Reference no: EM133004308

BUS5CA Customer Analytics and Social Media - La Trobe University

Assignment - Customer Segmentation and Profiling

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.

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

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

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

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.

• 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

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.

Attachment:- Customer Segmentation and Profiling.rar