A Data Mining Algorithm to Gaining Customer Loyalty to Ports Based on OD Data for Improving Port Competitiveness

  • Lin, Qianfeng (Department of Computer Engineering, Korea Maritime & Ocean University) ;
  • Son, Jooyoung (Division of Marine IT Engineering, Korea Maritime & Ocean University)
  • Received : 2020.08.11
  • Accepted : 2020.10.18
  • Published : 2020.10.31


Every port is competing for attracting loyal customers from other ports to achieve more profits stably. This paper proposes a data-mining scheme to facilitate this process. For resolving the problem, the OD (Origination-Destination) data are gathered from the AIS (Automatic Identification System) data. The OD data are clustered according to the arrival dates and ports. The FP-growth algorithm is applied to mine the frequent patterns of ships arriving at ports. Maintaining a loyal customer list for port updates and accuracy is critical in establishing its usefulness. These lists are critical as they can be used to provide suggestions for new products and services to loyal customers. Finally, based on the frequent patterns of the ships and the mode of arrival times, a formula proposed in this paper to derive shipping companies' loyalty to ports was applied. The case of Kaohsiung port was shown as an example of our algorithm, and the OD data of ships in 2017-2018 were processed. Using the results of our algorithm, other rival ports, such as Shanghai or Busan, may attract customers no longer loyal to Kaohsiung ports in the last two years and attract them as new loyal customers.


  1. Tongzon, J. et al. (2005), "Port privatization, efficiency and competitiveness: Some empirical evidence from container ports (terminals)", Transportation Research Part A: Policy and Practice, Vol. 5, pp. 405-424.
  2. Caliskan, A. et al. (2020), "An assessment of port and shipping line relationships: the value of relationship marketing", Maritime Policy & Management, Vol.47, pp. 240-257.
  3. Foster, T. (1978), "What's important in A Port", Distribution Worldwide, Vol. 78, pp. 33-36.
  4. Slack, B. C. (1985), "Inter-Port Competition, and Port Selection", Maritime policy and management, Vol. 12, pp. 293-303.
  5. Hosmer, B. E. (1998)," The Loyalty Effect: The Hidden Force Behind Growth", Profits, and Lasting Value. Consulting to Management, Vol. 10, pp. 82.
  6. Bernard, K. (1995), Marketing promotion tools for ports.
  7. Laxe, F. G. (2010), "Port Marketing Strategies and the Challenges of Maritime Globalization", Essays on Port Economics, pp. 5-18.
  8. Ng, K. et al. (1998), "A data mining application: customer retention at the Port of Singapore Authority (PSA)", ACM SIGMOD Record, Vol. 27, pp. 522-525.
  9. Chang, C.H. et al. (2016), "Do port security quality and service quality influence customer satisfaction and loyalty?", Maritime Policy & Management, Vol. 43, pp. 720-736.
  10. Rishika, R. et al. (2013), "The effect of customers' social media participation on customer visit frequency and profitability: an empirical investigation", Information systems research, Vol. 24, pp. 108-127.
  11. Huang, M. H. et al. (2016), "A longitudinal comparison of customer satisfaction and customer-company identification in a service context", Journal of Service Management, Vol. 27, pp. 730-750.
  12. Han, J. et al. (2000), "Mining Frequent Patterns Without Candidate Generation", ACM SIGMOD Record, Vol. 29, pp. 1-12.
  13. Huang, L.J. (2007), "FP-growth Apriori algorithm's Application in the Design for Individualized Virtual Shop on the Internet." International Conference on Machine Learning and Cybernetics 2007 Proceedings, pp. 3800-3804.
  14. Wang, S. al. (2019), "Use Product Segmentation to Enhance the Competitiveness of Enterprises in the IoT", IEEE 10th International Conference on Awareness Science and Technology 2019 Proceedings, pp. 1-6.
  15. Manley, E. et al. (2019), "New Forms of Data for Understanding Urban Activity in Developing Countries", Applied Spatial Analysis and Policy, Vol. 12, pp. 45-70.
  16. Alsger, A. et al. (2018), "Public transport trip purpose inference using smart card fare data", Transportation Research Part C: Emerging Technologies, Vol. 87, pp. 123-137.
  17. Jahani A. et al. (2020), "Tourism Impact Assessment Modeling in Vegetation Density of Protected Areas Using Data Mining Techniques", Land Degradation & Development, pp. 1-18.
  18. Campbell E. al. (2020), "Temporal Condition Pattern Mining in Large, Sparse Electronic Health Record Data: A Case Study in Characterizing Pediatric Asthma", Journal of the American Medical Informatics Association, Vol. 27, pp. 558-566.
  19. Zheng S. et al (2020), "A New Unsupervised Data Mining Method Based on the Stacked Auto Encoder for Chemical Process Fault Diagnosis", Computers & Chemical Engineering, Vol. 135, pp. 1-17.
  20. Qiu, X. et al. (2020), "Error Checking of Large Land Quality Databases Through Data Mining Based on Low Frequency Associations", Land Degradation & Development, pp. 1-10.
  21. Kelvin, K. et al. (2020), "Customer Churn's Analysis In Telecommunications Company Using FP-Growth Algorithm", Jurnal Mantik, Vol. 4, pp. 1285-1290.
  22. Taiwan Navigation (2020), Taiwan Navigation shipping company,
  23. Chen J. et al. (2015), "An Early-Warning System for Shipping Market Crisis Using Climate Index", Journal of Coastal Research, Vol. 73, pp. 620-627.
  24. Lee J. Y. et al. (2011), "Factors Affecting Customer Loyalty in the Taiwanese Imported Lumber Market", Forest Products Journal, Vol. 61, pp. 489-493.
  25. Kim M. K. et al. (2004), "The Effects of Customer Satisfaction and Switching Barrier on Customer Loyalty in Korean Mobile Telecommunication Services", Telecommunications Policy, Vol. 28, pp. 145-159.