Improving Residential Marketing Campaigns via Customer Data Clustering

Year 2025, Volume: 30 Issue: 1, 129 - 144, 29.04.2025

Mohammed Muqbel , Selçuk Özcan , Çağrı Sel

https://doi.org/10.53433/yyufbed.1463691

Abstract

As the construction industry struggles to develop effective marketing plans for residential projects, using rich datasets to understand customer demand helps builders of residential complexes with complex use cases. Decision-makers often struggle to understand big data. Solving this problem begins with relevant data being mined and collected. Multi-criteria decision-making (MCDM) models are used to rank the data or alternative options according to their importance to decision-makers. The weights of the criteria, obtained according to their importance, are essential to reveal the relative value of different criteria. To understand and analyze big data, cluster analysis within the data mining discipline is used to segment and score data. This analysis is an effective tool for determining marketing strategies and understanding customer behavior. This study was conducted to determine the marketing strategy appropriate for customer segmentation. For this, the Rank Order Centroid (ROC) criterion weight method gives weights to the criteria according to their relative importance. The K-means cluster analysis algorithm uses the values obtained from the ROC method. By combining ROC and K-means methods, this study will contribute to extracting information from large data sets and simplifying decision-making processes in the residential sector. As a result of the study, customers were divided into groups, and it was concluded that the groups with the highest scores should be prioritized in marketing strategies.

Keywords

Clustering , Data mining , Marketing , Multicriteria decision-making , Resident

Müşteri Verilerinin Kümelenmesi Yoluyla Konut Pazarlama Kampanyalarının İyileştirilmesi

Abstract

İnşaat sektörü, konut projeleri için etkili pazarlama planları geliştirmekle uğraşırken, müşteri taleplerini anlamak için zengin veri kümelerini kullanmak, karmaşık kullanım alanlarına sahip konut komplekslerinin inşa edicilerine yardımcı olmaktadır. Karar vericiler genellikle büyük verileri anlama konusunda zorluklarla karşılaşırlar. Bu sorunu çözmek, ilgili verilerin madenciliği ve toplanmasıyla başlar. Çok kriterli karar verme (MCDM) modelleri, verileri veya alternatif seçenekleri karar vericiler için önemlerine göre sıralamak için kullanılır. Önem derecelerine göre elde edilen kriterlerin ağırlıkları, farklı kriterlerin göreceli değerini ortaya çıkarmak için esastır. Büyük veriyi anlamak ve analiz etmek için, veri madenciliği disiplini içinde kümeleme analizi kullanılır; bu analiz, pazarlama stratejilerini belirleme ve müşteri davranışını anlama konusunda etkili bir araçtır. Bu çalışma, müşteri segmentasyonu için uygun pazarlama stratejisini belirlemek amacıyla yapılmıştır. Bunun için Rank Order Centroid (ROC) kriter ağırlığı yöntemi kriterlere göreceli önemlerine göre ağırlıklar vermektedir. K-ortalama kümeleme analizi algoritması ROC yönteminden elde edilen değerleri kullanır. Bu çalışma, ROC ve K-ortalama metotlarını bir arada kullanarak, büyük veri setlerinden bilgi çıkarılmasına ve konut sektöründe karar alma süreçlerinin basitleştirilmesine katkı sağlayacaktır. Çalışma sonucunda müşteriler gruplara ayrılmış ve en yüksek puanı alan grupların pazarlama stratejilerinde önceliklendirilmesi gerektiği sonucuna varılmıştır.

Keywords

Çok kriterli karar verme , Konut , Kümeleme , Pazarlama , Veri madenciliği

References

• Ali, H. H., & Kadhum, L. E. (2017). K-means clustering algorithm applications in data mining and pattern recognition. International Journal of Science and Research (IJSR), 6(8), 1577–1584. https://doi.org/10.21275/ART20176024
• Bai, C., Dhavale, D., & Sarkis, J. (2014). Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis. Expert Systems with Applications, 41(9), 4186–4196. https://doi.org/10.1016/j.eswa.2013.12.037
• Bajpai, V., Pandey, S., & Shriwas, S. (2012). Social media marketing: Strategies & its impact. International Journal of Social Science & Interdisciplinary Research, 1(7), 214–223.
• Bilici, Z., & Özdemir, D. (2022). Data mining studies in education: Literature review for the years 2014-2020. Bayburt Eğitim Fakültesi Dergisi, 17(33), 342–376. https://doi.org/10.35675/befdergi.849973
• Chiang, W.-Y. (2011). To mine association rules of customer values via a data mining procedure with improved model: An empirical case study. Expert Systems with Applications, 38(3), 1716–1722. https://doi.org/10.1016/j.eswa.2010.07.097
• Dabhi, D. P., & Patel, M. R. (2016). Extensive survey on hierarchical clustering methods in data mining. International Research Journal of Engineering and Technology (IRJET), 3(11), 659–665.
• Guo, F., & Qin, H. (2017). Data mining techniques for customer relationship management. Journal of Physics: Conference Series, 910(1), 012021. https://doi.org/10.1088/1742-6596/910/1/012021
• Gülagiz, F. K., & Sahin, S. (2017). Comparison of hierarchical and non-hierarchical clustering algorithms. International Journal of Computer Engineering and Information Technology, 9(1), 6.
• Gürbüz, T., Albayrak, Y. E., & Alaybeyoğlu, E. (2014). Criteria Weighting and 4P’s Planning in Marketing Using a Fuzzy Metric Distance and AHP Hybrid Method: International Journal of Computational Intelligence Systems, 7(Supplement 1), 94.
• Heryati, A., & Herdiansyah, M. I. (2020). The Application of Data Mining by using K-Means Clustering Method in Determining New Students’ Admission Promotion Strategy. International Journal of Engineering and Advanced Technology, 9(3), 824–833. https://doi.org/10.35940/ijeat.C5414.029320
• Hung, Y.-H., Huang, T.-L., Hsieh, J.-C., Tsuei, H.-J., Cheng, C.-C., & Tzeng, G.-H. (2012). Online reputation management for improving marketing by using a hybrid MCDM model. Knowledge-Based Systems, 35, 87–93. https://doi.org/10.1016/j.knosys.2012.03.004
• Hutagalung, J., Syahril, M., & Sobirin, S. (2022). Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation. Journal of Computer Networks, Architecture and High Performance Computing, 4(2), 137–147. https://doi.org/10.47709/cnahpc.v4i2.1458
• Kuo, R. J., Amornnikun, P., & Nguyen, T. P. Q. (2020). Metaheuristic-based possibilistic multivariate fuzzy weighted c-means algorithms for market segmentation. Applied Soft Computing, 96, 106639. https://doi.org/10.1016/j.asoc.2020.106639
• Maciejewski, G., Mokrysz, S., & Wróblewski, \Lukasz. (2019). Segmentation of coffee consumers using sustainable values: Cluster analysis on the Polish coffee market. Sustainability, 11(3), 613. https://doi.org/10.3390/su11030613
• Madni, H. A., Anwar, Z., & Shah, M. A. (2017). Data mining techniques and applications—A decade review. 2017 23rd International Conference on Automation and Computing (ICAC), 1–7.
• Maulina, N. R., Surjandari, I., & Rus, A. M. M. (2019). Data mining approach for customer segmentation in B2B settings using centroid-based clustering. 2019 16th International Conference on Service Systems and Service Management (ICSSSM), 1–6.
• Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62. https://doi.org/10.1016/j.dss.2014.03.001
• Oyelade, J., Isewon, I., Oladipupo, O., Emebo, O., Omogbadegun, Z., Aromolaran, O., Uwoghiren, E., Olaniyan, D., & Olawole, O. (2019). Data clustering: Algorithms and its applications. 2019 19th International Conference on Computational Science and Its Applications (ICCSA), 71–81. https://doi.org/10.1109/ICCSA.2019.000-1
• Pande, S. R., Sambare, S. S., & Thakre, V. M. (2012). Data clustering using data mining techniques. International Journal of Advanced Research in Computer and Communication Engineering, 1(8), 494–499.
• Pandiangan, I. M., Mesran, M., Borman, R. I., Windarto, A. P., & Setiawansyah, S. (2023). Implementation of Operational Competitiveness Rating Analysis (OCRA) and Rank Order Centroid (ROC) to Determination of Minimarket Location. Bulletin of Informatics and Data Science, 2(1), 1–8. http://dx.doi.org/10.61944/bids.v2i1.62
• Papakyriakou, D., & Barbounakis, I. S. (2022). Data mining methods: A review. International Journal of Computer Application, 183(48), 5–19. https://doi.org/10.5120/ijca2022921884
• Phillips-Wren, G., Doran, R., & Merrill, K. (2016). Creating a value proposition with a social media strategy for talent acquisition. Journal of Decision Systems, 25(sup1), 450–462. https://doi.org/10.1080/12460125.2016.1187398
• Rohmawati, T., & Winata, H. (2021). Information technology for modern marketing. International Journal of Research and Applied Technology (INJURATECH), 1(1), 90–96.
• Roszkowska, E. (2013). Rank ordering criteria weighting methods–a comparative overview. Optimum. Studia Ekonomiczne, 5 (65), 14–33.
• Sharma, M. (2014). Data mining: A literature survey. International Journal of Emerging Research in Management & Technology, 3(2).
• Singh, K., Malik, D., & Sharma, N. (2011). Evolving limitations in K-means algorithm in data mining and their removal. International Journal of Computational Engineering & Management, 12(1), 105–109.
• Singh, M., & Pant, M. (2021). A review of selected weighing methods in MCDM with a case study. International Journal of System Assurance Engineering and Management, 12, 126–144. https://doi.org/10.1007/s13198-020-01033-3
• Tacoli, C., McGranahan, G., & Satterthwaite, D. (2015). Urbanisation, rural-urban migration and urban poverty. JSTOR.
• Tamba, S. P., Purba, A., Kusuma, Y. E., Vidyastuti, M. A. S., & Dharma, S. (2021). Implementation of the rank order centroid (roc) method to determine the favorite betta fish. INFOKUM, 9(2, June), 381–386.
• Tleis, M., Callieris, R., & Roma, R. (2017). Segmenting the organic food market in Lebanon: An application of k-means cluster analysis. British Food Journal, 119(7), 1423–1441. https://doi.org/10.1108/BFJ-08-2016-0354
• Wijaya, B. K., Sudipa, I. G. I., Waas, D. V., & Santika, P. P. (2022). Selection of Online Sales Platforms for MSMEs using the OCRA Method with ROC Weighting. Journal of Intelligent Decision Support System (IDSS), 5(4), 146–152.
• Wirtz, B. W., & Daiser, P. (2018). Business model development: A customer-oriented perspective. Journal of Business Models, 6(3), 24–44.
• Wu, W.-T., Li, Y.-J., Feng, A.-Z., Li, L., Huang, T., Xu, A.-D., & Lyu, J. (2021). Data mining in clinical big data: The frequently used databases, steps, and methodological models. Military Medical Research, 8(1), 44. https://doi.org/10.1186/s40779-021-00338-z
• Zou, H. (2020). Clustering Algorithm and Its Application in Data Mining. Wireless Personal Communications, 110(1), 21–30. https://doi.org/10.1007/s11277-019-06709-z