Email this article 
Analysis of consumer characteristics on retail business with clustering analysis method and association rule for selling improvement strategy recommendations
Abstract
In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.
Keywords
Full Text:
PDFReferences
M. A. Rizaty, “Jumlah Toko Retail di Indonesia Sebanyak 3,98 Juta pada 2022.” Accessed: Nov. 06, 2023. [Online]. Available: https://dataindonesia.id/industri-perdagangan/detail/jumlah-toko-retail-di-indonesia-sebanyak-398-juta-pada-2022
F. Achmad, O. Nurdiawan, and Y. A. Wijaya, “Analisa Pola Transaksi Pembelian Konsumen pada Toko Ritel Kesehatan Menggunakan Algoritma FP-Growth,” Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 1, pp. 168–175, 2023.
E. Fitriyani, “Hippindo Ungkap pemicu gerai ritel berguguran: Tempat kegedean, Kumparan.” Accessed: Dec. 03, 2023. [Online]. Available: https://kumparan.com/kumparanbisnis/hippindo-ungkap-pemicu-gerai-ritel-berguguran-tempat-kegedean-1zyVPyvpBIH/3
N. H. Tamara, “Perubahan Peta Persaingan Bisnis Retail di Indonesia.” [Online]. Available: https://katadata.co.id/analisisdata/2019/07/03/perubahan-peta-persaingan-bisnis-retail-di-indonesia
K. Auliasari and M. Kertaningtyas, “Penerapan Algoritma K-Means untuk Segmentasi Konsumen Menggunakan R.,” Jurnal Teknologi & Manajemen Informatika, vol. 5, no. 1, 2019.
Martina, “7 Strategi Pemasaran Pada Bisnis Retail (Eceran).” [Online]. Available: https://ukirama.com/blogs/7-strategi-pemasaran-pada-bisnis-retail-eceran
IBM, Analytics: The real-world use of big data in retail. IBM Global Business Services, 2018.
K. Marwazia Shaliha, A. Angelyna, A. Aulia Nugraha, M. Humam Wahisyam, and T. Kurnia Sandi, “Implementasi K-Means Clustering pada Online Retail berdasarkan Recency, Frequency, dan Monetary (Implementation of K-Means Clustering in Online Retail based on Recency, Frequency, and Monetary),” Gunung Djati Conference Series, vol. 3, 2021, [Online]. Available: https://conferences.uinsgd.ac.id/gdcs
A. Sani, “Penerapan Metode K-means Clustering pada Perusahaan ,” Jurnal Ilmiah Teknologi Informasi , vol. 1, no. 7, 2018.
I. Kamila, U. Khairunnisa, and Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan,” Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, vol. 5, no. 1, pp. 119–125, 2019.
M. Kaur and S. Kang, “Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining,” in Procedia Computer Science, 2016, pp. 78–85. doi: 10.1016/j.procs.2016.05.180.
Y. Permata Bunda, “ALGORITMA FP-GROWTH UNTUK MENGANALISA POLA PEMBELIAN OLEH-OLEH (STUDI KASUS DI PUSAT OLEH-OLEH UMMI AUFA HAKIM).”
A. Setiawan, D. Indra, and G. Anugrah, “Penentuan Pola Pembelian Konsumen pada Indomaret GKB Gresik dengan Metode FP-Growth,” Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 2, no. 2, 2019.
Erwin, “Analisis Market Basket Dengan Algoritma Apriori dan FP-Growth,” Jurnal Generic, vol. 2, no. 2, pp. 472–478, 2009, Accessed: Dec. 10, 2023. [Online]. Available: http://generic.ilkom.unsri.ac.id/index.php/generic/article/view/15
S. Fitri Octavia, I. Permana, and S. Monalisa, “JURNAL MEDIA INFORMATIKA BUDIDARMA Penerapan Algoritma Association Rules Dalam Penentuan Pola Pembelian Berdasarkan Hasil Clustering,” 2023, doi: 10.30865/mib.v7i3.6129.
S. Sheng, A. M. Parker, and K. Nakamoto, “The effects of price discount and product complementarity on consumer evaluations of bundle components,” Journal of Marketing Theory and Practice, vol. 15, no. 1, pp. 53–64, 2007.
R. Rahmattullah and R. Yanti, “Peningkatan Strategi Penjualan dengan metode Association Rule Pada Toko Ritel ABC,” 2022.
I. Faisal, “Pengaruh Price Discount, Bonus Pack, Dan In-Store Display Terhadap Keputusan Impulse Buying Pada Giant Ekstra Banjar.,” Jurnal Sains Manajemen Dan Kewirausahaan, vol. 4, no. 2, pp. 266–274, 2018.
DOI: https://doi.org/10.31315/opsi.v17i1.11411
Refbacks
- There are currently no refbacks.
Sekretariat :
Jurusan Teknik Industri
FTI UPN "Veteran" Yogyakarta
d.a Jalan Babarsari 2 Tambakbayan Yogyakarta 55281
Telp. (0274) 486256
Website http://jurnal.upnyk.ac.id/index.php/opsi
email : jurnal.opsi@upnyk.ac.id
indexed by:
 |  |  |  |
 |  |  |  |
 |
This work is Licensed Under a Creative Commons Attribution 4.0 International license.
View My Stats