining and predictive analytics, Richard will show you how to use structured and unstructured data and combine it with the right algorithms and tools to predict probabilities and trends.


In this interactive 2-day session, you will learn how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Richard will show you commonly used algorithms for predictive analytics using practical case studies. With insights from extensive consulting work around the world and his research on many large multinationals on data analytics, this program will examine the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. You will discover the tips and tricks that are essential for successful predictive modeling, theory behind predictive analytics and techniques for conducting successful predictive analysis.


Through this program, you will discover why data is your single most  powerful tool, how to translate big data into meaningful, usable business information.



Programs, dates and locations are subject to change. In accordance with Clariden Global policy, we do not discriminate against any person on the basis of race, color, sex, religion, age, national or disability in admission to our programs.

Introduction

Predictive analytics is recognized as a major competitive advantage in business today. Businesses are now adopting predictive analytics to provide a more quantitative approach in their decision-making process. Harvard Business Professor Thomas Davenport’s best selling business book ‘Competing on Analytics’ is a testimonial to the ever-growing importance of this discipline.


With the explosion of information, businesses now have access to more data than ever before. However, data is really only valuable if you can translate it into actionable insights capable of delivering operational efficiencies, and improve your Return On Investment (ROI).


In this 2 day comprehensive program, you will discover how to use prior or historical information to predict what might happen going forward to help your organization make better decisions. You will be equipped with the skills necessary to formulate and embed effective predictive analytics solutions in identifying and managing business problems faced by your organization. This session will help you to gain practical insights on data auditing and implement strategic data mining tools to align the predictive solutions with your business objectives. You will also learn how to establish the optimum measurement framework to enhance your Return On Investment (ROI) and business dollar benefits with predictive analytics.


As with any discipline, there is a process and approach that is critical in creating the necessary steps for building successful solutions. Within this process and through many years of experience in building analytical solutions, much learning has amassed on what works and what does not work. This prior learning is leveraged upon as participants are provided with a comprehensive perspective in how to build and deploy predictive analytics within their organizations.

 

Through numerous case studies across multiple industries including finance and banking, insurance, retail, non-profit plus other industry sectors, you will understand the increasing significance of predictive analytics as a core business discipline.


At the end of these sessions, participants will have the knowledge and tools to build more meaningful predictive analytics solutions which are most impactful to their organizations.

What You Can Expect

Capitalize on the expert's knowledge to gain maximum value on these vital issues:

  • Successfully apply predictive analytics enables businesses to effectively interpret big data
  • Learn how to use Big Data and data mining to discover patterns and make predictions
  • Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data
  • Covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere
  • Learn how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions
  • Enhance Return On Investment (ROI) and business dollar benefits with Predictive Analytics
  • Master the top 10 key tips in building successful predictive analytics solutions with well-recognized tools and practices

FREE TAKEAWAY

Participants will receive:

  • Boire Filler Group’s whitepaper on ‘Best Practices in Data Mining’
  • Compilation of trainer’s Data Mining news articles in the last year
  • Articles and the powerpoint presentation on “Top 10 Tips to Successful Predictive Analytics”  to the Kiev Direct Marketing Conference(Nov/2010)
  • A presentation to Canada Post in Toronto(March/2010) and to the Canadian Marketing Association  in Winnipeg (Jan/2011) on ‘Predictive Analytics in the New Economy’
  • A presentation on “Practitioner’s Viewpoint to Data Mining –Key Lessons Learned in the Trenches and Case Studies”  at the  Knowledge Discovery and Data Mining In San Diego in Aug/2011

Bring your business problems to the course. The instructor will stay after hours on each day to address specific issues and challenges that each participant faces in their business.

 

Who Will Benefit Most

Directors, GMs, VPs, Senior Managers, Managers and Executives with responsibilities in the following functional areas:

  • FUNCTIONAL ANALYSTS: Customer Analytics, Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Database/ Market Research, Inventory Flow Analysts, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers, E-commerce System Architects and Web Data Analysts
  • BUSINESS/ DATA ANALYSTS who must develop and interpret the models, communicate the results and make actionable recommendations
  • DATA MINING, DATA WAREHOUSING and DATABASE PRACTITIONERS/ MODELERS/ SEGMENTATION who wish to expand their skills and analytical toolbox as well as hone proficiencies in maneuvering elusive data mining obstacles that stand in the way of superior model accuracy
  • BUSINESS & MARKET INTELLIGENCE, IT & MIS PROFESSIONALS who wish to expand their skills in this increasingly visible area within the corporate IT agenda
  • DECISION SUPPORT SYSTEM/ SOFTWARE ARCHITECTS and DEVELOPERS who require a solid understanding of the infrastructures required for supporting a data mining solution
  • PROJECT LEADERS and those in Portfolio/ Project Management Unit who must report on development progress, resource requirements and system performance
  • ACADEMIA: Statisticians/ Qualitative Experts, Computer Science, Bioinformatics and those in academia who utilize statistical, predictive modeling and data mining techniques for research and development


And those who work with data and wish to understand and use recent developments in Predictive Analytics and Business Intelligence/ Business Analytics, from cross industries especially: Banking & Financial Institutions, Telecommunications, Consumer Products, Manufacturing, Conglomerate, Retail, Education, Oil & Gas, Logistics and Utilities.

Program Outline

DAY 1

Overview of predictive analytics as a key business imperative

  • Defining predictive analytics
  • Identifying the role and impact of predictive analytics within the different business disciplines
  • How predictive analytics evolves within a given Business


Case Study: American Express

  • Background and History that led to their major challenges
  • How Predictive Analytics was introduced to the organization
  • How Overall performance was improved
  • What other challenges remained after the first solution was developed
  • What were the subsequent challenges that continued to arise and how predictive analytics evolved to meet these challenges


Formulating an effective predictive analytics solutions

  • What are the 4 required steps/phases
  • Roles and responsibilities of key stakeholders within this process
  • Building the right organization structure with predictive analytics as a core discipline
  • Establishing the right balance between software/hardware and people in building tools 


Case Study: Retailer 

  • What is the process used to identify the analytical requirements of an organization
  • How do we use both the existing organization’s  human resources as well as potential outside human resources to address these above analytical requirements
  • Who are the key stakeholders both within and outside the organization that will be our key partners
  • What kind of software currently exists and how does it address our analytical requirements
  • Identifying other tools that will be required to fill any analytical requirement gaps


Embedding predictive analytics solutions within the organization

  • Building the predictive analytics team and identifying the appropriate skill sets
  • Understanding what are the business expectations of predictive analytics solutions
    • Use of gains charts/decile tables to demonstrate this impact
  • Championing the C-Suite through the creation of quick wins
    • Examples using high value and RFM techniques as quick wins 
  • Increasing  automation of predictive analytics solutions-Advantages/disadvantages
    • What are the  Advantages/disadvantages of increasing automation
  • Using predictive analytics within the online environment
    • Using log data and how it differs from the off-line structured environment
    • The key information that is extracted from a page click
    • Integrating offline behaviour and online behaviour to maximize the performance of a given predictive analytics solution
    • Evaluating solutions within the online environment
  • Text Mining: Using predictive analytics within an unstructured data environment
    What is the difference between structured and unstructured information
    • What is the process used to build a given solution
    • What kind of statistical analysis is deployed in this process
    • How do we integrate the unstructured information solution within an overall predictive analytics solution  


Identifying and managing business problem

  • How to gather the right information
  • Increasing one’s  understanding  of the domain knowledge of the business
  • Prioritizing the predictive analytics solutions options


Case Studies: Telecommunications Company and Courier Organization

  • Defining the process
  • What are the right questions to ask
  • Analyzing historical results to obtain a better understanding of the business
  • Using Analytics to acquire more domain knowledge of the business

 

DAY 2


Gain practical insight on data auditing

  • Review on The Extract, Transform, and Load (ETL) process
  • The data audit process and its importance in  better understanding the data environment
  • Creating  source vs. derived variables
  • Creating the dependant variable vs. independent variable


Case study: Retail Photography Company

  • Defining the process
  • Identifying your source files
  • Conducting the data audit
    • Creating Frequency Distribution Reports and Data Diagnostic Reports to gain better insight on the data
  • From the above reports, determine how to organize, summarize, and manipulate the data into the analytical file


Implementing strategic Data Mining tools

  • Correlation analysis
  • Exploratory data Analysis reports
  • Value/behavior based segmentation vs. cluster Segmentation
  • Factor analysis
  • Decision-tree analysis CHAID (Chi-Square Automatic Interaction Detector)
  • Logistic regression vs. multiple regression vs. neural nets
  • Comparing the advantages and disadvantages of the above techniques
  • Evaluating the Business Benefit of a given solution and its ultimate ROI


Case Studies from the following sectors: Banking/Finance, Insurance, Non Profit, Etc

  • What is the process in actually creating the solution
  • How these above tools are used to build  the solution
  • Integration of segmentation and modeling into overall predictive analytics solution
  • Eliminating the ‘black box’ of predictive analytics and providing information regarding the solution that is meaningful to the business user
  • How to clearly demonstrate the $ impact of a given solution 


Establish the optimum measurement framework

  • What is the quality control process in ensuring that solutions are being correctly implemented
    • Identifying your measurement objectives
    • Creating the Right Measurement Framework


Case Studies and Examples to Demonstrate Actual Payback of Predictive Analytics Solution

  • Finance/banking
  • Property and Auto Insurance
  • Travel


The case studies will examine the following:

  • With a developed solution, what are the reports  that need to be created to help ensure effective implementation
  • What does the analyst need to do when results between implementation and when the solution was developed are drastically different
  • Given the specific environment of the organization, what is the process for identifying the measurement objectives
  • How does the analyst marry the data to create the appropriate testing  framework and matrix
  • How does the analyst use the data to create the appropriate measurement reports


The Case studies will also cover the various functional areas such as:

  • Marketing and such tools as profiles, response models, attrition models,  profitability models
  • Insurance Risk models that can be used to predict the price or premium for automobiles and property
  • Analytical Solutions that can be used to improve operational processes within a given organization  


For previous attendees to the course, additional case studies will focus on the following:

  • Building solutions that improve the operation effectiveness of the organization
  • Insurance Pricing Solutions that are more business-oriented towards the actuarial community.
  • Data Discovery exercise that provides a strategic roadmap for the organization
  • How an ongoing analytics process and culture provides solutions to a wealth management organization


Big Data and Predictive Analytics

  • What are the similarities between big data analytics and small data analytics
  • What are the differences between big data analytics and small data analytics
  • What are the business applications of Big Data Analytics to Predictive Analytics
  • Defining Strategies vs. Tactics using Big Data Analytics 


Mastering the top 10 tips of building successful Predictive Analytics solutions

  • Key Pieces of learning to consider when building predictive analytics solutions
    • How to create Quick Wins
    • Identifying when Results are Overstated
    • Where to Emphasize efforts: Statistics vs. the Data


Case Study: Financial Institution- Interpreting statistical output correctly to obtain a business solution

  • Examine actual output of statistical routine
  • Interpret results and identify next steps/creation of more statistical output
  • Comparison of different modeling output and identification of best model

 

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Venue: Grand Hyatt Hotel Singapore
Date: 16 - 17 Nov 2017
Faculty: Richard Boire
Early Bird 1: S$2,595 (by 21 Sep 2017)
Early Bird 2: S$2,795 (by 19 Oct 2017)
Regular Fee: S$2,895
Group Discount: 2nd participant get 10%, or register 3 participants and 4th participant get a complimentary seat
(1 discount scheme applies)
Note: GST is applicable to participants from Singapore registered companies.
Contact: stefanie@claridenglobal.org
 
 
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