Customer clustering using RFM analysis

Pakyurek, Muhammet; Sezgin, Mehmet Selman; Kestepe, Sedat; Bora, Busra; Duzagac, Remzi; Yıldız, Olcay Taner

doi:10.1109/siu.2018.8404680

Cited by 6 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scores are calculated for each of the three dimensions. Subsequently, it constructs segments according to threedimensional classes [15], [16], [17], [18].…”

Section: Literature Researchmentioning

confidence: 99%

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Seo

Yoo²

2023

IEEE Access

View full text Add to dashboard Cite

With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer's churn probability and shopping propensity without the burden of marketing costs in the online shopping mall.

show abstract

“…The scores are calculated for each of the three dimensions. Subsequently, it constructs segments according to threedimensional classes [15], [16], [17], [18].…”

Section: Literature Researchmentioning

confidence: 99%

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Seo

Yoo²

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To optimize marketing results, both customer segmentation and customer targeting are needed [16]. The transactional data is first subjected to an RFM analysis [17,18], after which clustering techniques like standard K-means [19][20][21], Fuzzy C-means [22], and Repetitive Median based K-Means (RM KMeans) clustering algorithms [23] are used. Following that, the clusters are further evaluated to segment customers appropriately.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the study, customers who had recently invested a lot of money and purchased a lot of items were much more likely to react to potential promotions. As a result, the scope of LRFMV research has been broad [19]. Through length, recency, frequency, monetary and volume, the customer relationship matrix assists management in identifying the characteristics of four different types of customer traits [39].…”

Section: Feature Extractionmentioning

confidence: 99%

LRFMV: An efficient customer segmentation model for superstores

et al. 2022

View full text Add to dashboard Cite

The Recency, Frequency, and Monetary model, also known as the RFM model, is a popular and widely used business model for determining beneficial client segments and analyzing profit. It is also recommended and frequently used in superstores to identify customer segments and increase profit margins. Later, the Length, Recency, Frequency, and Monetary model, also known as the LRFM model, was introduced as an improved version of the RFM model to identify more relevant and exact consumer groups for profit maximization. Superstores have a varying number of different products. In RFM and LRFM models, the relationship between profit and purchased quantity has never been investigated. Therefore, this paper proposed an efficient customer segmentation model, namely LRFMV (Length, Recency, Frequency, Monetary and Volume) and studied the profit-quantity relationship. A new dimension V (volume) has been added to the existing LRFM model to show a direct profit-quantity relationship in customer segmentation. The V stands for volume, which is derived by calculating the average number of products purchased by a frequent superstore client in a single day. The data obtained from feature extraction of the LRMFV model is then clustered by using conventional K-means, K-Medoids, and Mini Batch K-means methods. The results obtained from the three algorithms are compared, and the K-means algorithm is chosen for the superstore dataset of the proposed LRFMV model. All clusters created using these three algorithms are evaluated in the LRFMV model, and a close relationship between profit and volume is observed. A clear profit-quantity relationship of items has yet not been seen in any prior study on the RFM and LRFM models. Grouping customers aiming at profit maximization existed previously, but there was no clear and direct depiction of profit and quantity of sold items. This study applied unsupervised machine learning to investigate the patterns, trends, and correlations between volume and profit. The traits of all the clusters are analyzed by the Customer-Classification Matrix. The LRFMV values, larger or less than the overall average for each cluster, are identified as their traits. The performance of the proposed LRFMV model is compared with the legacy RFM and LRFM customer segmentation models. The outcome shows that the LRFMV model creates precise customer segments with the same number of customers while maintaining a greater profit.

show abstract

“…Although there are a lot of studies showing that RFM model can be used for customer segmentation and profiling in the world such as (Piersma & Jonker, 2004), (Wei et al, 2010), (Namvar et al, 2011) , (Maryani & Riana, 2017) there are very few case studies in Turkey. These few studies applied on different sectors e-commerce sports shop (Birant, 2011), B2B industrial markets (Ekergil & Ersoy, İbrahim SABUNCU & Edanur TÜRKAN & Hilal POLAT 2016), hotel (Dursun & Caber, 2016), pizza restaurant chain (Sarvari et al, 2016), grocery chain (Peker et al, 2017), tourism (Pakyurek et al, 2018), airline (Altan, 2019), e-retailer (Kabasakal, 2020) but no other study on the fuel retailing sector could be found.…”

Section: Tujom (2020) 5 (1): 22-36mentioning

confidence: 99%

Customer Segmentation and Profiling With RFM Analysis

Sabuncu¹,

Türkan²,

Polat³

2020

tujom

View full text Add to dashboard Cite

This paper is a case study on segmentation and profiling of customers according to their lifetime value by using the RFM (Recency, Frequency and Monetary Value) model which is an analytical method for behavioral customer segmentation. Real customer data that is gathered from a fuel station in Istanbul, Turkey is used for the case study. The data contain 1015 customers’ arrival frequency, last arrival date and total spend amount in the first half of 2016, and 10 descriptor variables of customers. First, demographic characteristics of fuel station customers were analyzed by descriptive statistics. Then customers' RFM score was calculated through SPSS program, and customers were divided into 5 segments according to their RFM scores by cluster analysis. Finally, the customer profile of segments has been created by using Correspond analysis and Discriminant analysis. Although fuel station managers think that the most valuable customer for their company are automobile drivers, result of the analysis suggests that the most valuable customers are Truck drivers. At the end of the paper, recommendations are made based on customer profiles of two most valuables segments that are named VIP and GOLD.

show abstract

Customer clustering using RFM analysis

Cited by 6 publications

References 4 publications

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

LRFMV: An efficient customer segmentation model for superstores

Customer Segmentation and Profiling With RFM Analysis

Contact Info

Product

Resources

About