AMBS Teaching & Research
Value of SAS in Business Analytics
Dr Yu-wang Chen
Alliance Manchester Business School (AMBS), The University of Manchester (UoM)
Tel: (+44) 161 275 6345
Email: [email protected]
SAS Data Science and Advanced Analytics Forum, April 26 – 27, 2017 | Cary, NC
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A “red brick” university
25 Nobel Laureates
The University of Manchester
Three core goals: World-class research, Outstanding learning and student
experience, Social responsibility
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Alliance Manchester Business School Alliance Manchester Business School was established in 1965 as one of the
UK's first two business schools.
http://www.mbs.ac.uk/about-us/
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MSc Business Analytics: Operational Research and Risk Analysis Programme Directors: Dr Yu-wang Chen & Dr Julia Handl
2006/07 ~ 2010/11: Programme launched with 12 students and recruited less
than 31 students till 2010/11
2011/12 ~ 2016/17: Increasing number of students
2013/14: Programme name updated from MSc Business Analytics: Operational
Research and Risk Analysis
2011/12 2012/13 2013/14 2014/15 2015/16 2016/17
Student No. 35 49 63 75 71 100
0
20
40
60
80
100
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MSc Business Analytics: Programme StructureSemester 1 Semester 2 Summer
Core 1: Applied Statistics Core 1: Risk, Performance and Decision Analysis
Dissertation *Core 2: Mathematical Programming and Optimization
Core 2: Simulation and Risk Analysis
Elective 1 Core 3: Data Analytics for Business Decision Making (SAS)
Elective 2 Elective 1
* Industrial dissertation projects with:
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Operations Research /
Analytics Theme
Mathematical programming and
Optimization
Applied Statistics and Business Forecasting
Simulation and Risk Analysis
Games Businesses Play
Decision Science Theme
Risk, Performance and Decision
Analysis
Psychology of Behaviour and
Decision Making
Decision Behaviour,
Analysis and Support
Data Science Theme
2013 onwards: Data Analytics for Business Decision
Making
Social Media and Web Analytics
IS Strategy and Enterprise
Information and Knowledge
Management
Analytics Applications
Selected electives from
other MSc programmes in areas such as
Global Operations
Management, Supply Chain
Management, Accounting &
Finance
Dr Dong-Ling Xu/ Dr Ludi Mikhailov
Dr Jim Freeman
Dr Julia Handl
Dr Luciana Nocollier
Prof Jian-Bo Yang/ Dr Manuel Lopez-Ibanez
Dr Oscar de Bruijn
Dr Nadia Papamichail
Dr Yu-wang Chen
Dr Weigang Wang
Prof Chris Holland
Prof Peter Kawalek
NEW in 2016: Programming in
PythonDr Richard Allmendinger/ Dr Manuel Lopez-Ibanez
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MSc Business Analytics: Features and Facts
Shift from traditional OR to data science featured MSc programme
Key learning aims for students to develop quantitative (e.g., optimisation,
statistics) and analytical (e.g., simulation, decision and risk analysis, data
analytics) skills
Exposure to a range of specialist software tools, such as SAS, Risk Solver,
Minitab, Simul8 and IDS
Received excellent student feedback and PTES (Postgraduate Taught
Experience Survey) results (an overall 97% satisfaction score of vs. the
university average of 83% in the academic year 2015/16)
https://www.heacademy.ac.uk/institutions/surveys/postgraduate-taught-experience-survey
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Why Business & Data Analytics?
Business analytics programs will continue to grow. 100+ business schools in the United States that have, or have committed
to launch, curriculum at the undergraduate and graduate levels with
degrees or certificates in business analytics
Data scientists will be the head-hunter’s best friend.The past year 2015 has seen the number of advertised data scientist jobs in
the UK increase by 22 percent, in addition to the push from the nation’s tech
sector for ‘data scientist’ to be added to the UK’s skills shortages list.http://www.itproportal.com/2016/01/08/four-
analytics-trends-to-watch-in-2016/
6 Predictions in 2017 For The $203 Billion Big Data Analytics
Market.The creation and consumption of data continues to grow by leaps and
bounds and with it the investment in big data analytics hardware, software,
and services and in data scientists and their continuing education.
http://www.forbes.com/sites/gilpress/2017/01/20/6-predictions-
for-the-203-billion-big-data-analytics-market/#609883426c66
http://www.analytics-magazine.org/http://www.bloomberg.com/video/why-data-analytics-is-the-
future-of-everything-WeneeY4LQzKJ4khYdMi9uw.html
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Why SAS? SAS provides a suite of business solutions and technologies to help
organizations support business decision making.
Information Management
High Performance Analytics
Analysis of Means
Cluster Analysis
Ensemble Models
Sample Size Computations
Categorical Data Analysis
Psychometric Analysis
Survival Analysis
Statistical Process Control
X11 & X12 Models
Decision Trees
Analysis of Variance
Survey Data Analysis
Vector Autoregressive
Models
Nonlinear
ProgrammingNetwork Flow Models
Nonparametric AnalysisARIMA
Models
Linear Programming
Interior-Point Models
Scheduling
Bayesian
R Integration
Multivariate Analysis
Neural Networks
Random Forests
Mixed Models
Design of Experiments
Predictive Modeling
Information
Theory
Reliability Analysis
Social Network AnalysisProcess Capability Analysis
Descriptive Modeling
Mixed-Integer ProgrammingD-Optimal
Multinomial Discrete
Choice
High Performance ForecastingAnalytics
Text Mining
Content Categorization
Sentiment Analysis
Business Solutions
Business IntelligenceScoring Acceleration
Predictive
Analytics
Statistical
Analysis
Employability:http://www.indeed.co.uk/
157 176 1,146 2,182 2,6084,221
29,255
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SAS Course for MSc Business Analytics BMAN60422 Data Analytics for Business Decision Making
Learning aims:
• To understand data analytics for business decision making, including classification,
clustering, predictive modelling, text mining, visual analytics, etc.
• Emphasis is placed on the use of an industry-leading software tool, SAS.
Outcomes:
• Understand the fundamentals of data analytics and a variety of data analytical
techniques
• Understand their applications in business decision making
• Skills on specialised software packages, e.g., SAS
• Independent research & teamwork skills
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Syllabus and TimetableWeek Lecture (Tuesday 10:00-12:00) Lab session (Tue, Wed or Thu)
1 Introduction to business & data analyticsGetting started with SAS & SAS Enterprise
Guide (EG)
2 Data management and manipulation SAS EG – Case study
3 Predictive modelling: decision treesIntroduction to SAS Enterprise Miner (EM) –
Case study
4 Predictive modelling: neural networks SAS EM – Case study
5 Applied clustering techniques SAS EM – Case study
6 Customer segmentation SAS EM – Case study
7Association analysis (market basket analysis &
sequence analysis)SAS EM – Case study
8 Text analytics & sentiment analysis SAS EM & Text Miner – Case study
9 Advanced analytics Group Presentation
10 Big data & visual analytics Visual & Big Data Analytics Tools
11 Revision lecture
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SAS Support & Joint Certificate SAS Big Data Skills Festival, Careers Fair & Student Ambassador Program
SAS Guest Lecture (Janice Newell - Analytics in Action: How SAS Analytics is
applied in Industry, tbc)
SAS-university Joint Certificate
• Attendance of lab sessions and completion of SAS case studies
• Pass mark for Joint Certificate – a minimum of 60% irrespective of
passing the whole module.
http://www.e-skills.com/research/research-themes/big-data-analytics/
http://support.sas.com/learn/ap/student/amb.html
http://www.sas.com/uk/academic
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SAS Course for MSc Business Analytics: Students’ FeedbackPeriod of time Number of students Evaluation scores (/5) Response rate (%)
2016/17 141 tbc tbc
2015/16 118 4.72 45%
2014/15 109 4.67 72%
2013/14 71 3.60 38%
A high proportion of Chinese students
enrolled to the course.
Business/ data analytics programmes
emerging rapidly in China
• MSc Business Analytics (Xi’An Jiao Tong -Liverpool
University http://www.xjtlu.edu.cn/en/find-a-
programme/masters/msc-business-analytics)
• School of Data Science, Fudan University
• ……
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SAS for Research Data-driven Segmentation and Prediction of Consumers’ Purchase Behaviour in
the Retail Industry (PhD & MSc research projects)
Research Background
• Marketing communications shifted away from advertising towards sales promotion
(Gilbert and Jackaria, 2002)
• Promotions have positive effects on sales, but low response rates
• Understanding consumers for the purpose of providing tailored promotions is the key
to making attractive promotions
• Customers segmentation provides an opportunity to genuinely understand
consumers’ purchasing behaviours
Gilbert, D. C., & Jackaria, N. (2002). The Efficacy of Sales Promotions in UK Supermarkets: A Consumer View. International Journal of Retail &
Distribution Management, 30(6), 315-322.
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Research Aims To measure consumers’ promotion proneness and variety seeking behaviours by using
prevalence of promotion and expected value of information respectively through dealing
with store scanner data.
To collectively analyse intrinsic and extrinsic motivations of variety seeking behaviour and
segment consumers in terms of their variety seeking behaviours in react to marketing
communications.
To distinguish and profile consumers in each behavioural segment with their demographic
characteristics by developing a response model between their demographic variables and
the associated behavioural segments.
To support retailers to make marketing strategies to increase the response rate of their
promotions for profit maximisation.
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Literature Review Brand choice model (Bucklin et al., 1998): segment consumers based on their reactions
to sales promotion in brand choice (what to buy), purchase incidence (whether to buy),
and stockpiling (how much to buy).
Variety seeking (Heilman et al., 2000, p.141): inverted U-relationship between expected
value of information and the amount of market knowledge.
Dynamic choice process (Heilman et al., 2000,
p.141)
Demographic characteristics
• Promotion proneness: income, education, family size, type
of residence, age, employment situation, and children
group (Inman et al., 2004; Teunter, 2002)
• Variety seeking: income, gender, age, education,
occupation (Skogland and Siguaw, 2004; Patterson, 2007)
Heilman, C., Bowman, D. & Wright, G., 2000. The Evolution of Brand Preferences and Choice Behaviors of Consumers New to a Market. Journal of
Marketing Research, 37(2), pp. 139-155.
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A Case Study Dataset for analysis: 589 consumers with 169678 purchase records, US salt-snack
market, IRI marketing dataset (Bronnenberg et al., 2008)
Behavioural measurements
• Promotion proneness: the extent to which a consumer is motivated to search and take advantage
of promotions to maximise the immediate purchase value.
• Value of information: reduced uncertainty about the market from trying new goods (measured by
the generalized Entropy Theory of Information ).
𝑃𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑜𝑓 𝑃𝑟𝑜𝑚𝑜𝑡𝑖𝑜𝑛 =𝑇ℎ𝑒 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠 𝑜𝑛 𝑃𝑟𝑜𝑚𝑜𝑡𝑖𝑜𝑛 𝑖𝑛 𝑎 𝑃𝑒𝑟𝑖𝑜𝑑
𝑇ℎ𝑒 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑟𝑜𝑚𝑜𝑡𝑖𝑜𝑛𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑃𝑒𝑟𝑖𝑜𝑑
𝑀𝑎𝑟𝑘𝑒𝑡 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 =𝑇ℎ𝑒 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐵𝑟𝑎𝑛𝑑𝑠 𝑡𝑟𝑖𝑒𝑑 𝑏𝑦 𝑎 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑖𝑛 𝑡ℎ𝑒𝑖𝑟 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝐿𝑖𝑓𝑒 𝐶𝑦𝑐𝑙𝑒
𝑇ℎ𝑒 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐵𝑟𝑎𝑛𝑑𝑠 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑖𝑛 𝑡ℎ𝑒 𝑀𝑎𝑟𝑘𝑒𝑡 𝐷𝑢𝑟𝑖𝑛𝑔 𝑡ℎ𝑒 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟′𝑠 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒 𝐿𝑖𝑓𝑒 𝐶𝑦𝑐𝑙𝑒=𝑛
𝑁
𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠= 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝑎𝑏𝑜𝑢𝑡 𝑎 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑀𝑎𝑟𝑘𝑒𝑡 × 𝑇ℎ𝑒 𝑂𝑏𝑡𝑎𝑖𝑛𝑎𝑏𝑙𝑒 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜n
= 𝐼 𝑀𝑝 × −log2 𝐼 𝑀𝑝 =𝑛
𝑁× −log2(
𝑛
𝑁)
Bronnenberg, B., Kruger, M. & Mela, C., 2008. The IRI Marketing Data Set. Marketing Science, 27(4), pp. 745-748.
Segment Name Prevalence of Promotion Value of Information Definition
Bargain Hunters High (0.75) Medium (0.29)Actively looking for promotions to maximise the immediate
purchase value.
Opportunistic Explorers Medium (0.58) High (0.35)Motivated to try new brands. Promotions are used to enable this
market exploration.
Promotion Averse
ExploitersLow (0.32) Medium (0.26)
Purchase brands that are well known to them. Unwilling to risk
new brands regardless of promotions.
Opportunistic Exploiters Medium (0.6) Low (0.19)Purchase items that they are familiar with to reduce the risks
involved whilst taking advantages of promotions where possible.18
SAS Segmentation Analysis
Value of Information
Prevalence of Promotion
Bargain Hunters Opportunistic Explorers Promotion Averse Exploiters Opportunistic Exploiters
Bargain Hunters Opportunistic Explorers Promotion Averse Exploiters Opportunistic Exploiters
0.00
0.25
0.50
0.75
1.00
0.1
0.2
0.3
0.4
0.5
Behavioural Charachteristics of the Consumer Segments
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SAS Profiling and Predictive Modelling
Male: Retired
Male: Retired/Not Employed
Male: Graduated High School
Male: 65+
$35,000−$44,999
Female: Retired
Female: Retired/Service Industry
Female: Some High School
Family Size: 2
Female: N/A
Female: N/A
Divorced
Male: N/A
Male: N/A
Male: N/A
$65,000−$74,999 /$100,000+
Female: Pro/Tech
Family Size: 3
Children: 12−17
Male: Pro/Tech + Manager/Admin
Male: Post Graduate Work
Male: 55−64
Family Size: 2
Children: None
0%
20%
40%
60%
80%
Bargain Hunter Opportunistic Exploiter Opportunistic Explorer Promotion Averse ExploiterIm
pro
ved
Ta
rge
ttin
g P
erf
orm
ance
Demographics
Children Code
Family Size
Female Age
Female Education
Female Occupation
Female Working Hour
Household Income
Male Age
Male Education
Male Occupation
Male Working Hour
Marital Status
Demographic Indicators of Segments
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Behavioural Evolvement
Segment transitions between two consecutive years
Cluster centroid tracking over four years
Behavioural evolvement analysis shows how competitive retail markets change over time
and how a market will look if current trends continue.
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A Brief Summary Business analytics – a rapidly emerging market.
Business analytics/ Data science programs will continue to grow.
SAS adds values to both teaching and research in AMBS.
Open platform?
Thank you!
Q&A
SAS Data Science and Advanced Analytics Forum, April 26 – 27, 2017 | Cary, NC