Transformasi Pemasaran UMKM Bakpia Melalui Analisis Data Mining

Abstract

The micro, small, and medium enterprises (MSMEs) sector is critical to Indonesia's economy. Especially the food and beverage industry is one of the sectors that is relied on and has rapid growth. Although, business competition is getting tighter for MSMEs today, there needs to be a marketing strategy to improve business. This study aims to optimize marketing strategies in food and beverage MSMEs using data mining method integration, with a case study of MSME Bakpia Tugu. To optimize business improvement, this study integrates data mining methods: Analytical Hierarchy Process (AHP), Association Rules-Market Basket Analysis (AR-MBA), Clustering, and Classification. Data collection in this study used a questionnaire method from 150 respondents for AR-MBA data mining, clustering, and classification, and an expert interview method was used for AHP analysis. The research results get marketing strategy priorities from the results of the AHP analysis, namely through digital marketing strategies, identifying consumer purchasing patterns based on the relationship between items purchased from the results of AR-MBA, grouping customers based on characteristic proximity (Clustering) producing three customer segments that can be optimized for relevant promotional approaches, and Classification to predict customer arrival pattern behaviour. This research can be implemented to improve the food and beverage MSME business in an increasingly competitive market.