BUS5CA: Customer Analytics and Social Media

Added on - 04 Oct 2021

  • 6

    Pages

  • 2227

    Words

  • 14

    Views

  • 0

    Downloads

Trusted by +2 million users,
1000+ happy students everyday
Showing pages 1 to 3 of 6 pages
1
BUS5CA Customer Analytics and Social Media
Semester 2, 2021

Assignment 2
Customer Segmentation and Profiling


Release Date: 13thSeptember 2021
Due Date:3rdOctober 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:
TheobjectiveofAssignment2istodevelopcustomeranalyticsskillsviaperforming
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
projectfocusingonclustering-basedcustomersegmentationandprofilingtodiscover
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
2
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 – thecsvand
theSASformats. 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%)

Foreachindividual(bothsubscribersandnon-subscribers),recordthecorresponding
demographics and behavioural clusters (based on Task 1 and Task 2 above). Perform a cross
cluster analysis in R by using demographics clusters asrowsand behavioural clusters as
columnsin a table.

[Hint: To do this, you may need to export your segment results from Task 1 and Task 2 (with
theSaveData node from theUtilitytab and save as a.csvformat) and use the R table and
3
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.]

Istherearelationshipbetweentheoutcome(Subscribed)andthecombined
demographics and behavioural segments identified? Explain the produced combined
segments from demographics and behavioural clusters and their associations with
the outcome (Subscribed).
[Hint:Investigatethecrosstablebasedondemographicclusters,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
wholedataset(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.
desklib-logo
You’re reading a preview
Preview Documents

To View Complete Document

Click the button to download
Subscribe to our plans

Download This Document