Using Decision Trees for Targeted Marketing in Vending Machine Upgrades

Abstract

The vending machine industry is experiencing rapid technological advancements, with smart vending machines offering features like cashless payments, remote monitoring, and AI-enabled functionalities. Effectively marketing these upgrades requires precise targeting to ensure relevance and cost-efficiency. This study investigates the use of decision trees, a machine learning technique, to identify and target customer segments most likely to adopt vending machine upgrades. Leveraging real-world data on customer demographics, purchase history, and usage patterns, the study demonstrates the efficacy of decision trees in optimizing marketing campaigns. The findings suggest that targeted marketing using decision trees increases conversion rates and enhances overall customer satisfaction while reducing marketing expenditures.