Reference no: EM132382346 , Length: 13 Pages

BUS5CA Customer Analytics and Social Media Assignment - Customer Segmentation and Profiling, Semester 2 2019, La Trobe University, Australia

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 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 (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 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

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

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

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.

• 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

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).

Note - it's basically on SAS ENTERPRISE MINER. Need to write interpretation - total 13 pages will not includes references and name page.

Attachment:- Customer Analytics and Social Media Assignment File.rar