Analysis of Purchase History Data Based on a New Latent Class Model for RFM Analysis

Zhang, Qian; Yamashita, Haruka; Mikawa, Kenta; Goto, Masayuki

doi:10.7232/iems.2020.19.2.476

Cited by 4 publications

(2 citation statements)

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“…Data ini adalah data yang sifatnya selalu bertambah dan berubah berdasar waktu, salah satunya contoh data ini adalah data transaksi. Analisa pada data transaksi untuk segmentasi pelanggan dapat dilakukan dengan memodelkan data transaksi tersebut menjadi data Recency, Frequency, Monetary (RFM) [8], [9].…”

Section: Pendahuluanunclassified

Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM

Djun,

Gunadi,

Sariyasa

2024

jtim

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The development of business strategies, particularly in the marketing of SMEs, requires the utilization of business intelligence as the foundation for objective decision-making. This research aims to develop a business intelligence scheme for SMEs and design targeted assistance strategies for SME support institutions. The implementation of business intelligence involves leveraging transactional data from SMEs to ascertain customer segmentation and correlating it with Customer Relationship Management (CRM) strategies. Transactional data is processed into a Recency, Frequency, Monetary (RFM) data model. Customer segmentation is achieved through a clustering process using the K-Means algorithm, and the results yield distinct profiles for SME customers. Evaluation processes are conducted to determine the optimal solution for the number of customer segments. Evaluation methods, including the Elbow Method, Silhouette Scores, and Davies–Bouldin Index, are employed to determine the optimum cluster. The evaluation results indicate that the optimum cluster is 3, with the best Silhouette Score being 0.548 and Davies–Bouldin Index at 0.76. The first customer segment exhibits the highest shopping frequency and monetary value, categorizing them as active and profitable customers. Special loyalty services are recommended for this segment. The second segment, despite having the largest number of customers, exhibits a shopping frequency of only 1-2 times, with an average recency of approximately the last 2 months. These customers require effective after-sales service. The third segment consists of customers who last shopped more than 6 months ago, making them a low-priority segment. Re-engagement strategies, such as email marketing, are suggested for this segment. Support institutions can focus on CRM assistance targeting these three identified segments.

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Section: Pendahuluanunclassified

Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM

Djun,

Gunadi,

Sariyasa

2024

jtim

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“…Penelitian ini berfokus pada penerapan DBSCAN dan AP untuk menemukan solusi optimal dalam hal segmentasi data pelanggan. Data pelanggan yang dimaksud adalah model data Recency, Frequency, Monetary (RFM) [17], [18]. Hasil segmentasi akan dievaluasi.…”

Section: Pendahuluanunclassified

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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A Study on Analysis Methods of Latent Customer Purchase Behavior Focused on Membership Stage Growth

Yang

Yamashita

Goto

2019

2019 IEEE International Conference on Big Data, Cloud Computing, Data Science &Amp; Engineering (BCD)

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Analysis of Purchase History Data Based on a New Latent Class Model for RFM Analysis

Cited by 4 publications

References 0 publications

Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM

Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM

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

A Study on Analysis Methods of Latent Customer Purchase Behavior Focused on Membership Stage Growth

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