Marketing Strategy Analysis Using Apriori Association Method To Increase Sales In E-Commerce Companies

Nadilla Okviannas; Sunu Widianto; Rita Komaladewi

doi:10.38035/dijefa.v6i3.4681

Authors

Nadilla Okviannas Padjajaran University, West Java, Indonesia
Sunu Widianto Padjajaran University, West Java, Indonesia
Rita Komaladewi Padjajaran University, West Java, Indonesia

DOI:

https://doi.org/10.38035/dijefa.v6i3.4681

Keywords:

Marketing Strategy, Data Mining, Apriori Algorithm, FP-Growth, E-Commerce

Abstract

This research aims to analyze marketing strategies by utilizing association data mining algorithms, specifically Apriori and FP-Growth, to increase sales in e-commerce companies. The data used consists of actual sales data from Blibli.com, analyzed using RapidMiner to identify significant purchasing patterns. The analysis results show a strong association between products such as "Mother & Baby Needs – Body Skin Care" (support 0.108, confidence 0.476) and "Skin Care – Household Care" (support 0.195, confidence 0.443). These patterns indicate opportunities to design marketing strategies based on product bundling, discounts, and personalized digital promotions.The Apriori algorithm provides more intuitive and relevant results for marketing analysis compared to FP-Growth, which is better suited for larger datasets. The proposed marketing strategies include digital campaigns, bundling offers, push notifications, and event marketing that emphasize consumers' functional and emotional values. This study highlights the importance of understanding consumer transaction patterns in designing effective marketing strategies to enhance customer appeal and e-commerce company profits. By applying data mining, companies can create more personalized and efficient shopping experiences tailored to market needs.

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