Promotional Strategy for Indonesian Streetfood by using Market Basket Analysis case study:Seblak

Abstract

This research seeks to optimize product bundling strategies for self-serve seblak dining experiences through the application of Market Basket Analysis (MBA) using the Apriori algorithm. This study aims to identify optimal combinations of ingredients that enhance customer satisfaction while maximizing sales performance. A dataset comprising 3,302 transaction records involving 17 unique seblak ingredients was analyzed. The Apriori Algorithm was employed to extract frequent itemsets and develop association rules based on predefined threshold for support, lift, and confidence. The analysis identified a combination of five key ingredients─basreng, chicken egg, fish cake, chicken sausage, and mustard green─as the most suitable bundle, achieving a support value of 3,4%, a lift of 1.0026, and a confidence level of 50,81%. This bundling strategy offers a well-balanced and appealing seblak bowl, simplifying customer choices and driving business profitability. The findings demonstrate the value of data-driven methods in optimizing product bundling within the culinary industry, emphasizing the potential of MBA to improve customer experience and operational effectiveness.