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: yu-wang.chen@manchester.ac.uk

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

Yu-wang.Chen@manchester.ac.uk

SAS Data Science and Advanced Analytics Forum, April 26 – 27, 2017 | Cary, NC