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Executive Education
Clariden Leadership Institute
Data Analytics For Managers (NZ)
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 in 2014 published by Palgrave Macmillian and available in Amazon on “Data Mining for Managers: How to Use Data (Big and Small) to Solve Business Problems”


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. 

His initial experience at organizations such as Reader’s Digest and American Express allowed him to become a pioneer in the application of predictive modelling 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.

With this experience, Richard formed his own consulting company back in 1994 which evolved into the Boire Filler Group, a Canadian leader in offering analytical and database services to companies seeking solutions to their existing predictive analytics or database marketing challenges. In 2016, the Boire Filler Group was acquired by Environics Analytics, where he is currently Senior Vice President Innovation.   

Richard is a recognized authority on predictive analytics and is among a very few, select top five experts in this field in Canada, with expertise and knowledge that is difficult, if not impossible to replicate in Canada. He gives seminars on segmentation and predictive analytics for organizations such as Canadian Marketing Association (CMA), Direct Marketing News, and Predictive Analytics World where he chaired the Canadian version in both 2013 and 2014. His articles have appeared in numerous publications including Direct Marketing News, Strategy Magazine, Marketing Magazine, and Predictive Analytics Times. He has taught applied statistics, data mining and database marketing at a variety of institutions across Canada which includes University of Toronto, George Brown College, Seneca College, etc. Richard was Chair at the CMA’s Customer Insight and Analytics Committee and sat on the CMA’s Board of Directors between 2009 – 2013. He has chaired numerous full day conferences on behalf of the CMA (the 2000 Database and Technology Seminar as well as the 2002 Database and Technology Seminar and the first-ever Customer Profitability Conference in 2005). He has 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 “Data Mining for Managers – How to Use Data (Big and Small) to Solve Business Problems” published by Palgrave Macmillan of New York City.

 

TESTIMONIALS

 

“Richard has provided my clients with expert analytic insight into their business. He has helped streamline Data Mining plans and identified un-tapped opportunities. I would highly recommend Richard”


“Richard co-chaired the database marketing and business intelligence council of the CMA while I was a member of said council. His contribution of effort and energy was and continues to be enormous; Richard is a large factor in the success of the council.”


"Richard has developed and facilitated workshops for the CMA for over a decade with highly positive feedback. He is a wealth of knowledge when in the area of analytics and database marketing. His easy going presentation style is welcomed by participants which allows for greater audience participation. I would highly recommend Richard to speak on any database related topic."


"Rich Boire has been an authoritative, popular and well-read contributor to Direct Marketing magazine in Canada for more than 15 years. He's one of the most-respected and sought-out speakers and consultants on analytics in all sectors of the marketplace. We've enjoyed working with him on articles and seminars, and he is a frequent invited speaker for any and all events we produce in this area."

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 give you a comprehensive perspective on how to both build and deploy data analytics within your respective organizations.

 

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 appropriate and commonly used tools in developing data analytics solutions to achieve optimum productivity. 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 advanced data analytics. You will discover the tips and tricks that are essential for building a data analytics solution with proven results and to incorporate it as a key corporate competitive advantage in order to stay on top in the respective industries.

 

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

The growing importance of data analytics is now recognized as a major competitive advantage in business today. Businesses are now adopting data analytics to provide a more quantitative and scientific approach in their decision-making. While all successful enterprises capitalize on data and analytics to some degree, new research from Forbes Insights and Cisco shows how pervasive modern analytics strategies have become within nearly all business initiatives. In fact, 51% of C-suite executives at large enterprises in both North America and Europe say analytics will only continue to grow in importance for maintaining and growing market share over the next two years. Furthermore, an impressive 85% of companies that are succeeding with analytics are seeing revenue growth greater than 7%. This highlights the importance of a rigorous and relevant data analytics strategy for any organization as well as the need to equip its workforce with strong analytical skills to drive and achieve organizational goals. 

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 give you a comprehensive perspective on how to both build and deploy data analytics within your organization

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 appropriate and commonly used tools in developing data analytics solutions to achieve optimum productivity. 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 advanced data analytics. You will discover the tips and tricks that are essential for building a data analytics solution with proven results and incorporate it as a key corporate competitive advantage in order to stay on top in the industry.

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

What You Can Expect

  • Justify data analytics as a key corporate competitive advantage in order to stay on top in your industry
  • Identify the four steps approach in building a data analytics solution with proven results
  • Explore the appropriate tools in developing data analytics solutions to achieve optimum productivity
  • Gain insights on aligning the solutions with the organization’s business objectives from a whole new perspective
  • Formulate data analytics as part of the corporate culture to ensure performance sustainability
  • Enhance return on investment (ROI) and business dollar benefits with data analytics
  • Review stakeholder’s roles and responsibilities from a data analytics perspective
  • Master the top 10 key tips in building successful data analytics solutions with well-recognized tools and practices
 

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
 

Program Outline

DAY 1 - 09:00 - 17:00 | 29 JULY 2019

Session 1: Overview Of Data Analytics As A Key Business Imperative

  • Defining data analytics
  • Identifying the role and impact of data analytics within the different business disciplines
  • How data analytics evolves within a given business
  • A brief overview of the difference between advanced vs. non-advanced analytics


Session 2: Case Study: American Express

  • Background and history that led to their major challenges
  • How advanced data 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 advanced analytics evolved to meet these challenges


Session 3: Formulating An Effective Data Analytics Solution

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


Session 4: 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


Session 5: Embedding Data Analytics Solutions Within The Organization

  • Building the data analytics team and identifying the appropriate skill sets
  • Understanding what are the business expectations of data analytics solutions
    • Use of gains charts/decile tables to demonstrate the impact of advanced analytics solutions
    • Use of cohort reports to demonstrate impact of non advanced analytics solutions
  • Championing the C-Suite through the creation of quick wins
    • Examples using high value and RFM techniques
    • Analytics reports at executive level: KPI’s and dashboard reports
  • Increasing automation of analytics reporting - advantages/disadvantages
    • What are the advantages/disadvantages of increasing automation
    • Empowering the business end user and the creation of Pivot Tables
    • Exploration of other tools such as Tableau
  • Using data 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
    • open rates/click through rates and understanding its impact on consumer behaviour
    • 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 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
    • The use of sentiment analysis as another analytics tool


Session 6: Identifying And Managing Business Problem

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


Session 7: Case Studies: Telecommunications Company/Courier Organization/Retail 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 - 09:00 - 17:00 | 30 JULY 2019


Session 1: 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


Session 2: 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


Session 3: Implementing Advanced Analytics 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


Session 4: 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 


Session 5: 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


Session 6: Case Studies And Examples To Demonstrate Actual Payback Of Predictive Analytics Solution

  • 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
  • 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  
  • 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


Session 7: 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
  • How do we leverage big data analytics alongside small data analytics for better decision-making
  • Defining strategies vs. tactics using big data analytics


Session 8: Mastering The Top 10 Tips Of Building Successful Data 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

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Venue: Stamford Plaza Auckland, New Zealand
Date: 29 - 30 July 2019
Faculty: Richard Boire
Early Bird 1: NZ$2,295 (by 3 June 2019)
Early Bird 2: NZ$2,495 (by 1 July 2019)
Regular Fee: NZ$2,595
Group Discount: 2nd participant get 10%, or register 3 participants and 4th participant get a complimentary seat
(1 discount scheme applies)
Contact: [email protected]
 
 
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