Customer Segmentation by using Annual E-Invoice Data

Fahrettin Burak Demir; Gürkan Çelik

Makale detayları

Customer Segmentation by using Annual E-Invoice Data

Yazarlar:

Fahrettin Burak Demir | Gürkan Çelik

Yayıncı:

Fırat University

Ana Başlıklar:

Bilgi Çıkarma ve Füzyon | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.979-991

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317065

pISSN:

1308-9072

Anahtar kelimeler:

Özet:

 As businesses navigate the digital landscape, the proliferation of electronic transactions has led to an abundance of valuable data that can be harnessed for strategic decision-making. This study explores the application of CRM and RFM analysis for customer profiling and segmentation, utilizing e-invoice data as a rich source of information. By leveraging these advanced statistical techniques, the research aims to uncover hidden patterns within electronic transaction records, allowing for the identification of distinct customer segments based on their purchasing behavior. The methodology involved collecting and pre-processing one year of e-invoice data from Fit IT Company, followed by applying statistical models to uncover underlying structures and relationships. Furthermore, the research examines the implications of customer segmentation on marketing strategies, customer relationship management, and personalized service offerings. CRM and RFM analyses were performed on the annual sales data obtained as a result of e-invoice usage service to customers. When the results of the analysis were analyzed, the number of transactions belonging to the sender, recipient, and parties in the top 10 every month were extracted. It has been demonstrated that customer segmentation can be conducted more comprehensively by using CRM and RFM analyses together. While CRM analysis focuses on transaction volume and customer relationships, RFM analysis provides a more detailed perspective on customer behavior by evaluating purchase frequency, recency, and monetary value. In the study, by analyzing e-invoice data through these two methods, the most valuable customer groups were identified, and how strategic marketing approaches can be developed for these groups was illustrated. The combined use of CRM and RFM analyses allows for more accurate customer segmentation based on both transaction volume and spending habits. This approach concludes that strategies can be developed to increase customer loyalty, optimize marketing strategies, and improve business performance.

 As businesses navigate the digital landscape, the proliferation of electronic transactions has led to an abundance of valuable data that can be harnessed for strategic decision-making. This study explores the application of CRM and RFM analysis for customer profiling and segmentation, utilizing e-invoice data as a rich source of information. By leveraging these advanced statistical techniques, the research aims to uncover hidden patterns within electronic transaction records, allowing for the identification of distinct customer segments based on their purchasing behavior. The methodology involved collecting and pre-processing one year of e-invoice data from Fit IT Company, followed by applying statistical models to uncover underlying structures and relationships. Furthermore, the research examines the implications of customer segmentation on marketing strategies, customer relationship management, and personalized service offerings. CRM and RFM analyses were performed on the annual sales data obtained as a result of e-invoice usage service to customers. When the results of the analysis were analyzed, the number of transactions belonging to the sender, recipient, and parties in the top 10 every month were extracted. It has been demonstrated that customer segmentation can be conducted more comprehensively by using CRM and RFM analyses together. While CRM analysis focuses on transaction volume and customer relationships, RFM analysis provides a more detailed perspective on customer behavior by evaluating purchase frequency, recency, and monetary value. In the study, by analyzing e-invoice data through these two methods, the most valuable customer groups were identified, and how strategic marketing approaches can be developed for these groups was illustrated. The combined use of CRM and RFM analyses allows for more accurate customer segmentation based on both transaction volume and spending habits. This approach concludes that strategies can be developed to increase customer loyalty, optimize marketing strategies, and improve business performance.

 As businesses navigate the digital landscape, the proliferation of electronic transactions has led to an abundance of valuable data that can be harnessed for strategic decision-making. This study explores the application of CRM and RFM analysis for customer profiling and segmentation, utilizing e-invoice data as a rich source of information. By leveraging these advanced statistical techniques, the research aims to uncover hidden patterns within electronic transaction records, allowing for the identification of distinct customer segments based on their purchasing behavior. The methodology involved collecting and pre-processing one year of e-invoice data from Fit IT Company, followed by applying statistical models to uncover underlying structures and relationships. Furthermore, the research examines the implications of customer segmentation on marketing strategies, customer relationship management, and personalized service offerings. CRM and RFM analyses were performed on the annual sales data obtained as a result of e-invoice usage service to customers. When the results of the analysis were analyzed, the number of transactions belonging to the sender, recipient, and parties in the top 10 every month were extracted. It has been demonstrated that customer segmentation can be conducted more comprehensively by using CRM and RFM analyses together. While CRM analysis focuses on transaction volume and customer relationships, RFM analysis provides a more detailed perspective on customer behavior by evaluating purchase frequency, recency, and monetary value. In the study, by analyzing e-invoice data through these two methods, the most valuable customer groups were identified, and how strategic marketing approaches can be developed for these groups was illustrated. The combined use of CRM and RFM analyses allows for more accurate customer segmentation based on both transaction volume and spending habits. This approach concludes that strategies can be developed to increase customer loyalty, optimize marketing strategies, and improve business performance.

 İşletmeler, dijital alanda gezinirken, elektronik işlemlerin yaygınlaşması stratejik karar alma için kullanılabilecek çok sayıda değerli veriye yol açmıştır. Bu çalışma, e-fatura verilerini zengin bir bilgi kaynağı olarak kullanarak müşteri profili ve segmentasyonu için CRM ve RFM analizinin uygulanmasını araştırmaktadır. Bu gelişmiş istatistiksel tekniklerden yararlanarak, elektronik işlem kayıtlarındaki gizli kalıpları ortaya çıkarmayı ve satın alma davranışlarına göre farklı müşteri segmentlerinin belirlenmesi amaçlamaktadır. Metodoloji, Fit IT Companyden bir yıllık e-fatura verisinin toplanmasını ve ön işlenmesini, ardından altta yatan yapıları ve ilişkileri ortaya çıkarmak için istatistiksel modeller uygulanmasını içermektedir. Ayrıca çalışma, müşteri segmentasyonunun pazarlama stratejileri, müşteri ilişkileri yönetimi ve kişiselleştirilmiş hizmet teklifleri üzerindeki etkilerini incelemektedir. CRM ve RFM analizleri, müşterilere e-fatura kullanım hizmeti sonucunda elde edilen yıllık satış verileri üzerinde gerçekleştirilmiştir. Analiz sonuçları incelendiğinde, her ay ilk 10da yer alan gönderici, alıcı ve taraflara ait işlem sayısı çıkarılmıştır. CRM ve RFM analizlerinin birlikte kullanılmasıyla müşteri segmentasyonunun daha kapsamlı bir şekilde yapılabileceği gösterilmiştir. CRM analizi işlem hacmi ve müşteri ilişkilerine odaklanırken, RFM analizi satın alma sıklığı, yakınlık ve parasal değeri değerlendirerek müşteri davranışı hakkında daha detaylı bir bakış açısı sunmaktadır. Çalışmada, e-fatura verilerinin bu iki yöntemle analiz edilmesiyle en değerli müşteri grupları belirlenmiş ve bu gruplara yönelik stratejik pazarlama yaklaşımlarının nasıl geliştirilebileceği gösterilmiştir. CRM ve RFM analizlerinin birlikte kullanılması, hem işlem hacmi hem de harcama alışkanlıklarına göre daha doğru müşteri segmentasyonuna olanak sağlamaktadır. Bu yaklaşım, müşteri sadakatini artırmak, pazarlama stratejilerini optimize etmek ve iş performansını iyileştirmek için stratejiler geliştirilebileceği sonucuna varmaktadır.

Makale detayları

Customer Segmentation by using Annual E-Invoice Data

Yazarlar:

Fahrettin Burak Demir | Gürkan Çelik

Yayıncı:

Fırat University

Ana Başlıklar:

Bilgi Çıkarma ve Füzyon | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.979-991

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317065

pISSN:

1308-9072

CRM Analyses

Customer Segmentation

Digital Business

e-Invoice

RFM AnalysesCRM Analizleri

Müşteri Segmentasyonu

Dijital İşletme

e-Fatura

RFM Analizleri

Sayı

Makale detayları

Yazarlar: Fahrettin Burak Demir | Gürkan Çelik

Yayıncı: Fırat University

Ana Başlıklar: Bilgi Çıkarma ve Füzyon | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi: Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.979-991

Belge Tipi: Makale

Yayın Tarihi: 9/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001317065

pISSN: 1308-9072

Sayı

Yazarlar:

Fahrettin Burak Demir | Gürkan Çelik

Yayıncı:

Fırat University

Ana Başlıklar:

Bilgi Çıkarma ve Füzyon | Veri Yönetimi ve Veri Bilimi (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.979-991

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317065

pISSN:

1308-9072