Product bundling and substitution recommendation system: Facilitating marketing improvement strategy of retail business

Ellysa Tjandra; Liliana Liliana

doi:10.24246/aiti.v22i1.29-45

Authors

Ellysa Tjandra Program Studi Teknik Informatika, Fakultas Teknik, Universitas Surabaya
Liliana Program Studi Teknik Informatika https://orcid.org/0000-0002-6550-0845

DOI:

https://doi.org/10.24246/aiti.v22i1.29-45

Keywords:

apriori, information system, market basket, product bundling, TOPSIS

Abstract

A retail store is a place where a variety of everyday items are sold. Supermarkets need to use different tactics to sell their products and attract consumers. According to an interview with an employee of Supermarket X, one strategy supermarkets use is to bundle products. This involves combining two or more products into one package and selling them at a discounted price. Supermarkets sometimes need help choosing the products to bundle and finding similar products due to limited stock. Based on the problem description, it can be inferred that the supermarket system will utilize the Apriori algorithm for creating product bundling and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to showcase similar products and predict if any products are running low on stock. The system's performance has been evaluated through trials and validation at the supermarket. The findings showed that they were able to determine which products may be included in the package with the use of this product packaging automation system. The results were promising, indicating that choosing replacement products can aid in lowering the inventory of goods that are not doing well.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

C. H. Wu and P. D. L. Pambudi, “On-Premise Software vs. Cloud-Based Software under the Presence of Product Bundling,” Procedia Comput. Sci., vol. 234, pp. 180–187, 2024, doi: 10.1016/j.procs.2024.02.164. DOI: https://doi.org/10.1016/j.procs.2024.02.164

J. Yap and T. Wiradinata, “A comparative study on classification models for stock rating prediction,” Aiti, vol. 21, no. 1, pp. 140–151, 2024, doi: 10.24246/aiti.v21i1.140-151. DOI: https://doi.org/10.24246/aiti.v21i1.140-151

M. Mutasar and C. Niesa, “Analisis Transaksi Konsumen Bidang Data Mining Menggunakan Algoritma Apriori Untuk Rekomendasi Bundling Produk Pada 212 Mart Kota Lhokseumawe,” J. Tika, vol. 6, no. 02, pp. 92–98, 2021, doi: 10.51179/tika.v6i02.463. DOI: https://doi.org/10.51179/tika.v6i02.463

A. Vamosiu, “Optimal bundling under imperfect competition,” Int. J. Prod. Econ., vol. 195, pp. 45–53, 2018, doi: 10.1016/j.ijpe.2017.09.016. DOI: https://doi.org/10.1016/j.ijpe.2017.09.016

A. D. Puspitasari, H. Riofita, and H. Com, “Strategi Pemasaran Product Bundling pada Produk Kosmetik Wardah dalam Meningkatkan Penjualan Produk Retail,” Ekonodinamika J. Ekon. Din., vol. 6, no. 2, pp. 139–146, 2024, [Online]. Available: https://journalpedia.com/1/index.php/jed.

P. T. Pungkasanti, N. Wakidah, and R. R. F. Kurniawan, “Penerapan metode Weighted Aggregated Sum Product Assessment (WASPAS) dalam menentukan reseller terbaik,” Aiti, vol. 20, no. 2, pp. 206–219, 2023, doi: 10.24246/aiti.v20i2.206-219. DOI: https://doi.org/10.24246/aiti.v20i2.206-219

Z. Sun, J. Yang, K. Feng, H. Fang, X. Qu, and Y. S. Ong, Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling, vol. 1, no. 1. Association for Computing Machinery, 2022. DOI: https://doi.org/10.1145/3477495.3531904

D. Lukyani, R. Arifin, and A. B. Primanto, “Pengaruh Pemasaran Cross selling, Pemasaran Viral, dan Pemasaran Produk Bundling Terhadap Keputusan Pembelian Pada Produk Skintific (Studi Pada Konsumen di Kecamatan Lowokwaru Kota Malang),” e – J. Ris. Manaj., vol. 12, no. 01, pp. 510–518, 2023, doi: http://dx.doi.org/10.24912/jseb.v1i1.22734. DOI: https://doi.org/10.24912/jseb.v1i1.22734

T. Chen, F. Shan, F. Yang, and F. Xu, “Online retailer bundling strategy in a dual-channel supply chain,” Int. J. Prod. Econ., vol. 259, 2023, doi: https://doi.org/10.1016/j.ijpe.2023.108821. DOI: https://doi.org/10.1016/j.ijpe.2023.108821

T. Nie, B. Song, and J. Zhang, “Sales pricing models based on returns: Bundling vs. add-on,” Omega, vol. 125, 2024, doi: https://doi.org/10.1016/j.omega.2024.103038. DOI: https://doi.org/10.1016/j.omega.2024.103038

J. Li, L. Liu, X. Luo, and S. X. Zhu, “Interactive bundle pricing strategy for online pharmacies,” Transp. Res. Part E Logist. Transp. Rev., vol. 177, no. July, p. 103223, 2023, doi: 10.1016/j.tre.2023.103223. DOI: https://doi.org/10.1016/j.tre.2023.103223

O. K. Sari and A. Cherid, “Aplikasi Berbasis Web Menggunakan Apriori Untuk Rekomendasi Bundling Produk Sembako,” Bit (Fakultas Teknol. Inf. Univ. Budi Luhur), vol. 20, no. 2, p. 95, 2023, doi: 10.36080/bit.v20i2.2444. DOI: https://doi.org/10.36080/bit.v20i2.2444

F. Anggraini and A. Budiarti, “Pengaruh Harga, Promosi, dan Kualitas Pelayanan Terhadap Loyalitas Pelanggan Dimediasi Kepuasan Pelanggan Pada Konsumen Gojek,” J. Pendidik. Ekon., vol. 8, no. 3, pp. 86–94, 2020, doi: 10.26740/jupe.v8n3.p86-94. DOI: https://doi.org/10.26740/jupe.v8n3.p86-94

S. Bera and B. C. Giri, “Impact of consumer preferences on pricing and strategic decisions in a triopoly with heterogeneous smart sustainable supply chains,” Expert Syst. Appl., vol. 247, no. January, p. 123348, 2024, doi: 10.1016/j.eswa.2024.123348. DOI: https://doi.org/10.1016/j.eswa.2024.123348

S. Aryanti, D. Mahdiana, and A. Setiadi, “Penerapan Metode K-Means Dan Apriori Untuk Pemilihan Produk Bundling,” J. CERITA, vol. 8, no. 1, pp. 1–12, 2022, doi: 10.33050/cerita.v8i1.2126. DOI: https://doi.org/10.33050/cerita.v8i1.2126

V. Venkatesh, H. Hoehle, J. A. Aloysius, and H. R. Nikkhah, “Being at the cutting edge of online shopping: Role of recommendations and discounts on privacy perceptions,” Comput. Human Behav., vol. 121, no. August 2020, p. 106785, 2021, doi: 10.1016/j.chb.2021.106785. DOI: https://doi.org/10.1016/j.chb.2021.106785

M. Zhang and J. Bockstedt, “Complements and substitutes in online product recommendations: The differential effects on consumers’ willingness to pay,” Inf. Manag., vol. 57, no. 6, p. 103341, 2020, doi: 10.1016/j.im.2020.103341. DOI: https://doi.org/10.1016/j.im.2020.103341

V. Gupta, D. Ivanov, and T. Choi, “Competitive pricing of substitute products under supply disruption,” Omega, vol. 101, no. January, 2021. DOI: https://doi.org/10.1016/j.omega.2020.102279

R. R. Ramadina, T. H. Pudjiantoro, and I. Santikarama, “Pembangunan Sistem Customer Relationship Management (CRM) Menggunakan Metode Asosiasi Algoritma Apriori,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 2, no. 2, p. 78, 2020, doi: 10.36499/jinrpl.v2i2.3539. DOI: https://doi.org/10.36499/jinrpl.v2i2.3539

A. R. Riszky and M. Sadikin, “Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan,” J. Teknol. dan Sist. Komput., vol. 7, no. 3, pp. 103–108, 2019, doi: 10.14710/jtsiskom.7.3.2019.103-108. DOI: https://doi.org/10.14710/jtsiskom.7.3.2019.103-108

S. B. Aher and L. M. R. J. Lobo, “Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data,” Knowledge-Based Syst., vol. 51, pp. 1–14, 2013, doi: https://doi.org/10.1016/j.knosys.2013.04.015. DOI: https://doi.org/10.1016/j.knosys.2013.04.015

S. Setiawansyah, “Sistem Pendukung Keputusan Rekomendasi Tempat Wisata Menggunakan Metode TOPSIS,” J. Ilm. Inform. dan Ilmu Komput., vol. 1, no. 2, pp. 54–62, 2022, doi: 10.58602/jima-ilkom.v1i2.8. DOI: https://doi.org/10.58602/jima-ilkom.v1i2.8

Y. Çelikbilek and F. Tüysüz, “An in-depth review of theory of the TOPSIS method: An experimental analysis,” J. Manag. Anal., vol. 7, no. 2, pp. 281–300, 2020, doi: 10.1080/23270012.2020.1748528. DOI: https://doi.org/10.1080/23270012.2020.1748528

S. Chakraborty, “TOPSIS and Modified TOPSIS: A comparative analysis,” Decis. Anal. J., vol. 2, no. September 2021, p. 100021, 2022, doi: 10.1016/j.dajour.2021.100021. DOI: https://doi.org/10.1016/j.dajour.2021.100021

E. F. L. Awalina and W. I. Rahayu, “Optimalisasi Strategi Pemasaran dengan Segmentasi Pelanggan Menggunakan Penerapan K-Means Clustering pada Transaksi Online Retail,” J. Teknol. dan Inf., vol. 13, no. 2, pp. 122–137, 2023, doi: 10.34010/jati.v13i2.10090. DOI: https://doi.org/10.34010/jati.v13i2.10090

C.-H. Wang, “Considering economic indicators and dynamic channel interactions to conduct sales forecasting for retail sectors,” Comput. Ind. Eng., vol. 165, 2022, doi: https://doi.org/10.1016/j.cie.2022.107965. DOI: https://doi.org/10.1016/j.cie.2022.107965

A. Alshamrani and A. Bahattab, “A Comparison Between Three SDLC Models Waterfall Model, Spiral Model, and Incremental/Iterative Model,” IJCSI Int. J. Comput. Sci. Issues, vol. 12, no. 1, pp. 106–111, 2015, [Online]. Available: https://www.academia.edu/10793943/A_Comparison_Between_Three_SDLC_Models_Waterfall_Model_Spiral_Model_and_Incremental_Iterative_Model.

P. Dybka, “Crow’s Foot Notation,” Vertabelo SA, 2020. https://www.vertabelo.com/blog/crow-s-foot-notation/.

BPMN, “BPMN,” Object Management Group, Inc., 2020. http://www.bpmn.org/.

D. S. S. Sahid, P. I. Santosa, R. Ferdiana, and E. N. Lukito, “Evaluation and measurement of Learning Management System based on user experience,” Proc. - 2016 6th Int. Annu. Eng. Semin. Ina. 2016, pp. 72–77, 2017, doi: 10.1109/INAES.2016.7821910. DOI: https://doi.org/10.1109/INAES.2016.7821910

I. A. Ashari, A. Wirasto, and D. N. Triwibowo, “Implementasi Market Basket Analysis dengan Algoritma Apriori untuk Analisis Pendapatan Usaha Retail,” Matrik J. Manajemen, Tek. Inform. dan Rekayasa Komput. Vol., vol. 21, no. 3, 2022, doi: 10.30812/matrik.v21i3.1439. DOI: https://doi.org/10.30812/matrik.v21i3.1439

S. M. Katrachanca and A. J. Koleske, “Causal Interpretations of Black-Box Models,” J. Bus. Econ. Stat., 2019, doi: https://doi.org/10.1080/07350015.2019.1624293. DOI: https://doi.org/10.1080/07350015.2019.1624293