Social media analytical CRM: a case study in a bank

Name: Social media analytical CRM: a case study in a bank
Author: Duman, Ekrem

İsim	Social media analytical CRM: a case study in a bank
Yazar	Duman, Ekrem
Basım Tarihi:	2023
Basım Yeri	- IOS Press
Konu	Banking, CRM, NLP, Sentiment analysis, Social media
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1064-1246
Kayıt Numarası	b05cde10-d506-4e06-85ce-287a378905c2
Lokasyon	Industrial Engineering
Tarih	2023
Örnek Metin	The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models.
DOI	10.3233/JIFS-221619
Cilt	44

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Social media analytical CRM: a case study in a bank

Yazar Duman, Ekrem

Basım Tarihi 2023

Basım Yeri - IOS Press

Konu Banking, CRM, NLP, Sentiment analysis, Social media

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1064-1246

Kayıt Numarası b05cde10-d506-4e06-85ce-287a378905c2

Lokasyon Industrial Engineering

Tarih 2023

Örnek Metin The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models.

DOI 10.3233/JIFS-221619

Cilt 44