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, I am tasked to analyse historical data and develop predictive models for marketing purposes.
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.
I used SAS to perform clustering and profiling segments with the support of other tools like R and Excel. I relateed the segments and profiles in conjunction with Roy Morgan value segments. To further understand these value segments, I used: • http://www.roymorgan.com/products/values-segments
Task 1: Customer segmentation based on demographics data By using the SAS Enterprise Miner, conducted a clustering and segment profiling based on the demographics data (Age, Job, Marital_Status, Education) and answered the following questions. • 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.
Task 2: Customer segmentation based on behavioural data Considering the behavioural variables in the data (Default_Credit, Housing_Loan, Personal_Loan), I conducted a clustering and segment profiling and answered the following questions. • 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.
Task 3: Cross cluster analysis – demographics to behavioural segments For each individual (both subscribers and non-subscribers), recorded the corresponding demographics and behavioural clusters (based on Task 1 and Task 2 above). Performed a cross cluster analysis in R by using demographics clusters as rows and behavioural clusters as columns in a table. I answered the following questions. • Are there any significant associations between the two types of segments? Discuss the 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).
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, I performed 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. I answered the following questions. • 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.