Customer Behaviour Segmentation Among Mobile Service Providers In Kenya Using K-Means Algorithm
Domain:
socioeconomicRecord type:
paper
Creator:
KhaRimCal
Publisher:
Host:
In today’s competitive environment, operators are
investing in understanding their customers better,
especially their most profitable customer groups and the
groups that have the biggest potential to become such. By
segmenting customers based on their behavior, operators
can better target their actions, such as launching tailored
products and target one-to-one marketing, to meet the
customer expectations. The general objective of the study
is to provide customer behavior segmentation in mobile
telecommunication markets using K-means Algorithm. The
specific objectives include to handle multidimensionality
data using K-means algorithm with Principal component
analysis, to determine the value of parameter K (number of
clusters) using stability plot before clustering, to use
financial variables (mean monthly charges) for each
frequently used service as inputs in k-means for
Segmentation, to evaluate Clustering results and determine
the most profitable segment using completely randomized
design (CRD).The experiment to achieve the objectives
was being done on R software. Results show that Cluster 3
and 1 are the most profitable segments. The operators
often need to design distinguishable marketing strategy
based on different behavior of their mobile subscribers in
order to improve their marketing result and revenue.
investing in understanding their customers better,
especially their most profitable customer groups and the
groups that have the biggest potential to become such. By
segmenting customers based on their behavior, operators
can better target their actions, such as launching tailored
products and target one-to-one marketing, to meet the
customer expectations. The general objective of the study
is to provide customer behavior segmentation in mobile
telecommunication markets using K-means Algorithm. The
specific objectives include to handle multidimensionality
data using K-means algorithm with Principal component
analysis, to determine the value of parameter K (number of
clusters) using stability plot before clustering, to use
financial variables (mean monthly charges) for each
frequently used service as inputs in k-means for
Segmentation, to evaluate Clustering results and determine
the most profitable segment using completely randomized
design (CRD).The experiment to achieve the objectives
was being done on R software. Results show that Cluster 3
and 1 are the most profitable segments. The operators
often need to design distinguishable marketing strategy
based on different behavior of their mobile subscribers in
order to improve their marketing result and revenue.