Customer Segmentation Analysis

This assignment focuses on customer segmentation analysis using both demographic and behavioral data. It involves creating clusters based on provided variables, analyzing the relationship between demographic and behavioral clusters, and finally segmenting customers based on combined demographic and behavioral data. The case study provides insights into identifying key customer segments for business targeting.

BUS5CA Customer Analytics and Social Media
Assignment 2 – Customer Segmentation and
Profiling
Table of Contents
Introduction:..........................................................................................................................................1
Tasks:.....................................................................................................................................................1
Case Study task 1: Customer Segmentation based on demographics data.......................................1
Case Study task 2: Customer segmentation based on behavioural data...........................................4
Case Study task 3: Cross cluster analysis – demographics to behavioural segments.........................5
Case Study task 4: Customer segmentation based on combined demographic and behavioural data
...........................................................................................................................................................6
References.............................................................................................................................................8
Appendix...............................................................................................................................................8

Introduction:
In this project, we have tried to solve the targeted marketing task, mainly seen in retail sector. For
this task we have used the customer database of Bit Bank. Using the clustering techniques, we have
tried to identify different marketing segments & help business to have effective communication with
their customers(Chen 2014).
Tasks:
Case Study task 1: Customer Segmentation based on demographics
data
The customer clustering has been performed using SAS Enterprise Miner. We have run the
K-means clustering with total 5 clusters. Only demographic variables has been used for
creating the clusters. This has been done in two ways(Iaci & Singh 2012; Trebuna et al.
2014). Firstly, the cluster analysis has been done with only demographic variables & then it
was run including the target variable: Subscription.
Without Outcome Variable included:
We have identified the total 5 clusters from the analysis.
Note: Please zoom in if the results are not very clear.

In the above plot we are exploring the distribution of each variables among the 5 clusters.
i. Cluster 1
Cluster 1 consists of fairly mix age group people. There is no significant age group
present in cluster 1. But it consists of only people who are students. People with
other career options are not included in the cluster. It is dominated by the
people with unknown education level & people who are mostly married. Couldn’t
be mapped directly to any segments from Roy Morgan.
ii. Cluster 2
It includes people of different age as well as mix of different career options.
Education level is coming out to be significant for the segments as only people
with unknown education level are included in this segment. Most number of
people in the segment are married. Couldn’t be mapped directly to any segments
from Roy Morgan.
iii. Cluster 3
It also has mix distribution of age & career. Both the variables are not significant
in this cluster. But higher number of people in the group has secondary level of
education & all of them are divorced. This is very interesting segments. People in
this segments can be mapped to “Something Better” segment from Roy Morgan.
People are educated but divorced which implies they understand their current
situation is not working out & wants something better than their current life.
iv. Cluster 4
This segment is dominated by younger generation people age ranging from 27-37
but having different career option. The education level for most of the people is
secondary & the marital status consist of half married & half single people. This
clusters can be mapped to young optimism segments from Roy Morgan. People
in this groups are most often associated with high optimism & who are always
looking to improve their prospects of life.
v. Cluster 5
This cluster includes mix of people of different age, career & education level. But
it has higher people who are married & no divorcee. Couldn’t be mapped directly
to any segments from Roy Morgan.
Note: All the clusters couldn’t be mapped to Roy Morgan Segments. This is
because the attributes defined in Roy Morgan Segments are present in the
current data set.

With Outcome Variable:
Distribution of each variables among the 5 clusters with target variable
We have identified 3 key segments from the outcome variable. Segment 1 & 4 consist of
only people who have not subscribed. The segment 2 consist of people who have
subscribed. Now, we will examine whether any demographic variable have significant roles
in defining the cluster.
If we explore each cluster based on the different demographic variables, we will be able to
find any variable which have significant role in defining the cluster. But from the above plot,
we couldn’t find any variable as such. So, there is no clear differences in segments for
customers subscribed to term deposit and those who did not.
Case Study task 2: Customer segmentation based on behavioural
data
Only behavioural variables has been used for creating the clusters. This has been done in
two ways(Seiler et al. 2013; Davis et al. 2008). Firstly, the cluster analysis has been done
with only behavioural variables & then it was run including the target variable: Subscription.

Note: all 3 behavioural variables are binary
The key segments that are created on basis of default credit & personal loan. Segment 1
consist of people who don’t have personal loan & not defaulted. Segment 2 consist of
people with personal loan & mortgage loan but has not defaulted yet. Segment 5 consist of
people who has personal loan but don’t have mortgage loan & has not defaulted yet.
Based on the behavioural variable, we have identified segment 1 as key segment as this
consist most number of people who have taken the subscription. It consist of people who
doesn’t have personal loan & has not defaulted yet.

Case Study task 3: Cross cluster analysis – demographics to
behavioural segments
Note: This has been done in excel using the pivot table. For further reference kindly refer to the excel
workbook provided.
Based on the clusters formed in task 1 for behavioural & demographic segments, we have
assigned each individual to clusters. Using excel, we have created a cross tab of both the
clusters. Visually examining the cross tab, we couldn’t see any association between the
clusters formed.
To understand the relationship between the outcome & the combined clusters, we would
examine the number of subscribed clusters falling into each cells. We would examine
number of subscriber who would fall in the combined section.
In the above cross tab, we are observing the percentage of subscribers falling into each
clusters. For example, in cluster 1 of behavioural clusters & cluster 3 of demographic
clusters there are 113.44% subscriber. The lift ratio of 13% in the segment. Similarly, the
cluster formed from 2 in demography & 5 in behavioural consists 117.86% subscriber as
compared to average of each selected segments. All the segments marked with green are
key segments for the business.

Case Study task 4: Customer segmentation based on combined
demographic and behavioural data
When the cluster algorithm is run with both demographic & behavioural variables. We have
given the maximum clusters as 5. Most significant variables is default credit as each 5
segments have people who have either default history or has not defaulted yet. The key
segment is segment 5 which consist of only students who doesn’t have personal loan.
Similarly segment 3 is the key segment as it consist of all people who have mortgage loan
but has not defaulted yet. This can be major target segment for business.

Now running the cluster analysis with the target variables. In segment 1, only customer with
subscribed people are included. In segment 1 there are mix age of people, also consist of
people with mix career & who has not defaulted yet. People who have subscribed have no
personal loan. So, the most important variable is personal loan & default credit.
References
Chen, J., 2014. Retail Customer Segmentation using SAS,
Davis, A., Gunderson, M. & Brown, M., 2008. THE EFFECT DEMOGRAPHICS HAVE ON THE
DEMAND, Florida.
Iaci, R. & Singh, A.K., 2012. Clustering high dimensional sparse casino player tracking
datasets. UNLV Gaming Research & Review Journa, 16(1), pp.21–43.
Seiler, V., Rudolf, M. & Krume, T., 2013. The influence of socio demographic variables on‐
customer satisfaction and loyalty in the private banking industry. International Journal
of Bank Marketing, 31(4), pp.235–258.
Trebuna, P., Halcinova, J. & Fil’o, M., 2014. The importance of normalization and
standardization in the process of clustering. IEEE, 12, p.381.
Appendix
Task 2: