CUSTOMER PORTFOLIO OF A CONSUMER GOODS BASED VIRTUAL STORE: IDENTIFYING CUSTOMER SEGMENTS WITH CLUSTER ANALYSIS

Yıl 2019, Cilt: 14 Sayı: 52, 356 - 371, 24.07.2019

Begüm Hattatoğlu , Çağla Şeneler Gökhan Şahin Fazlı Yıldırım

https://doi.org/10.14783/maruoneri.594975

Öz

In the last decade, analyzing and identifying
customers became an irreplaceable need for companies. This research
concentrates on discovering a company’s customer segments using different machine
learning algorithms, benchmarking different algorithms and its parameters to
conclude the best results. Improvements
in the technology provided several approaches to dive in and gain insights from
a mass amount of data. Machine learning algorithms which is one of the most
popular approaches was chosen to convey this empirical study. A dataset with
mix categorical and numeric variables is analyzed with one of the conventional
machine learning algorithms, namely Hierarchical Agglomerative Clustering Algorithm
with Gower’s distance. Kernel Principal Component Analysis is used for
preprocessing due to the existence of categorical variables. K-prototypes
Algorithm is chosen as benchmark algorithm that fits the qualities of the
dataset with mixed categorical and numeric features. Benchmarking provides verification
in respect to the accuracy of the results by evaluating the final clusters.
Also, examining different parameters and comparing their effects on analysis
results indicates the importance and vitality of them for machine learning
algorithms, which need to be enlightened to do more accurate analyses. The
results showed that both K-prototypes and HAC yield similar results proving
that clusters mostly divided appropriately. However, there are a few significant
points that are different at both algorithms’ results, which should be examined
in further study.

Anahtar Kelimeler

Machine Learning, Kernel Principal Component Analysis, Clustering, Hierarchical Agglomerative Clustering, Gower’s Distance, K-prototypes

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