Abstract:
Almost every industry, organization use Information Technology for their core business activities. Similarly, Small Medium Enterprises (SME(s)) try to use technologies for their businesses
and this usage has increased data generation. Like other business organizations, SMEs keen
on customer-oriented marketing and in order to foresee such a marketing strategy customer
information needed to be gathered. Hence, adopting data mining and machine learning will
increase the information gathering in terms of customers at SMEs. Information extracted from
data mining in SMEs can be used to identify SMEs’ customers and thereby, engage in target
marketing campaigns for customer bases of SMEs. SME at the initial stage of data mining lacks
high-pitched objectives due to inexperience with the concept. Since, at data repositories, there
can be data under numerous attributes, which will lead to complex data mining analytical approaches at SMEs. In order to avoid that extreme course, Principle Component Analysis (PCA)
can be practiced, so SMEs can identify which attributes significantly facilitate accurate and reliable initial customer identification. The cloth retailing dataset which comprises of demographic
features; Gender, Age, Residency information, Marital Status and Occupation first subjugated
to preprocessing and feature engineering. Then, PCA was carried out to investigate the most
significant and prominent data features that needed to be focused on when performing customer
identification. Once the PCA has done the total explained variance percentage for the first two
dimensions are 10%. With the complexity of the data dimensionality, the extension of PCA,
the Multi Component Analysis (MCA) was carried out, which improved the degree of customer
identification in terms of demographic attributes. According to PCA, it has identified the Age
and Occupation statuses were principle components and with MCA the Age group of 0-17 and
Occupation category of 10 have the greater prominence with respect to customer deification.
Considering the above facts, a classification of customers carried out with 4 definitive classes.
With this initial, sound customer identification, SMEs can focus on effective target marketing
based on customer segmentation and better customer relationship management.
