Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)

https://doi.org/10.22146/ijccs.58107

Dayan Ramly Ramadhan(1*), Nur Rokhman(2)

(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics; FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer segment to become product promotion for potential consumers. The method was tested by comparing product promotions based on rules and product promotions without based on rules. Based on the test results, the average percentage of transaction from product promotion based on rules is 2,622%, higher than the promotion with the latest products with an average rate of transactions only 0,315%. The hypothesis in each segment obtained from the sample can support the statement that product promotion in all segments based on rules can be more effective in increasing sales compared to promotions that use the latest products without using rules recommendations.


Keywords


Data Mining; Sequential Rules; Rule-Growth; Customer Segmentation; RFM Model

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References

[1] J. Zhang and J. Li, “Retail Commodity Sale Forecast Model Based on Data Mining,” Int. Conf. Intell. Netw. Collab. Syst., pp. 307–310, 2016.

[2] Y. Liu and Y. Guan, “Application in Market Basket Analysis Based on FP-growth Algorithm,” WRI World Congr. Comput. Sci. Inf. Eng., vol. 4, pp. 112–115, 2009.

[3] B. E. Adiana, I. Soesanti, and A. E. Permanasari, “Analisis Segmentasi Pelanggan Menggunakan Kombinasi RFM Model dan Teknik Clustering,” Jutei, vol. 2, no. 2, pp. 23–32, 2018, doi: 10.21460/jutei.2017.21.76.

[4] D. R. Liu and Y. Y. Shih, “Integrating AHP and data mining for product recommendation based on customer lifetime value,” Inf. Manag., vol. 42, no. 3, pp. 387–400, 2005, doi: 10.1016/j.im.2004.01.008.

[5] D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, Recommender Systems An Introduction. New York: Cambridge University Press, 2011.

[6] D. R. Liu, C. H. Lai, and W. J. Lee, “A hybrid of sequential rules and collaborative filtering for product recommendation,” Inf. Sci. (Ny)., vol. 179, no. 20, pp. 3505–3519, 2009, doi: 10.1016/j.ins.2009.06.004.

[7] M. S. Kahreh, M. Tive, A. Babania, and M. Hesan, “Analyzing the Applications of Customer Lifetime Value (CLV) based on Benefit Segmentation for the Banking Sector,” Procedia - Soc. Behav. Sci., vol. 109, no. Clv, pp. 590–594, 2014, doi: 10.1016/j.sbspro.2013.12.511. [Online]. Available: http://linkinghub.elsevier.com/retrieve/pii/S1877042813051434

[8] D. Birant, “Data Mining Using RFM Analysis,” Knowledge-Oriented Appl. Data Min., no. iii, 2011, doi: 10.5772/13683.

[9] J. Wu and Z. Lin, “Research on customer segmentation model by clustering,” ACM Int. Conf. Proceeding Ser., vol. 113, pp. 316–318, 2005, doi: 10.1145/1089551.1089610.

[10] Y. W. Trio Pramono and Suhardi, “Anomaly-based intrusion detection and prevention system on website usage using rule-growth sequential pattern analysis: Case study: Statistics of Indonesia (BPS) website,” in Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014, 2015, doi: 10.1109/ICAICTA.2014.7005941.

[11] P. Fournier-Viger, C. W. Wu, V. S. Tseng, L. Cao, and R. Nkambou, “Mining Partially-Ordered Sequential Rules Common to Multiple Sequences,” IEEE Trans. Knowl. Data Eng., vol. 27, no. 8, pp. 2203–2216, 2015, doi: 10.1109/TKDE.2015.2405509.

[12] R. Juliastio and D. Gunawan, “Sequential Pattern Mining Dengan Spade Untuk Prediksi Pembelian Spare Part Dan Aksesoris Komputer Pada Kedatangan Kembali Konsumen,” pp. 314–325, 2015.

[13] D. R. Liu, C. H. Lai, and W. J. Lee, “A hybrid of sequential rules and collaborative filtering for product recommendation,” Inf. Sci. (Ny)., vol. 179, no. 20, pp. 3505–3519, 2009, doi: 10.1016/j.ins.2009.06.004. [Online]. Available: http://dx.doi.org/10.1016/j.ins.2009.06.004

[14] P. Fournier-viger, R. Nkambou, and V. S. Tseng, “RuleGrowth : Mining Sequential Rules Common to Several Sequences by Pattern-Growth,” Symp. Appl. Comput., 2011, doi: 10.1145/1982185.1982394.

[15] B. Santosa, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu, 2007.

[16] Sugiyono, Statistika Untuk Penelitian. Bandung: CV Alfabeta, 2007.



DOI: https://doi.org/10.22146/ijccs.58107

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