BUS5CA: Customer Analytics and Social Media

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BUS5CA Customer Analytics and Social Media
Semester 2, 2021
Assignment 2
Customer Segmentation and Profiling
Release Date: 13th September 2021
Due Date: 3rd October 2021 (Sunday) @ 11:59pm
Assignment Type: Individual
Weight: 35%
Format of Submission: A report (electronic form) and electronic submissions of analytics
files (SAS files and R scripts) on LMS.
Learning Objective:
The objective of Assignment 2 is to develop customer analytics skills via performing
customer segmentation and profiling tasks based on a case study.
Case Study:
Customer segmentation is a pivotal task for business analytics. Customer segmentation is
the process of splitting customers into different groups with similar characteristics for
potential business value proposition. Many companies find that segmenting their customers
enable them to communicate, engage with their customers more effectively.
Future Bank is conducting an analysis on the existing customer profiles and the marketing
campaign data to identify the target customers who are mostly likely to subscribe long-term
deposits. As a member of the data analytics team, you are tasked to analyse historical data
and develop predictive models for marketing purposes. Your manager has designed a pilot
project focusing on clustering-based customer segmentation and profiling to discover
consumer insights.
Requirements:
The project is seeking knowledge and insights relating to:
• The demographics-based segments and their profiles;
• The representative behavioural profiles for each segment;
• How the produced segments can be mapped to a broader concept of segments in
Australian community.
A number of analytics tasks are designed by the team to achieve the above objectives. You
are expected to use SAS to perform clustering and profiling segments with the support of
other tools like R and/or Excel for this assignment. You are required to relate the segments
and profiles in conjunction with Roy Morgan value segments. Please use the following link
to further understand these value segments:
• http://www.roymorgan.com/products/values-segments

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Dataset:
The dataset required for this assignment is available on the remote server under the F drive:
F:\BUS5CA\Assignment2_Datasets\. The dataset is available in two formats – the csv and
the SAS formats. You should import one of these formats into your SAS project, without
having to keep a copy under your own workspace folder.
Task 1: Customer segmentation based on demographics data (10%)
By using the SAS Enterprise Miner, conduct a clustering and segment profiling based on the
demographics data (Age, Job, Marital_Status, Education).
• What are the key demographics segments for the whole dataset? Describe the main
profiles and then map them into the Roy Morgan segments.
• What are the most important variables based on each segment? (Target: Subscribed)
• Are there differences in segments for customers subscribed to long-term deposit and
those who did not? Discuss the segment differences.
[Hint: Adopt and try 5-7 clusters, interpret and map them into the Roy Morgan segments.
To identify variable importance, you need to set “Subscribed” as target. To understand the
difference in segments, you may need perform clustering separately for the subscribed
customers and the non-subscribed group. In order to do this, you may need the Filter node
from the Sample tab under SAS Enterprise Miner.]
Task 2: Customer segmentation based on behavioural data (7%)
Considering the behavioural variables in the data ( Default_Credit, Housing_Loan,
Personal_Loan), you are required to conduct a clustering and segment profiling.
• What are the key behavioural segments for the whole dataset? Describe the main
profiles.
• What are the important variables based on each segment? (Target: Subscribed)
• Are there differences in segments for customers subscribed to long-term deposit and
those who did not? Discuss the segment differences.
[Hint: Use no more than 5 clusters. You should adopt the same approach from Task 1.]
Task 3: Cross cluster analysis – demographics to behavioural segments (10%)
For each individual (both subscribers and non-subscribers), record the corresponding
demographics and behavioural clusters (based on Task 1 and Task 2 above). Perform a cross
cluster analysis in R by using demographics clusters as rows and behavioural clusters as
columns in a table.
[Hint: To do this, you may need to export your segment results from Task 1 and Task 2 (with
the Save Data node from the Utility tab and save as a .csv format) and use the R table and

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probability table functions. You should make sure that your segment results from SAS
include the customer index (the row number) and the target variable (“Subscribed”).]
• Are there any significant associations between the two types of segments? Discuss
the associations.
[Hint: Investigate the cross table based on demographic clusters and behavioural
clusters, and identify the combined segments with major associations.]
• Is there a relationship between the outcome (Subscribed) and the combined
demographics and behavioural segments identified? Explain the produced combined
segments from demographics and behavioural clusters and their associations with
the outcome (Subscribed).
[Hint: Investigate the cross table based on demographic clusters, behavioural
clusters and the outcome (Subscribed), and look at the lift of “yes” of Variable 8 as
compared to the average for each selected combined segment.]
Lift calculation example:
The lift for the combined segment of demographic segment 1 and behavioural
segment 1 = Frequency of subscribers in the combined segment of demographic
segment 1 and behavioural segment 1 / Frequency of the whole population in the
combined segment of demographic segment 1 and behavioural segment 1
Task 4: Customer segmentation based on combined demographic and behavioural data
(8%)
Instead of conducting clustering and profiling separately on demographics and behavioural
data and then working on cross cluster analysis, you are required to perform the task on the
whole data set (Age, Job, Marital_Status, Education, Default_Credit,
Housing_Loan, Personal_Loan) except the target variable with the SAS Enterprise
Miner.
• What are the key segments for the whole dataset? Describe the main profiles.
• What are the important variables considering the outcome? (Target: Subscribed)
• Are there different segments and profiles identified (as compared to what were
produced in Task 3)? If yes, what are they? Discuss the differences.
You are required to:
a) Prepare a written report with answers for the above four key tasks. (You can use an
appendix for any additional screenshots which you feel are important for the report.)
The report should be named as: StudentID_Assignment2_Report.doc
b) Save the SAS project for Tasks 1, 2, and 4 above as SPK files with the name, e.g.
StudentID_Assignment 2_Task_N.spk
c) Save the R code for Task 3 as: StudentID_Assignment 2_Task_3.R
d) Submit the written report and all the SAS Model files and the R file (or the Excel file if
any) to the LMS Assignment submission site.

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Report Guidelines
1. The report should consist of a table of contents, an introduction, and logically organised
sections/topics, a conclusion and a list of references where necessary.
2. Choose a fitting sequence of sections/topics for the body of the report.
3. You must include diagrams, tables and charts from the analytics solutions to effectively
present your results. (Use Alt + Print Screen to capture screenshots if needed).
4. Page limit: ten (10) pages for the main report writing but not more than fifteen (15)
pages including appendices.
5. Reports should be written in Microsoft Word (font size 11) and submitted as a Word file.
6. Final submission will comprise two separate submissions:
a. StudentID_Assignment2_Report.doc (should not be zipped);
b. StudentID_Assignment2_AllFiles.zip (in zip format) including all analytics files
– all the SAS SPK files and the R file (or the Excel file if any).

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Marking Rubrics
A grade will be awarded to each of the tasks and then an overall mark determined for the
entire assessment. The rubric below gives you an idea of what you must achieve to earn a
certain ‘grade’.
As a general rule, to meet a ‘C’, you must first satisfy the requirements of a ‘D’. And for an
‘A’, you must first satisfy the requirements of a ‘B’, which must of course first meet the
requirements of a ‘C’ and so on.
The marking rubric for this assignment is given below.
Criterion Pass Credit Distinction High Distinction
Case Study Task 1:
Segmentation/profiling
on demographics data
(10 marks)
Limited effort to
address questions
and present
information and
insights.
Limited knowledge of
SAS Enterprise Miner.
Fair effort to address
questions and
present information
and insights.
Fair knowledge of
SAS Enterprise Miner.
Excellent effort to
address questions
and present
information and
insights.
Excellent knowledge
of SAS Enterprise
Miner.
Exceptional effort to
address questions
and present
information and
insights.
Comprehensive
knowledge of SAS
Enterprise Miner.
Case Study Task 2:
Segmentation/profiling
on behavioural data
(7 marks)
Limited effort to
address questions
and present
information and
insights.
Limited knowledge of
SAS Enterprise Miner.
Fair effort to address
questions and
present information
and insights.
Fair knowledge of
SAS Enterprise Miner.
Excellent effort to
address questions
and present
information and
insights.
Excellent knowledge
of SAS Enterprise
Miner.
Exceptional effort to
address questions
and present
information and
insights.
Comprehensive
knowledge of SAS
Enterprise Miner.
Case Study Task 3:
Cross cluster analysis
(10 marks)
Limited effort to
address questions
and present
information and
insights.
Limited knowledge of
R and other
supporting tools.
Fair effort to address
questions and
present information
and insights.
Fair knowledge of R
and other supporting
tools.
Excellent effort to
address questions
and present
information and
insights.
Excellent knowledge
of R and other
supporting tools.
Exceptional effort to
address questions
and present
information and
insights.
Comprehensive
knowledge of R and
other supporting
tools.
Case Study Task 4:
Segmentation/profiling
on combined data
(8 marks)
Limited effort to
address questions
and present
information and
insights.
Limited knowledge of
SAS Enterprise Miner.
Fair effort to address
questions and
present information
and insights.
Fair knowledge of
SAS Enterprise Miner.
Excellent effort to
address questions
and present
information and
insights.
Excellent knowledge
of SAS Enterprise
Miner.
Exceptional effort to
address questions
and present
information and
insights.
Comprehensive
knowledge of SAS
Enterprise Miner.
Other Information
• Standard plagiarism and collusion policy, and extension and special consideration
policy of this university apply to this assignment.
• A cover sheet is NOT required. By submitting your work online, the declaration on
the university’s assignment cover sheet is implied and agreed to by you.

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Appendix – Attribute Information
This section contains a description of the attributes of the dataset.
{‘name of the column’: ‘description’}
Input variables:
1 - Age (numeric)
2 - Job: career type (categorical: 'admin.', 'blue-collar', 'entrepreneur', 'housemaid',
'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed')
3 - Marital_Status: marital status (categorical: 'divorced', 'married', 'single'; note: 'divorced'
means divorced or widowed)
4 - Education (categorical: 'Primary_Education', 'Secondary_Education', 'Tertiary_Education',
'Professional_Education')
5 - Default_Credit: has a credit in default? (binary: 'yes', 'no')
6 - Housing_Loan: has a home loan? (binary: 'yes', 'no')
7 - Personal_Loan: has a personal loan? (binary: 'yes', 'no')
Output variable (desired target):
8 - Subscribed - has the client subscribed a long-term deposit? (binary: 'yes', 'no')
The dataset is adapted from:
S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank
Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014.