Telekomünikasyon Sektöründe PSO ile Müşteri Bölümlemesi

Year 2018, Volume: 11 Issue: 2, 163 - 173, 30.04.2018

Mihrimah Özmen , Yılmaz Delice Emel Kızılkaya Aydoğan

https://doi.org/10.17671/gazibtd.368460

Cited By: 3

Abstract

Rekabetçi
piyasa ekonomisi koşullarında, işletmelerin gelişiminde etkili olan en önemli
kaynak müşterilerdir. Farklı müşteri gruplarının tercihlerini, alışveriş
tutumlarını ve fiyat duyarlılıklarını anlamak pazarlama faaliyetlerinin
yönelimi açısından çok önemlidir. Bu durumda müşteri segmentasyonu hedef
pazardaki uygun müşteri gruplarını seçmek için kullanılmaktadır. Bu çalışmada
Türkiye'nin ilk 100 telekomünikasyon şirketlerinden birine müşteri
segmentasyonu uygulanmıştır. Çalışmada yer alan firma, veri ambarında müşteri
davranışlarıyla ilgili çağrı detayları, fatura bilgisi, müşteri demografik
özellikleri gibi çok miktarda veri toplamıştır. Bu verilerin boyutu, manuel
analizin mümkün olmadığı kadar büyüktür. Bununla birlikte; bu veriler
operasyonel ve stratejik amaçlar için uygulanabilecek değerli bilgileri
barındırmaktadır. Bu verilerden anlamlı bilgi çıkarmak için gelişmiş veri
madenciliği teknikleri gereklidir. Bu çalışmada, PSO tabanlı kümeleme tekniği
ve DB uygunluk fonksiyonu ile müşteri segmentleri belirlenmiştir. 

Keywords

Müşteri Segmentasyonu, Kümeleme, Meta Sezgisel Optimizasyon, PSO

Customer Segmentation with PSO in Telecommunication Sector

Abstract

Under the
competitive market economy conditions, the most important source of the
development of businesses is the customers. Understanding the preferences,
shopping attitudes and price sensitivities of different customer groups is very
important in terms of the direction of marketing activities. In this case,
customer segmentation is used to select the appropriate customer groups in the
target market. In this study, customer segmentation was applied to one of the
Turkey's top 100 telecommunication companies. The company involved in the study
collects a lot of data on customer behaviors such as call details, billing
information, customer demographics, etc. in the data warehouse. The size of
this data is so large that manual analysis is not possible. However, these data
contain valuable information that can be applied for operational and strategic
purposes. Advanced data mining techniques are required to obtain meaningful
information from these data. In this study, customer segments were identified
with PSO-based clustering technique and DB fitness function.

Keywords

Customer Segmentation, Clustering, Meta Heuristic Optimization, PSO

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