Optimizing Product Promotion with Smart Bundling: An FP-Growth and IT-Enabled Analytical Framework

Abstract

In a business, product promotion is necessary. This is done to increase sales and enhance the competitiveness of the business amid similar market competition. One strategy that can be used in promotion is smart bundling, which combines products into a single package offering that can increase the value of consumer purchases. Problems arise when determining the optimal product combinations is done subjectively without the support of systematic and strong data analysis. The aim of this research is to obtain suitable product bundling based on customer transaction history data, so that the produced products can increase the sales volume itself. In this study, product bundling will be carried out using an information technology-based analytical framework using the Frequent Pattern Growth (FP-Growth) algorithm. This method can identify customers' usual purchasing patterns and generate smart and data-driven product bundling recommendations. The results of the study show that 50% of consumers order Bengawan rice along with signature coffee, indicating that the two products can be sold together at a competitive price. This will impact an increase in sales volume as well as customer loyalty.