Implementation of FP-Growth Algorithms for Promo Package Determination in a Scooter Motorcycle Workshop Business
DOI:
https://doi.org/10.30871/jaic.v9i3.9499Keywords:
FP-Growth Algorithm, Bundle Package, Association rules, Data Mining, Customer SatisfactionAbstract
This study applies the FP-Growth algorithm to design bundled promotions for a scooter motorcycle accessory store and workshop in Denpasar, Bali. FP-Growth was chosen for its efficiency in mining frequent itemsets without generating candidate sets. From 23,381 transaction records (January-August 2024), the algorithm identified 16 association rules using a minimum support of 1% and confidence of 50%. These rules were selected based on lift values and product relevance. One notable example is the association between "BAUT TITANIUM GR5 M10 X 60" and "BAUT TITANIUM GR5 M8X50", which had a lift of 47.814, indicating a very strong co-purchase relationship. These high-lift combinations present valuable opportunities for bundling and targeted point-of-sale offers. The algorithm performed efficiently, with a runtime of just 0.1354 seconds and 402.6 MB of memory usage. Bundles based on these associations were presented to customers, and feedback was collected through a Customer Satisfaction (CSAT) survey involving 56 recent buyers. The survey yielded a high CSAT score of 83.93%, demonstrating customer satisfaction with the bundles’ relevance and appeal. These results confirm that FP-Growth can effectively inform promotional strategies by identifying strong product pairings that align with actual purchasing behavior. Strategically promoting such bundles not only enhances customer experience but also encourages multi-item purchases. This data-driven bundling approach is practical and profitable for medium-sized retail businesses, ultimately supporting the goal of increasing the Average Order Value.
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[1] I. A. A. Alsibhawi, J. B. Yahaya, and H. B. Mohamed, “Business Intelligence Adoption for Small and Medium Enterprises: Conceptual Framework,” Applied Sciences (Switzerland), vol. 13, no. 7, Apr. 2023, doi: 10.3390/app13074121.
[2] M. S. Hosen et al., “Data-Driven Decision Making: Advanced Database Systems for Business Intelligence,” Nanotechnol Percept, vol. 20, no. S3, pp. 687–704, 2024, doi: 10.62441/nano-ntp.v20iS3.51.
[3] M. Yusuf, N. M. Stie, and N. Sangatta, “ANALYSIS OF THE EFFECT OF THE MARKETING MIX ON PURCHASING DECISIONS”, [Online]. Available: https://ijemr.politeknikpratama.ac.id/index.php/ijemr
[4] H. Kismo and L. Lahindah, “Designing a Promotion Strategy to Increase Online Sales,” Jurnal Manajemen Bisnis, vol. 11, no. 2, pp. 1962–1975, Sep. 2024, doi: 10.33096/jmb.v11i2.975.
[5] Q. Wang, X. Zhu, M. Wang, F. Zhou, and S. Cheng, “A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis,” PLoS One, vol. 18, no. 5 May, May 2023, doi: 10.1371/journal.pone.0286034.
[6] R. M. Teja Nursasongka, I. Fahrurrozi, U. Y. Oktiawati, U. Taufiq, U. Farooq, and G. Alfian, “Utilizing association rule mining for enhancing sales performance in web-based dashboard application,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 36, no. 2, p. 1105, Nov. 2024, doi: 10.11591/ijeecs.v36.i2.pp1105-1113.
[7] S. Mohammed et al., “A statistical method for predicting quantitative variables in association rule mining,” Inf Syst, vol. 118, Sep. 2023, doi: 10.1016/j.is.2023.102253.
[8] J. Li, L. Liu, X. Luo, and S. X. Zhu, “Interactive bundle pricing strategy for online pharmacies,” Transp Res E Logist Transp Rev, vol. 177, Sep. 2023, doi: 10.1016/j.tre.2023.103223.
[9] X. Sun, A. Ngueilbaye, K. Luo, Y. Cai, D. Wu, and J. Z. Huang, “A scalable and flexible basket analysis system for big transaction data in Spark,” Inf Process Manag, vol. 61, no. 2, Mar. 2024, doi: 10.1016/j.ipm.2023.103577.
[10] A. F. Zakaria, S. C. J. Lim, and M. Aamir, “A pricing optimization modelling for assisted decision making in telecommunication product-service bundling,” International Journal of Information Management Data Insights, vol. 4, no. 1, Apr. 2024, doi: 10.1016/j.jjimei.2024.100212.
[11] V. Srinadh, “Evaluation of Apriori, FP growth and Eclat association rule mining algorithms,” Int J Health Sci (Qassim), pp. 7475–7485, Apr. 2022, doi: 10.53730/ijhs.v6ns2.6729.
[12] R. Wahyuningsih and A. Suharsono, “Comparison Of Market Basket Analysis Method Using Apriori Algorithm, Frequent Pattern Growth (Fp-Growth) And Equivalence Class Transformation (Eclat) (Case Study: Supermarket ‘X’ Transaction Data For 2021).”
[13] T. A. Djabalul Lael and D. A. Pramudito, “Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 4, no. 2, pp. 128–136, Mar. 2023, doi: 10.34306/itsdi.v4i2.582.
[14] A. Wenninger, D. Rau, and M. Röglinger, “Improving customer satisfaction in proactive service design: A Kano model approach,” Electronic Markets, vol. 32, no. 3, pp. 1399–1418, Sep. 2022, doi: 10.1007/s12525-022-00565-9.
[15] S. K. Jena and A. Ghadge, “Product bundling and advertising strategy for a duopoly supply chain: a power-balance perspective,” Ann Oper Res, vol. 315, no. 2, pp. 1729–1753, Aug. 2022, doi: 10.1007/s10479-020-03861-9.
[16] N. Purwati, Y. Pedliyansah, H. Kurniawan, S. Karnila, and R. Herwanto, “Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan,” vol. 8, no. 2, 2023.
[17] T. Lakshika and A. Caldera, “Association Rules for Knowledge Discovery From E-News Articles: A Review of Apriori and FP-Growth Algorithms,” Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 178–192, 2022, doi: 10.25046/aj070519.
[18] R. Wahyuningsih and A. Suharsono, “Comparison Of Market Basket Analysis Method Using Apriori Algorithm, Frequent Pattern Growth (Fp-Growth) And Equivalence Class Transformation (Eclat) (Case Study: Supermarket ‘X’ Transaction Data For 2021).”
[19] B. Praveen Kumar, T. Padmavathy, S. U. Muthunagai, and D. Paulraj, “An optimized fuzzy based FP-growth algorithm for mining temporal data,” Journal of Intelligent and Fuzzy Systems, vol. 46, no. 1, pp. 41–51, Jan. 2024, doi: 10.3233/JIFS-223030.
[20] K. S. Yanisa Putri, I Made Agus Dwi Suarjaya, and Wayan Oger Vihikan, “Sistem Rekomendasi Skincare Menggunakan Metode Content Based Filtering dan Collaborative Filtering,” Decode: Jurnal Pendidikan Teknologi Informasi, vol. 4, no. 3, pp. 764–774, Aug. 2024, doi: 10.51454/decode.v4i3.601.
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