DOI:
https://doi.org/10.14483/23448350.23083Published:
09/27/2025Issue:
Vol. 52 No. 1 (2025): January-April 2025Section:
Research ArticlesAccount-Based Marketing in the Railway Sector: An Optimization Approach with K-means 9++w and Linear Programming
Marketing basado en cuentas en el sector ferroviario: un enfoque de optimización con K-means9++w y programación lineal
Keywords:
K-means, linear programming,, railway industry, Account Based Marketing, ABM, marketing management, optimization (en).Keywords:
K-means, programación lineal, industria ferroviaria, marketing basado en cuentas, gestión de marketing, optimización (es).Downloads
Abstract (en)
This article proposes an optimized approach for account-based marketing (ABM) in the railway sector, utilizing a variant of the K-means algorithm, dubbed K-means 9++w, as well as linear programming. The methodology integrates the CRISP-DM model and the K-means++ initialization strategy to overcome computational inefficiency and suboptimal initialization of traditional algorithms. This study analyzed data from various sources, such as railway track length, passenger and freight transport, and per capita GDP. The results demonstrate the effectiveness of Chebyshov and weighted Euclidean distance metrics in data processing and variable evaluation. Crucially, the proposed weighted Euclidean distance showed a superior performance, achieving the highest silhouette coefficient (0.3807) and the lowest Davies-Bouldin index (0.8174) in comparison with traditional distance variants. ABM optimization through automatic rules yielded time savings, while the normalization of scattered data improved coherence and computational performance. The implications of this study indicate the global potential of the railway construction campaign and a significant potential for the railway sector in Colombia. Our analysis of the findings, carried out with a global dataset, validates the model's applicability to the expansion of a B2B company beyond its local market. The limitations include the availability of public data and time and budget constraints.
Abstract (es)
Este artículo propone un enfoque optimizado para el marketing basado en cuentas (ABM) en el sector ferroviario, utilizando una variante del algoritmo K-means, denominada K-means 9++w, y la programación lineal. La metodología integra el modelo CRISP-DM y la estrategia de inicialización K-means++ para superar la ineficiencia computacional y la inicialización subóptima de los algoritmos tradicionales. Este estudio analizó datos de diversas fuentes, como la longitud de las vías férreas, el transporte de pasajeros y carga, y el PIB per cápita. Los resultados demuestran la efectividad de las distancias Chebyshov y Euclidiana ponderada en el procesamiento y la evaluación de variables. De manera crucial, la distancia Euclidiana ponderada propuesta mostró un rendimiento superior, logrando el coeficiente de silueta más alto (0.3807) y el índice Davies-Bouldin más bajo (0.8174) en comparación con las variantes de distancia tradicionales. La optimización de ABM a través de reglas automáticas produjo un ahorro de tiempo, mientras que la normalización de datos dispersos mejoró la coherencia y el rendimiento computacional. Las implicaciones del estudio indican el potencial de la campaña de construcción de vías férreas a nivel global y un potencial significativo para el sector ferroviario en Colombia. El análisis de los hallazgos, realizado con un conjunto de datos global, valida la aplicabilidad del modelo en la expansión de una empresa B2B más allá de su mercado local. Las limitaciones incluyen la disponibilidad de datos públicos y las restricciones de tiempo y presupuesto.
References
Abdulnassar, A. A., & Nair, L. R. (2023). Performance analysis of Kmeans with modified initial centroid selection algorithms and developed Kmeans9+ model. Measurement: Sensors, 25, 100666. https://doi.org/10.1016/J.MEASEN.2023.100666
Account-based marketing budgets demystified: Findings from SiriusDecisions’ latest study. (2019). https://www.forrester.com/blogs/abm-budgets-study-findings/
Ayele, W. Y. (2020). Adapting CRISP-DM for idea mining a data mining process for generating ideas using a textual dataset. International Journal of Advanced Computer Science and Applications, 11(6), 3. https://doi.org/10.14569/IJACSA.2020.0110603
Bereg, S., Haghpanah, M., Malouf, B., & Sudborough, I. H. (2024). Improved bounds for permutation arrays under Chebyshev distance. Designs, Codes, and Cryptography, 92(4), 1023-1039. https://doi.org/10.1007/S10623-023-01326-1/TABLES/5
Burgess, B. (2025). Account-based marketing: The definitive handbook for B2B marketers. Kogan Page.
Buzzell, G. A., Niu, Y., Aviyente, S., & Bernat, E. (2022). A practical introduction to EEG time-frequency principal components analysis (TF-PCA). Developmental Cognitive Neuroscience, 55, 101114. https://doi.org/10.1016/J.DCN.2022.101114
Central Intelligence Agency (CIA) (2024). The world factbook. https://www.cia.gov/the-world-factbook/
Chikumbo, O., & Granville, V. (2019). Optimal clustering and cluster identity in understanding high-dimensional data spaces with tightly distributed points. Machine Learning and Knowledge Extraction, 1(2), 715-744. https://doi.org/10.3390/MAKE1020042
Colombian National Planning Department (2020). APP-Sector férreo (2020). https://colaboracion.dnp.gov.co/CDT/Participacin%20privada%20en%20proyectos%20de%20infraestrucu/Gu%C3%ADa%20APP%20Sector%20F%C3%A9rreo.pdf
Cuevas-Díaz Durán, R., Wei, H., & Wu, J. (2024). Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets. BMC Genomics, 25(1), 1-18. https://doi.org/10.1186/S12864-024-10364-5
Fabri, G., Ometto, A., Li, H., & d’Ovidio, G. (2024). Redesign of a non-electrified urban railway line with hydrogenfuelled trains. Lecture Notes in Civil Engineering, 526, 640-648. https://doi.org/10.1007/978981-97-4355-1_62
Figueroa, A. E. N., Castellanos, G. C., & Sanabria, J. S. G. (2023). Modelo de machine learning para la clasificación de municipios por cultivos ilícitos en Colombia de 2010 a 2020. Inge CuC, 19(1), 47-60. https://doi.org/10.17981/INGECUC.19.1.2023.05
Findeter (2020). Metro de Medellín tendrá dos nuevos trenes y llega el primero de los 20 que habían sido comprados| Findeter.
Forrester Research (2019). Account-based marketing budgets demystified: Findings from SiriusDecisions’ latest study.https://www.forrester.com/blogs/abm-budgets-study-findings/
Gene Day, D., & Wei Shi, S. (2020). Automated and scalable: Account-based B2B marketing for startup companies. Journal of Business Theory and Practice, 8(2), 16-23. https://doi.org/10.22158/jbtp.v8n2p16
Gobierno de Colombia. (2020, November). Plan maestro ferroviario. https://colaboracion.dnp.gov.co/CDT/Prensa/Plan-Maestro-Ferroviario.pdf
Hernández Sampieri, R., Feránadez Collado, C., & Baptista Lucio, M. D. P. (2014). Metodología de la investigación. McGraw Hill España. https://dialnet.unirioja.es/servlet/libro?codigo=775008&info=resumen&idioma=SPA
Karam, A., Jensen, A. J. K., & Hussein, M. (2023). Analysis of the barriers to multimodal freight transport and their mitigation strategies. European Transport Research Review, 15(1), 1-16. https://doi.org/10.1186/S12544-023-00614-0/METRICS
Lenssen, L., & Schubert, E. (2023). Medoid silhouette clustering with automatic cluster number selection. Information Systems, 120, 102290. https://doi.org/10.1016/j.is.2023.102290
Li, X., Shi, H., Yang, K., Dou, Q., & Jia, N. (2025). Ground fault insulation monitoring method for smart substation based on Mahalanobis distance and automatic code generation. Energy Informatics, 8(1), 22. https://doi.org/10.1186/S42162-025-00470-3
Ling, L. S., & Weiling, C. T. (2025). Enhancing segmentation: A comparative study of clustering methods. IEEE Access, 13, 47418-47439. https://doi.org/10.1109/ACCESS.2025.3550339
Makarychev, K., Reddy, A., & Shan, L. (2020). Improved Guarantees for k-means++ and k-means++ Parallel. In H. Larochelle, M. Ranzato, R. Hadzel, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (pp. 1-11). NEurIPS. https://proceedings.neurips.cc/paper_files/paper/2020/hash/ba304f3809ed31d0ad97b5a2b5df2a39-Abstract.html
Ministry of Transportation (2019). En Colombia, cada año cerca de 670 mil personas usan el tren como medio de transporte. https://mintransporte.gov.co/publicaciones/7286/en-colombia-cada-ano-cerca-de-670-mil-personasusan-el-tren-como-medio-de-transporte/
Ministry of the Environment (2024). MinAmbiente presenta borrador de decreto para impulsar transporte ferroviario en ciudades del país. https://www.minambiente.gov.co/minambiente-presenta-borrador-de-decreto-paraimpulsar-transporte-ferroviario-en-ciudades-del-pais/
Ren, Y., Yang, M., Chen, E., Cheng, L., & Yuan, Y. (2024). Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach. Transportation, 51(4), 1493-1523. https://doi.org/10.1007/S11116-023-10375-3/FIGURES/7
RENFE (2023). Renfe licita la compra de 149 vagones para mercancías. https://www.renfe.com/es/es/grupo-renfe/comunicacion/renfe-al-dia/sala-de-prensa/renfe-licita-la-compra-de-vagones-para-mercancias
Rubiños, M., Díaz-Longueira, A., Timiraos, M., Michelena, Á., García-Ordás, M. T., & Alaiz-Moretón, H. (2024). A Comparative Analysis of Algorithms and Metrics to Perform Clustering. Lecture Notes in Networks and Systems,1173, 63-72. https://doi.org/10.1007/978-3-031-73910-1_7
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021). Setting B2B digital marketing in artificial intelligencebased CRMs: A review and directions for future research. Industrial Marketing Management, 98, 161-178. https://doi.org/10.1016/J.INDMARMAN.2021.08.006
United Nations (UN) (2024). Member States | United Nations. https://www.un.org/en/about-us/member-states
United Nations Economic Commission for Europe (UNECE) (2024). Data portal. https://w3.unece.org/PXWeb/en
Universidad Oberta de Catalunya (2024). Espacio de recursos de ciencia de datos. https://datascience.recursos.uoc.edu/es/optimizacion-con-pulp/
Vaquer, A. J. (2023). Aplicación de la programación lineal en el planeamiento de la producción de una acería. South Florida Journal of Development, 4(4), 1623-1638. https://doi.org/10.46932/sfjdv4n4-015
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/J.JJIMEI.2020.100002
World Bank (2024a). Superficie (kilómetros cuadrados) | Data. https://datos.bancomundial.org/indicador/AG.SRF.TOTL.K2
World Bank (2024b). PIB per cápita (US$ a precios actuales) | Data. https://datos.bancomundial.org/indicador/NY.GDP.PCAP.CD
World Bank (2024c). Individuos que utilizan Internet (% de la población) | Data. https://datos.bancomundial.org/indicador/IT.NET.USER.ZS
World Population Review (2024). Rail usage by country 2024. https://worldpopulationreview.com/country-rankings/rail-usage-by-country
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