SEGMENTASI PELANGGAN BISNIS DENGAN MULTI KRITERIA MENGGUNAKAN K-MEANS

Yanuar Wicaksono

Abstract


Customer knowledge is an important asset, in gathering, and managing from sharing customer knowledge into valuable capital for the company. This causes the company to continue to innovate in producing products and serving according to customer needs. To find out the needs of each customer, the company needs to make customer segmentation. Customer segmentation is defined as the division into different groups with similar characteristics to develop marketing strategies that are tailored to customer characteristics. The easiest, simplest, well-known and commonly used model of customer characteristics is the model of the recency, frequency, monetary (RFM) criteria. The RFM model still has weaknesses in low customer segmentation capacity and does not provide information on the continuity of customer transactions in understanding customer loyalty. The research method used is the Knowledge Discovery in Database (KDD) method. The data is transformed into another format that suits the needs of analysis and then the customer is segmented using clustering data mining techniques with the K-Means algorithm. From the experiments, the RFM model guesses loyal customers when reviews, frequency and monetary are high. In reality, the recency only provides information on the customer making the last transaction and the high number of transaction frequencies can be done without the customer's stability in making transactions each period. Implementing multi-criteria in customer segmentation can be better than just RFM criteria. So it will not be wrong to treat customers according to the groups that have been formed.

Keywords


Customer Segmentation; RFM; Loyalty; Average Demand; K-Means

Full Text:

PDF

References


M. García-Murillo and H. Annabi, “Customer knowledge management,” J. Oper. Res. Soc., vol. 53, no. 8, pp. 875–884, Aug. 2002.

T. R. Coltman, T. M. Devinney, and D. F. Midgley, “Strategy Content and Process in the Context of E-Business Performance,” in Advances in Strategic Management, vol. 22, Emerald Publishing, 2005, pp. 349–386.

R. S. Wu and P. H. Chou, “Customer segmentation of multiple category data in e-commerce using a soft-clustering approach,” Electron. Commer. Res. Appl., vol. 10, no. 3, pp. 331–341, 2011.

H. Güçdemir and H. Selim, “Integrating multi-criteria decision making and clustering for business customer segmentation,” Ind. Manag. Data Syst., vol. 115, no. 6, pp. 1022–1040, Jul. 2015.

Z. Soltani and N. J. Navimipour, “Customer relationship management mechanisms: A systematic review of the state of the art literature and recommendations for future research,” Comput. Human Behav., vol. 61, pp. 667–688, Aug. 2016.

S. W. Changchien and T.-C. Lu, “Mining association rules procedure to support on-line recommendation by customers and products fragmentation,” Expert Syst. Appl., vol. 20, no. 4, pp. 325–335, May 2001.

A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 651–666, Jun. 2010.

A. J. Cuadros and V. E. Domínguez, “Customer segmentation model based on value generation for marketing strategies formulation,” Estud. Gerenciales, vol. 30, no.130, pp. 25–30, 2014.

P. Q. Brito, C. Soares, S. Almeida, A. Monte, and M. Byvoet, “Customer segmentation in a large database of an online customized fashion business,” Robot. Comput. Integr.Manuf., vol. 36, pp. 93–100, 2015.




DOI: http://dx.doi.org/10.21927/ijubi.v1i2.872

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Indonesian Journal of Business Intelligence (IJUBI)

Lisensi Creative Commons
IJUBI by https://ejournal.almaata.ac.id/index.php/IJUBI is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

View My Stats