Authors :
Asif Iqbal; Rajeev Ranjan Pandey; Subhraneel Bagchi; Saikat Ruj; Sujata Dawn
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2867ba2m
DOI :
https://doi.org/10.5281/zenodo.8146941
Abstract :
The rise of numerous competitors and
entrepreneurs which has led to a great deal of
competition among businesses, compelling them to seek
out new customers while retaining their existing ones.
Consequently, the importance of delivering exceptional
customer service has become crucial, regardless of a
business's size or scale [2]. Moreover, understanding the
unique needs of each customer is paramount to
providing targeted support and developing personalized
customer service strategies. This level of comprehension
can be achieved through the implementation of a well-
structured customer service framework, as different
customer segments often share similar market
characteristics [5].
To tackle the challenges posed by a large customer
base, the integration machine learning has gained
traction, surpassing traditional market analytics
methods that tend to falter under such circumstances.
This paper adopts the k-means clustering algorithm to
address this issue [8]. The implementation of the k-
Means algorithm, facilitated by the Sklearn library
(refer to the Appendix), involves training a program
using a dataset comprising 100 patterns and two factors
Keywords :
data mining; machine learning; customer segment; k-Mean algorithm; sklearn; extrapolation.
The rise of numerous competitors and
entrepreneurs which has led to a great deal of
competition among businesses, compelling them to seek
out new customers while retaining their existing ones.
Consequently, the importance of delivering exceptional
customer service has become crucial, regardless of a
business's size or scale [2]. Moreover, understanding the
unique needs of each customer is paramount to
providing targeted support and developing personalized
customer service strategies. This level of comprehension
can be achieved through the implementation of a well-
structured customer service framework, as different
customer segments often share similar market
characteristics [5].
To tackle the challenges posed by a large customer
base, the integration machine learning has gained
traction, surpassing traditional market analytics
methods that tend to falter under such circumstances.
This paper adopts the k-means clustering algorithm to
address this issue [8]. The implementation of the k-
Means algorithm, facilitated by the Sklearn library
(refer to the Appendix), involves training a program
using a dataset comprising 100 patterns and two factors
Keywords :
data mining; machine learning; customer segment; k-Mean algorithm; sklearn; extrapolation.
