Reference no: EM132135301
Objective:
The objective of assignment 2 is to develop customer analytics skills via performing customer segmentation and profiling tasks in the following 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 the potential business value proposition. Many companies find that segmenting their customers enable them to communicate, engage with their customers more effectively.
Bit Bank is conducting an analysis of their existing customer base who have been involved in direct marketing campaigns based on outbound calls and inbound calls hijacking. The outbound calls are performed by call centre agents attempting to sell long-term deposits to Bit's existing customers. This is also done with inbound calls hijacking when a customer calls the call centre, he or she is proposed with a potential subscription.
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 demographic-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 the 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 Excel or R 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:
The breakdown of the tasks is:
1. Customer segmentation based on demographic data
Conduct a clustering and segment profiling based on the demographic data (Age, Career, Marital_Status, Education).
- What are the key demographic-based segments? Please 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 deposit 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 the target. To understand the difference in segments you may need to 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.
2. Customer segmentation based on behavioural data
Considering the behavioural variables in the data (Default_Credit, Mortgage, Personal_Loan), you are required to conduct a clustering and segment profiling.
- What are the key behavioural segments? Please 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?
Hint: Use no more than 5 clusters.
3. Cross-cluster analysis - demographic to behavioural segments
For each individual, record the corresponding demographic and behavioural cluster (based on parts 1 and 2 above).
Perform a cross-cluster analysis by using demographic 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 Save Data node from Utility tab) and use R table and probability table function.
- Are there any significant associations between the two types of segments? Discuss briefly. Hint: Investigate cross table and identify combined segments with major associations
- Is there a relationship between the outcome (Subscribed) and the combined demographic and behavioural segments identified? Explain the produced combined segments from demographic 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.
4. Customer segmentation based on combined demographic and behavioural data
Instead of conducting clustering and profiling separately on demographic and behavioural data and then working on cross-cluster analysis, you are required to perform the task on the whole data set (Age, Career, Marital_Status, Education, Default_Credit, Mortgage, Personal_Loan) except the target variable.
- What are the key segments? Please 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 step 3) and if yes, what are they?
You are required to:
a) Prepare a report with answers to above 4 key tasks.