Customer Segmentation and Profiling

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BUS5CA Customer Analytics and Social Media
Semester 2 2019
Assignment 2
Customer Segmentation and Profiling
Release Date: 19th September 2019
Due Date: 7th October 2019 @ 9:00am
Assignment Type: Individual
Weight: 30%
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.
Alpha Bank is conducting an analysis on the existing customer profiles and 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 purpose. Your manager has designed a pilot project focusing on clusteringbased
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 (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 links 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 unit
drive (BUS5CA F drive): F:_Assignment_Dataset\Assignment2. 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 keeping a copy under your own workspace folder.
Task 1: Customer segmentation based on demographics data (10%)
Conduct a clustering and segment profiling based on the demographics data (Age, Job,
Marital_Status, Education).
• What are the key demographics segments? Describe the main profiles and then map
them into 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 deposits
and those who did not?
Hint: Adopt and try 5-7 clusters, interpret and map them into 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 for the subscribed customers and
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 (5%)
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? 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 deposits
and those who did not?
Hint: Use no more than 5 clusters.
Task 3: Cross cluster analysis – demographics to behavioural segments (10%)
For each individual, record the corresponding demographics and behavioural cluster (based
on Task 1 and 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 result from Task 1 and 2 (with the
Save Data node from the Utility tab and save as a .csv format) and use the R table and
probability table functions.
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• Are there any significant associations between the two types of segments? Discuss
briefly. (Hint: Investigate the cross table and identify 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: Look at the lift of “yes” (of the variable 8) compared
to the average for each selected combined segment.
Task 4: Customer segmentation based on combined demographic and behavioural
data (5%)
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.
• What are the key segments? Describe the main profiles. What are the important
variables considering the outcome (Subscribed).
• Are there different segments and profiles identified (compared to what were
produced in Task 3) and if yes, what are they?
You are required to:
a) Prepare a report with answers for the above four key tasks. (You can use an
appendix for any additional screen shots which you feel are important for the
report.) The report should be named as: StudentID_Assignment2_Report.doc
b) Save the SAS project for Task 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 the SAS Model files and the R file (or the Excel file if
any) to the LMS Assignment submission site.
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 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 one:
    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 two:
    Segmentation/profiling
    on behavioural data
    (05 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 three:
    Cross cluster analysis
    (10 marks)
    Limited effort to
    address questions
    and present
    information and
    insights.
    Limited knowledge of
    SAS Enterprise Miner
    and other supporting
    tools.
    Fair effort to address
    questions and
    present information
    and insights.
    Fair knowledge of
    SAS Enterprise Miner
    and other supporting
    tools.
    Excellent effort to
    address questions
    and present
    information and
    insights.
    Excellent knowledge
    of SAS Enterprise
    Miner and other
    supporting tools.
    Exceptional effort to
    address questions
    and present
    information and
    insights.
    Comprehensive
    knowledge of SAS
    Enterprise Miner and
    other supporting
    tools.
    Case study task four:
    Segmentation/profiling
    on combined data
    (05 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 important 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’, ‘Professional_Education’,
    ‘Secondary_Education’, ‘Tertiary_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.

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