Implementation of Data Mining to Analyze Consumer Purchasing Patterns at CV XYZ Using the Apriori Algorithm

Abstract

CV XYZ is a retail store located on Jl. Tidar, Surabaya, East Java, specializing in the trade of industrial and household chemical products. To remain competitive amid rapid technological advancements and increasing competition in the area, CV XYZ has adopted online sales through the Shopee platform. This study aims to analyze consumer purchase patterns using the Apriori algorithm based on sales transaction data to support effective and efficient marketing strategies. Based on the analysis, five purchasing patterns were identified: (1) if Silicon Oil is purchased, Counterdust will also be purchased with a support value of 0.391 and a confidence value of 0.818; (2) if Cocamidopropyl Betain is purchased, Counterdust will also be purchased with a support value of 0.391 and a confidence value of 0.857; (3) if Car Shampoo is purchased, Counterdust will also be purchased with a support value of 0.359 and a confidence value of 0.868; (4) if Talc is purchased, Counterdust will also be purchased with a support value of 0.348 and a confidence value of 0.842; and (5) if Linear Alkylbenzene Sulfonate is purchased, Counterdust will also be purchased with a support value of 0.359 and a confidence value of 0.892. These findings indicate that data mining techniques using the Apriori algorithm provide an effective approach for identifying consumer purchasing patterns of chemical products sold online through the Shopee platform. This insight can help businesses optimize their marketing strategies and decision-making processes.