IAPA AGM and Victorian Chapter meeting
Join IAPA for the last event of the year where the head of the Customer Insights team for Australia Post, Bruce Wright, will share his experience in establishing the team, capability and early wins.
Presentation Title: Analytics for an icon Australian institution - Australia Post
Bruce Wright - Customer Insights Manager
IAPA will also be holding it's Annual General Meeting from 4.30pm to 5.45 pm, immediately prior to the Victorian Chapter meeting. Members are encouraged to attend and get involved in shaping the year ahead.
Date: Next Wednesday 9 December
Logistics:
IAPA AGM - Registration from 4.15pm
AGM commences at 4.30pm
AGM concludes at 5.45pm
IAPA Victorian Chapter meeting with Australia Post
Registration from 5.45pm
Commences at 6pm
Networking over drinks and canapes from 7pm to 8pm
IMPORTANT: please note the new location at Deloitte's new premises:
L9, 550 Bourke Street
Please indicate in the RSVP if you intend to attend both the AGM and the Chapter meeting (or just one), email to: rsvp@iapa.org.au
SAS is delighted to announce the return of Tony Woods to Australia to present two specialised analytics workshops in Sydney and Melbourne.
Design of Marketing Experiments for Direct Marketers (Brand New 2 day workshop)
Melbourne: 3 - 4 December, 2009
How do you test different marketing concepts without spending vast sums or taking a risk with your customer base? Carefully, is the only possible answer; but what can that mean? Certainly you will have to restrict the numbers; you do not want to inflict on too many customers an offer which functions surprisingly badly. Even when all the offers you have in mind would work quite well you want to switch everyone, as far as possible, to the most attractive one.
However, if you try each concept only on a "few", is there not a danger that the picture will be confused; perhaps there is even a risk that a weaker concept might perform very well on a lucky sample and outshine a better idea? That is exactly why marketing tests need to be designed on solid statistical principles.
In this class you will learn how to develop the analysis ideas needed to motivate the concept of a good test design and which help to determine both the size and the shape of an efficient design. We begin with simple cases and gradually develop the complexity.
This two-day workshop explores how to:
- Optimise your marketing process and significantly reduce marketing costs while improving response rates
- Understand, measure, and optimise the effectiveness of each of your marketing channels within your campaigns
- Design a process that will actively measure and report on the benefit of applying analytics
Course link: http://www.sas.com/apps/wtraining2/coursedetails.jsp?ctry=AU&course_code=AUS1213
Propensity Scoring Models (2 day workshop)
Melbourne: 7 - 8 December, 2009
Who will buy a product? Who will repay a loan? Who will display any particular behaviour with a commercial value?
We present all the tools that you need during the first day of the course. We begin by taking a rapid look at the development of propensity scoring in order to bring out how it has changed from its origins and to examine how the demands made on the technique have changed, as have the criteria for measuring is success. We cover briefly the theory of logistic regression and discuss the importance of the concepts of Odds and the Odds ratio.
From there we help you to explore the types of data structure that is likely to arise in a commercial context, discuss the advantages and difficulties that may arise from different structures and look at the extent to which simple correlations might be helpful (when your predictors are continuous measurements) or Chi-square analysis (when they are nominal or banded variables).
We will show you why it is never necessary to use dummy (indicator) variables, though it might sometimes be helpful to do so, since it may make it easier to use some form of automatic selection of predictor variables. We look at different ways of pre-processing the possible predictor variables to encourage them to perform better (Principal Components, percentile splits - sometimes called "bins" - and other transformations).
The course provides several opportunities to practice the techniques introduced. These hands-on exercises use a large and realistic customer dataset. The final session of the course takes the form of a brainstorm discussion where you have the opportunity to present any special problems you may have, or have discovered during the workshop, and exploit the experience of the trainer and the other participants.
Learn How to:
- Build and assess models
- Choose and modify predictors
- Score (and re-score) individuals in a large database
- Validate and maintain your models
- Deal with the biases that can arise through repeated use of the models
- Exploit fully the opportunities (and challenges) that arise when data is available on very large numbers of customers.
Course link: http://www.sas.com/apps/wtraining2/coursedetails.jsp?ctry=AU&course_code=AUS676
Bio Notes
Dr Tony Woods, Ph.D. (Statistics), B.Sc. (Mathematics), M.A. (Literature, Philosophy), is an international consultant, trainer and author who has advised major companies around the world including Marks & Spencer, Lloyds TSB, Nortel, Procter & Gamble. He has trained analysts from more than 100 companies across the world. Tony has more than 40 years experience in the commercial application of statistics, including direct marketing, credit management, business forecasting, product development, and manufacturing process analysis. He is a Chartered Statistician and a Fellow of the Royal Statistical Society. He is also on the Editorial Board of the Journal of Financial Services Marketing.
We are pleased to announce that Dr Catherine Truxillo from the SAS Institute in Cary, North Carolina, will be visiting Australia to deliver two advanced statistics classes in Sydney in early December. This is a once only opportunity to learn from one of SAS' most experienced analytics instructors.
Mixed Model Analyses using SAS
Dates: 30 November - 2 December, 2009
Location: Sydney
This course teaches students how to analyse linear mixed models using PROC MIXED. A brief introduction to analyzing generalized linear mixed models using PROC GLIMMIX is also included.
Learn how to:
- analyze data (including binary data) with random effects
- fit random coefficient models and hierarchical linear models
- analyze repeated measures data
- obtain and interpret the best linear unbiased predictions
- perform residual and influence diagnostic analysis
- deal with convergence issues
Course link: http://www.sas.com/apps/wtraining2/coursedetails.jsp?ctry=AU&course_code=AUS1223
Multilevel Modelling of Hierarchical and Longitudinal Data
Dates: 3-4 December, 2009
Location: Sydney
This course teaches students how to identify complex and dynamic patterns within multilevel data to inform a variety of decision-making needs. The course provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM) and their appropriate use in a variety of settings.
Learn how to:
- use basic multilevel models
- use three-level and cross-classified models
- use multilevel models for discrete dependent variables and generalized multilevel linear models for longitudinal data
Course link: http://www.sas.com/apps/wtraining2/coursedetails.jsp?ctry=AU&course_code=AUS1225
Bio Notes
Dr. Catherine Truxillo has been a Statistical Training Specialist at SAS for about 10 years and has written or co-written SAS training courses for advanced statistical methods including: multivariate statistics, linear and generalized linear mixed models, multilevel models, structural equation models, multiple imputation methods for missing data, statistical process control, design and analysis of experiments, and cluster analysis. Although she primarily works with advanced statistics topics, she also teaches SAS courses using SAS/IML (the interactive matrix language), IML Studio, Enterprise Guide, SAS Model Manager and JMP software.
Catherine's previous experiences with teaching, statistical consulting, and software design led her to seek a job teaching statistics for SAS.
Before moving to SAS, Catherine completed her Ph.D. in Social Psychology with an emphasis in Statistics at The University of Texas at Austin. While at UT Austin, she completed an internship with the Math and Computer Science department's statistical consulting help desk and taught a number of undergraduate courses. While teaching and performing her own graduate research, she worked for a software usability design company conducting experiments to assess the ease-of-use of various software interfaces and website designs.
