Sequential Pattern Mining to Support Customer Relationship Management at Beauty Clinics
DOI:
https://doi.org/10.31763/businta.v6i2.602Keywords:
Data Mining, Generalized Sequential Pattern, Sequential Pattern MiningAbstract
The increasing competition for beauty clinics, makes management need to think of methods to survive in this competition. For that, the company needs to improve CRM in its service to customers. Customer Relationship Management is a series of activities managed in an effort to better understand, attract attention, and maintain customer loyalty.
Sequential Pattern Mining is one of the data mining techniques that is useful for finding patterns sequential / sequence of a set of items. The algorithm that is used is the Generalized Sequential Pattern (GSP). GSP performs candidate generation and support counting processes that is, the union of L1−k with itself which generates a candidate sequence that cannot exist as twin candidate, after that deletion candidate who does not meet the minimum support. While carrying out the process through existing data, is also carried out increasing the number of supports from the included candidates in data sequences. The output to be produced by the program are all frequent itemsets that satisfy minimum support in the form of rules.
Sales transaction data will be processed by using the Generalized Sequential Pattern algorithm so that it can produce a rule, namely the purchase order that meets the minimum support. The result of the rule used by management to support enterprise CRM activities such as acquiring new customers, increasing the profits from existing customers, and retaining existing customers.
References
R. Kalakota, M. Robinson, and D. Tapscott, E-business 2.0: Roadmap for Success, vol. 11, pp. 544. Addison-Wesley Boston, 2001. [Online]. Available: https://marmamun.gov.np/.
S. Wilde, Customer Knowledge Management: improving customer relationship through knowledge application. Springer Science & Business Media, 2011, doi: 10.1007/978-3-642-16475-0.
R. J. Baran, R. J. Galka, and D. P. Strunk, Principles of customer relationship management. Cengage Learning, 2008.
L. E. Herman, S. Sulhaini, and N. Farida, “Electronic Customer Relationship Management and Company Performance: Exploring the Product Innovativeness Development,” J. Relatsh. Mark., vol. 20, no. 1, pp. 1–19, Jan. 2021, doi: 10.1080/15332667.2019.1688600.
C.-C. Chen, H.-H. Shuai, and M.-S. Chen, “Distributed and scalable sequential pattern mining through stream processing,” Knowl. Inf. Syst., vol. 53, no. 2, pp. 365–390, Nov. 2017, doi: 10.1007/s10115-017-1037-1.
A. R. Andriyan, D. M. Rochma, M. N. Mudyawati, M. Jannah, S. L. D. Agustini, and A. A. Nugraha, “Determination of Film Recommendations using the Generalized Sequence Pattern (GSP) Association Method,” in Gunung Djati Conference Series, 2021, vol. 3, pp. 7–11, [Online]. Available: https://conferences.uinsgd.ac.id/index.php/gdcs/article/view/89.
T. Astuti and L. Anggraini, “Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm,” Int. J. Informatics Inf. Syst., vol. 2, no. 1, pp. 17–23, 2019, doi: 10.47738/ijiis.v2i1.10.
Y. Feng, H. Chen, and L. He, “Consumer responses to femvertising: A data-mining case of Dove’s ‘Campaign for Real Beauty’ on YouTube,” J. Advert., vol. 48, no. 3, pp. 292–301, 2019, doi: 10.1080/00913367.2019.1602858.
N. Setiyawati, “Application of Association Rule Mining and Mining Sequential Patterns on Crm PT. Armada International Motor,” Creat. Commun. Innov. Technol. J., vol. 11, no. 1, pp. 95–101, 2018, doi: 10.33050/ccit.v11i1.562.
E. A. Z. Noughabi, A. Albadvi, and B. H. Far, “How Can We Explore Patterns of Customer Segments’ Structural Changes? A Sequential Rule Mining Approach,” in 2015 IEEE International Conference on Information Reuse and Integration, 2015, pp. 273–280, doi: 10.1109/IRI.2015.52.
B. C. Kachhadiya and B. Patel, “A Survey on Sequential Pattern Mining Algorithm for Web Log Pattern Data,” in 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), May 2018, pp. 1269–1273, doi: 10.1109/ICOEI.2018.8553691.
D. S. Maylawati, H. Aulawi, and M. A. Ramdhani, “The concept of sequential pattern mining for text,” IOP Conf. Ser. Mater. Sci. Eng., vol. 434, no. 1, p. 012042, Dec. 2018, doi: 10.1088/1757-899X/434/1/012042.
M. Zaki, “Fast mining of sequential patterns in very large databases,” 1997.
K. Shudo, “Message bundling on structured overlays,” in 2017 IEEE Symposium on Computers and Communications (ISCC), Jul. 2017, pp. 424–431, doi: 10.1109/ISCC.2017.8024566.
S. Rezig, Z. Achour, and N. Rezg, “Using Data Mining Methods for Predicting Sequential Maintenance Activities,” Appl. Sci., vol. 8, no. 11, p. 2184, Nov. 2018, doi: 10.3390/app8112184.
W. Gan, J. C.-W. Lin, P. Fournier-Viger, H.-C. Chao, and P. S. Yu, “A Survey of Parallel Sequential Pattern Mining,” ACM Trans. Knowl. Discov. Data, vol. 13, no. 3, pp. 1–34, Jun. 2019, doi: 10.1145/3314107.
R. L. Helmreich and H. C. Foushee, “Why CRM? Empirical and Theoretical Bases of Human Factors Training,” in Crew Resource Management, Elsevier, 2019, pp. 3–52, doi: 10.1016/B978-0-12-812995-1.00001-4.
T. Guyet and R. Quiniou, “NegPSpan: efficient extraction of negative sequential patterns with embedding constraints,” Data Min. Knowl. Discov., vol. 34, no. 2, pp. 563–609, Mar. 2020, doi: 10.1007/s10618-019-00672-w.
C. Pypno and G. Sierpiński, “Automated large capacity multi-story garage—Concept and modeling of client service processes,” Autom. Constr., vol. 81, pp. 422–433, Sep. 2017, doi: 10.1016/j.autcon.2017.03.006.
M. Taufik, F. Renaldi, and F. R. Umbara, “Implementing Online Analytical Processing in Hotel Customer Relationship Management,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1115, no. 1, p. 012040, Mar. 2021, doi: 10.1088/1757-899X/1115/1/012040.
J. Sterne, Customer service on the Internet: building relationships, increasing loyalty, and staying competitive. John Wiley & Sons, Inc., 2000.
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