Customer Segmentation and Profiling With RFM Analysis

Sabuncu, İbrahim; Türkan, Edanur; Polat, Hilal

doi:10.30685/tujom.v5i1.84

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The way to do a customer profile is to collect similar customer data and then put it together and analyze it. In addition, the shape that has been formed can also be used to find new customers [12].…”

Section: Customer Profilingmentioning

confidence: 99%

The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering

Barus¹,

Nathasya²,

Pangaribuan³

2023

MMEP

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The vast development of information technology causes an explosion in the amount of data, yet the data must be processed to obtain useful insights. The use of data is needed to study the needs, behavior, and customer's value which are meant to build better relationships or what is often referred to Customer Relationship Management (CRM). As the company grows, data is getting abundant and more difficult to interact directly with customers and problems such as marketing campaigns that are less effective can result in losses if not immediately addressed. Therefore, customer segmentation was carried out using recency, frequency, and monetary (RFM) as variables and K-Means clustering by determining the number of clusters using the elbow method and silhouette score. Based on the analysis results, there are three types of clusters, categorized as best customers, may not lost customers, and average customers.

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Section: Customer Profilingmentioning

confidence: 99%

The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering

Barus¹,

Nathasya²,

Pangaribuan³

2023

MMEP

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“…Selanjutnya pada hasil pembuatan segmen pelanggan dengan algoritma AP, secara keseluruhan dapat disimpulkan bahwa segmen yang dihasilkan memiliki karakteristik yang lebih jelas dari pada hasil dari algoritma DBSCAN. Hal ini memudahkan dari sisi interpretasi dan menarik keterkaitan dengan konsep customer relationship management (CRM) yang selanjutnya menjadi dasar dalam perancangan strategi marketing bagi bisnis [25]- [27]. Terkait penjelasan detail dari segmen yang dihasilkan dengan algoritma AP, segmen pertama terdiri dari 3 data dengan pengaruh dari variabel frequency dimana hasil segmentasi ini berdasarkan pelanggan yang sudah melakukan transaksi lebih dari sekali.…”

Section: Analisa Segmentasi Pelangganunclassified

Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation

Agustino¹,

Budaya²,

Harsemadi³

et al. 2023

SISFOKOM

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The increasingly complex business environment necessitates businesses to design more effective and efficient strategies for company development, including market expansion. To understand customer behaviors, customer data analysis becomes crucial. One common approach used to group customers is segmentation based on RFM analysis (Recency, Frequency, and Monetary). This study aims to compare the performance of two clustering algorithms, namely DBSCAN and Affinity Propagation (AP), in providing customer profile segment recommendations using RFM analysis. DBSCAN algorithm is employed due to its ability to identify arbitrarily shaped clusters and handle data noise. On the other hand, Affinity Propagation (AP) algorithm is chosen for its capability to discover cluster centers without requiring a pre-defined number of clusters. The transaction dataset used in this research is obtained from one of the business incubator tenants at STIKOM Bali. The dataset undergoes preprocessing steps before being segmented using both DBSCAN and AP algorithms. Performance evaluation of the algorithms is conducted using the Silhouette Scores and Davies-Bouldin Index (DBI) matrices. The research findings indicate that the AP algorithm outperforms DBSCAN in this customer segmentation case. The AP algorithm yields Silhouette Scores of 0.699 and DBI of 0.429, along with recommendations for 4 customer segments. Furthermore, further analysis is performed on the AP results using a statistical approach based on the mean values of each segment for the RFM variables. The four customer segments generated by the AP algorithm, based on the mean values of the RFM variables, can be associated with the concept of customer relationship management.

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“…During the RFM application, first, recency value as the time between the last transaction and the present, frequency value as the number of transactions, and monetary value as the price of the transactions were calculated for each customer in each cluster based on products and contracts (Zalaghi and Varzi, 2014;Kadir and Achyar, 2019;Maraghi et al, 2020). All criteria scores were then combined and ranked to form an overall RFM score between 1 and 5 (Zalaghi and Varzi, 2014;Sabuncu et al, 2020). Second, according to the average RFM scores, clusters were prioritized as seen in Table 2.…”

Section: Data Mining and Customer Segmentationmentioning

confidence: 99%

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Serper¹,

Şen²,

Uslu³

2022

ibr

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Optimization models enable organizations to find the best solution and respond to the demand from an uncertain environment and stochastic process promptly and with less engineering effort. This study aims to optimize the number of seasonal agents and customer prioritization needed for a call center system using big data analytics and discrete event simulations to improve customer satisfaction. The study was carried out based on data from a leading heating and ventilation company's call center. The K-means clustering technique was used to determine customer segmentation on 6-million-customer data. For prioritization, the making of a Recency-Frequency-Monetary (RFM) analysis was applied. The system was modeled using ARENA simulation software, and performance parameters were measured depending on the segments obtained. The results show that the simulation model performed with data analytics gives better results for a beneficial financial impact with numerical values in customer prioritization, reducing the average waiting time of the most prioritized customers by more than 90%, and for the least prioritized customers, it increased the average waiting time by approximately just 40%. However, with the company segments, the increase in the average waiting time of the least prioritized customers was approximately 300%.

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Customer Segmentation and Profiling With RFM Analysis

Cited by 11 publications

References 16 publications

The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering

The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering

Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation

Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

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