ANALYSIS OF SEGMENTATION AND CLIENT TARGET MARKET BUSINESS DECISIONS IN CONSTRUCTION SERVICE COMPANY USING K-MEANS AND DECISION TREE ALGORITHMS : CASE STUDY AT CV JOWON SOLUSINDO

Wondho Setia; Mardiani Mardiani

doi:10.47709/cnahpc.v8i2.7942

Authors

Wondho Setia Universitas Multi Data Palembang,South Sumatera, Indonesia
Mardiani Universitas Multi Data Palembang,South Sumatera, Indonesia

DOI:

https://doi.org/10.47709/cnahpc.v8i2.7942

Keywords:

Data Mining, Customer Segmentation, K-Means Clustering, Decision Tree, Marketing Strategy, Construction Industry.

Abstract

High competition in the construction service industry requires companies to adopt efficient marketing strategies to reduce Customer Acquisition Costs (CAC). CV Jowon Solusindo faces challenges regarding marketing inefficiency due to the implementation of a one size fits all strategy and a high number of unconverted leads (Lost Prospects). This study aims to classify customer characteristics and discover decision rules to formulate personalized marketing strategies. This research employs a quantitative approach with Data Mining methods based on the CRISP-DM framework. The dataset consists of 576 historical transaction records that have undergone data cleaning processes. The method used is a hybrid approach, combining the K-Means Clustering algorithm for customer segmentation and the Decision Tree (C4.5) for rule pattern extraction. The results indicate that the K-Means algorithm with k=3 successfully mapped customers into three distinctive segments, Young Emerging Clients (Average age 33 years with the highest project value), Established Senior Clients (Average age 54 years with stable frequency), and Lost Prospects (Average age 42 years with the lowest offer value). The Decision Tree analysis yielded an accuracy of 67% and identified Age as the primary determinant factor with a split point at 43.5 years. Based on these findings, it is recommended to differentiate marketing strategies into digital visual approaches for customers under 43.5 years and personal approaches for those above that age, as well as pricing strategy adjustments to minimize failure in the Lost Prospects segment.

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References

Abdul-Rahman, S., Arifin, N. F. K., Hanafiah, M., & Mutalib, S. (2021). Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier. International Journal of Advanced Computer Science and Applications, 12(9), 434–444. https://doi.org/10.14569/IJACSA.2021.0120950

Al-Ali, A. S., Haris, R. M., Akbari, Y., Saleh, M., Al-Maadeed, S., & Rajesh Kumar, M. (2025). Integrating binary classification and clustering for multi-class dysarthria severity level classification: a two-stage approach. Cluster Computing, 28(2), 1–19. https://doi.org/10.1007/s10586-024-04748-1

Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34(5), 1785–1792. https://doi.org/10.1016/j.jksuci.2019.12.011

Anitsal, I. (2011). Volume 15, Special Issue, Number 2 ACADEMY OF MARKETING STUDIES JOURNAL. 15(2).

Aulia, S., Muhammad, A. N., & Wibowo, A. (2025). Optimization of Customer Segmentation with RFM, K-Means, and FP-Growth for Marketing Strategy. SINTECH Journal, 8(2), 163–177. https://doi.org/10.31598

B, A. (2018). Comparative Evaluation of SOM-Ward Clustering and Decision Tree for Conducting Customer-Portfolio Analysis. Advances in Multidisciplinary and Scientific Research Journal Publication, 8(1), 1161–128. https://doi.org/10.22624/aims/cisdi/v8n1p11

Budilaksono, S., Jupriyanto, J., Suwarno, M. A. S., Suwartane, I. G. A., Azhari, L., Fauzi, A., Mahpud, M., Mariana, N., & Effendi, M. S. (2021). Customer Profilling for Precision Marketing using RFM Method, K-MEANS algorithm and Decision Tree. SinkrOn, 6(1), 191–200. https://doi.org/10.33395/sinkron.v6i1.11225

Chang, C. C., & Chen, S. H. (2015). A comparative analysis on artificial neural network-based two-stage clustering. Cogent Engineering, 2(1). https://doi.org/10.1080/23311916.2014.995785

Chen, A. H. L., Liang, Y. C., Chang, W. J., Siauw, H. Y., & Minanda, V. (2022). RFM Model and K -Means Clustering Analysis of Transit Traveller Profiles: A Case Study. Journal of Advanced Transportation, 2022. https://doi.org/10.1155/2022/1108105

Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management, 19(3), 197–208. https://doi.org/10.1057/dbm.2012.17

Dawane, V., Waghodekar, P., & Pagare, J. (2021). RFM Analysis Using K-Means Clustering to Improve Revenue and Customer Retention. SSRN Electronic Journal, Icsmdi. https://doi.org/10.2139/ssrn.3852887

Deldadehasl, M., Karahroodi, H. H., & Haddadian Nekah, P. (2025). Customer Clustering and Marketing Optimization in Hospitality: A Hybrid Data Mining and Decision-Making Approach from an Emerging Economy. Tourism and Hospitality, 6(2), 1–19. https://doi.org/10.3390/tourhosp6020080

Gustriansyah, R., Suhandi, N., & Antony, F. (2019). Clustering optimization in RFM analysis based on k-means. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 470–477. https://doi.org/10.11591/ijeecs.v18.i1.pp470-477

Harris, A. F., & Austin, V. (n.d.). Comparative Study of Supervised and Unsupervised Machine Learning Approaches in Banking Applications. Ml.

Hartanto, U. I., Buditjahjanto, I. G. P. A., & Yustanti, W. (2025). Hybrid Clustering and Classification of At-Risk Customer Segments in Network Marketing. Journal of Information Engineering and Educational Technology, 9(1), 42–50. https://doi.org/10.26740/jieet.v9n1.p42-50

Hassan, M. M., & M, T. (2018). Customer Profiling and Segmentation in Retail Banks Using Data Mining Techniques. International Journal of Advanced Research in Computer Science, 9(4), 24–29. http://dx.doi.org/10.26483/ijarcs.v9i4.6172

Imani, A., Abbasi, M., Ahang, F., Ghaffari, H., & Mehdi, M. (2022). 1. RIEJ_Volume 11_Issue 1_Pages 62-76. International Journal of Research in Industrial Engineering, 11(1), 62–76. http://www.riejournal.com/article_138379.html

Kasem, M. S. E., Hamada, M., & Taj-Eddin, I. (2024). Customer profiling, segmentation, and sales prediction using AI in direct marketing. Neural Computing and Applications, 36(9), 4995–5005. https://doi.org/10.1007/s00521-023-09339-6

Lee, C. (2021). Predicting land prices and measuring uncertainty by combining supervised and unsupervised learning. International Journal of Strategic Property Management, 25(2), 169–178. https://doi.org/10.3846/ijspm.2021.14293

Lee, J. W., & Harel, O. (2025). A Two-Stage Classification for Dealing with Unseen Clusters in the Testing Data. Journal of Data Science, 23(1), 188–207. https://doi.org/10.6339/24-JDS1140

Lee, Z. J., Lee, C. Y., Chang, L. Y., & Sano, N. (2021). Clustering and classification based on distributed automatic feature engineering for customer segmentation. Symmetry, 13(9), 1–14. https://doi.org/10.3390/sym13091557

Maghfirah, Adji, T. B., & Setiawan, N. A. (2015). Appropriate Data mining Technique and Algorithm for Using in Analysis of Customer Relationship Management ( CRM ) in Bank Industry. Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 7–10.

Mohamad Anas Sobarnas, & Iskandar. (2020). Komparasi Akurasi Metode K-Nearest Neighbor Dan C4.5 Untuk Customer Relationship Management Pada Perusahaan Pembiayaan. INFOTECH?: Jurnal Informatika & Teknologi, 1(1), 1–14. https://doi.org/10.37373/infotech.v1i1.33

Ogwoka, T. M., Cheruiyot, W., & Okeyo, G. (2015). A Model for Predicting Students’ Academic Performance using a Hybrid of K-means and Decision tree Algorithms. International Journal of Computer Applications Technology and Research, 4(9), 693–697. https://doi.org/10.7753/ijcatr0409.1009

Owoade, A. A. (2025). Development of customer segmentation system using supervised and unsupervised machine learning algorithms. Science World Journal, 20(2), 554–565. https://doi.org/10.4314/swj.v20i2.16

Palaniappan, S., Mustapha, A., Foozy, C. F. M., & Atan, R. (2017). Customer profiling using classification approach for bank telemarketing. International Journal on Informatics Visualization, 1(4–2), 214–217. https://doi.org/10.30630/joiv.1.4-2.68

Palma, G. R., Skocze?, M., & Maguire, P. (2024). Combining supervised and unsupervised learning methods to predict financial market movements. 1–22. http://arxiv.org/abs/2409.03762

Park, H. G., Shin, K. S., & Kim, J. C. (2025). Efficient Clustering Method for Graph Images Using Two-Stage Clustering Technique. Electronics (Switzerland), 14(6), 1–27. https://doi.org/10.3390/electronics14061232

Perdana, S. H. (2023). Customer Relationship Management. EAI/Springer Innovations in Communication and Computing, Part F1354, 237–264. https://doi.org/10.1007/978-3-031-39626-7_10

Pertiwi, N. K. P. A., Sutramiani, N. P., & Wibawa, K. S. (2025). Design and Development of Customer Relationship Management in a Construction Company. INOVTEK Polbeng - Seri Informatika, 10(2), 659–669. https://doi.org/10.35314/qhkc3j28

Rahmadhan, R., & Wasesa, M. (2022). Segmentation using Customers Lifetime Value: Hybrid K-means Clustering and Analytic Hierarchy Process. Journal of Information Systems Engineering and Business Intelligence, 8(2), 130–141. https://doi.org/10.20473/jisebi.8.2.130-141

Sáez-Ortuño, L., Huertas-Garcia, R., Forgas-Coll, S., & Puertas-Prats, E. (2023). How can entrepreneurs improve digital market segmentation? A comparative analysis of supervised and unsupervised learning algorithms. International Entrepreneurship and Management Journal, 19(4), 1893–1920. https://doi.org/10.1007/s11365-023-00882-1

Saputra, A. D., & Yustanti, W. (2025). A Hybrid Clustering – Classification Framework for SMEs Success Level Prediction. 9(2), 89–100.

Sarkar, M., Puja, A. R., & Chowdhury, F. R. (2023). Journal of Business and Management Studies Determinants of Employee Retention in Pharmaceutical Companies: Case of Saudi Arabia. Determinants of Employee Retention in Pharmaceutical Company: Case of Saudi Arabia, 2709–0876, 8–22. https://doi.org/10.32996/jbms

Shirole, R., Salokhe, L., & Jadhav, S. (2021). Customer Segmentation using RFM Model and K-Means Clustering. International Journal of Scientific Research in Science and Technology, 591–597. https://doi.org/10.32628/ijsrst2183118

Ufeli, C. P., Sattar, M. U., Hasan, R., & Mahmood, S. (2025). Enhancing Customer Segmentation Through Factor Analysis of Mixed Data (FAMD)-Based Approach Using K-Means and Hierarchical Clustering Algorithms. Information (Switzerland), 16(6), 1–25. https://doi.org/10.3390/info16060441

Widiputra, H., Kisworo, M., Novita, A., & Pardede, T. (2015). Two-stage graph-clustering algorithm and localised classification model to identify apt business locale. International Journal of Business Information Systems, 20(2), 195–218. https://doi.org/10.1504/IJBIS.2015.071537

Zhang, Y., & Fan, Y. (2025). POSTER PRESENTATION Characterizing heterogeneous atrophy patterns of Alzheimer ’ s disease dementia with mixture-of-experts ( MOE ) for joint classification and clustering. 21, 2–4. https://doi.org/10.1002/alz70856