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

Authors

  • Dedy Panji Agustino ITB STIKOM Bali
  • I Gede Bintang Arya Budaya ITB STIKOM Bali
  • I Gede Harsemadi ITB STIKOM Bali
  • I Komang Dharmendra ITB STIKOM Bali
  • I Made Suandana Astika Pande ITB STIKOM Bali

DOI:

https://doi.org/10.32736/sisfokom.v12i2.1682

Keywords:

customer profiling, customer relationship management, clustering, RFM

Abstract

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.

References

S. Hwang and Y. Lee, “Identifying customer priority for new products in target marketing: Using RFM model and TextRank,” Marketing, vol. 17, no. 2, pp. 125–136, 2021.

R. W. Palmatier, J. A. Petersen, and F. Germann, Marketing Analytics: Based on First Principles. Bloomsbury Publishing, 2022.

V. Kumar and W. Reinartz, Customer relationship management. Springer, 2018.

K. Khalili-Damghani, F. Abdi, and S. Abolmakarem, “Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries,” Applied Soft Computing Journal, vol. 73, pp. 816–828, Dec. 2018, doi: 10.1016/j.asoc.2018.09.001.

P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1785–1792, May 2022, doi: 10.1016/j.jksuci.2019.12.011.

B. D. Komara and H. C. B. Setiawan, “Inkubator Bisnis Sebagai Pendorong Tumbuhnya Wirausaha Muda: Studi Tentang Suksesi Kewirausahaan Mahasiswa Universitas Muhammadiyah Gresik,” Jurnal Riset Entrepreneurship, vol. 3, no. 1, pp. 33–39, 2020.

N. Lutfiani, U. Rahardja, and I. S. P. Manik, “Peran Inkubator Bisnis dalam Membangun Startup pada Perguruan Tinggi,” Jurnal Penelitian Ekonomi dan Bisnis, vol. 5, no. 1, pp. 77–89, 2020.

W. Gunadi, “Pengembangan Kewirausahaan Usaha Mikro, Kecil Dan Menengah Melalui Inkubator Bisnis,” Jurnal Ilmiah M-Progress, vol. 8, no. 1, 2018.

M. M. D. Alam, R. Al Karim, and W. Habiba, “The relationship between CRM and customer loyalty: The moderating role of customer trust,” International Journal of Bank Marketing, vol. 39, no. 7, pp. 1248–1272, 2021.

R. U. Khan, Y. Salamzadeh, Q. Iqbal, and S. Yang, “The impact of customer relationship management and company reputation on customer loyalty: The mediating role of customer satisfaction,” Journal of Relationship Marketing, vol. 21, no. 1, pp. 1–26, 2022.

D. P. Agustino, I. G. Harsemadi, and I. G. B. A. Budaya, “Edutech Digital Start-Up Customer Profiling Based on RFM Data Model Using K-Means Clustering,” Journal of Information Systems and Informatics, vol. 4, no. 3, pp. 724–736, 2022.

I. G. Harsemadi, D. P. Agustino, and I. G. B. A. Budaya, “Klasterisasi Pelanggan Tenant Inkubator Bisnis STIKOM Bali Untuk Strategi Manajemen Relasi Dengan Menggunakan Fuzzy C-Means,” JTIM: Jurnal Teknologi Informasi dan Multimedia, vol. 4, no. 4, pp. 232–243, 2023.

M. Hahsler, M. Piekenbrock, and D. Doran, “dbscan: Fast density-based clustering with R,” J Stat Softw, vol. 91, pp. 1–30, 2019.

W. Lai, M. Zhou, F. Hu, K. Bian, and Q. Song, “A new DBSCAN parameters determination method based on improved MVO,” Ieee Access, vol. 7, pp. 104085–104095, 2019.

P. Kalpana, A. M. Nivetha, R. Madhumitha, P. Tamijeselvy, and S. S. Devi, “Improvisation of spectral clustering through affinity propagation,” in AIP Conference Proceedings, AIP Publishing LLC, 2021, p. 030004.

H. Keller, H. Möllering, T. Schneider, and H. Yalame, “Balancing quality and efficiency in private clustering with affinity propagation,” Cryptology ePrint Archive, 2021.

M. Tavakoli, M. Molavi, V. Masoumi, M. Mobini, S. Etemad, and R. Rahmani, “Customer segmentation and strategy development based on user behavior analysis, RFM model and data mining techniques: a case study,” in 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), IEEE, 2018, pp. 119–126.

Q. Zhang, H. Yamashita, K. Mikawa, and M. Goto, “Analysis of purchase history data based on a new latent class model for RFM analysis,” Industrial Engineering & Management Systems, vol. 19, no. 2, pp. 476–483, 2020.

D.-T. Dinh, T. Fujinami, and V.-N. Huynh, “Estimating the optimal number of clusters in categorical data clustering by silhouette coefficient,” in International Symposium on Knowledge and Systems Sciences, Springer, 2019, pp. 1–17.

K. R. Shahapure and C. Nicholas, “Cluster quality analysis using silhouette score,” in 2020 IEEE 7th international conference on data science and advanced analytics (DSAA), IEEE, 2020, pp. 747–748.

Y. A. Wijaya, D. A. Kurniady, E. Setyanto, W. S. Tarihoran, D. Rusmana, and R. Rahim, “Davies bouldin index algorithm for optimizing clustering case studies mapping school facilities,” TEM J, vol. 10, no. 3, pp. 1099–1103, 2021.

A. K. Singh, S. Mittal, P. Malhotra, and Y. V. Srivastava, “Clustering Evaluation by Davies-Bouldin Index (DBI) in Cereal data using K-Means,” in 2020 Fourth international conference on computing methodologies and communication (ICCMC), IEEE, 2020, pp. 306–310.

J. S. Thomas, C. Chen, and D. Iacobucci, “Email Marketing as a Tool for Strategic Persuasion,” Journal of Interactive Marketing, vol. 57, no. 3, pp. 377–392, May 2022, doi: 10.1177/10949968221095552.

C. E. Khedkar and A. E. Khedkar, “Email Marketing: A Cost-Effective Marketing Method,” Vidyabharati International Interdisciplinary Research Journal 13 (1), 2021.

İ. SABUNCU, E. TÜRKAN, and H. POLAT, “Customer segmentation and profiling with RFM analysis,” Turkish Journal of Marketing, vol. 5, no. 1, pp. 22–36, 2020.

T.-H. Chou and S.-C. Chang, “The RFM Model Analysis for VIP Customer: A case study of golf clothing brand,” International Journal of Knowledge Management (IJKM), vol. 18, no. 1, pp. 1–18, 2022.

A. T. Widiyanto and A. Witanti, “Segmentasi Pelanggan Berdasarkan Analisis RFM Menggunakan Algoritma K-Means Sebagai Dasar Strategi Pemasaran (Studi Kasus PT Coversuper Indonesia Global),” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 1, no. 1, pp. 204–215, 2021.

H. Rangriz and Z. Bayrami Shahrivar, “The impact of E-CRM on customer loyalty using data mining techniques,” BI Management Studies, vol. 7, no. 27, pp. 175–205, 2019.

I. G. Juanamasta et al., “The role of customer service through customer relationship management (Crm) to increase customer loyalty and good image,” International Journal of Scientific and Technology Research, vol. 8, no. 10, pp. 2004–2007, 2019.

C. M. Durugbo, “After-sales services and aftermarket support: a systematic review, theory and future research directions,” Int J Prod Res, vol. 58, no. 6, pp. 1857–1892, 2020.

Downloads

Published

2023-07-01

Issue

Section

Articles