Abstract:

Personalized marketing powered by Artificial Intelligence (AI) enables businesses to optimize consumer targeting and product recommendations for enhanced customer satisfaction and growth. By leveraging AI technologies, businesses can effectively reach their target audience, increase engagement, and drive higher sales. Existing methods of consumer targeting often rely on basic demographic data and limited consumer behavior insights, which can lead to suboptimal personalization and inefficiency. These traditional approaches lack the ability to capture complex patterns and preferences in large, diverse consumer datasets, resulting in generalized or inaccurate recommendations. The proposed framework, Segmenting Consumers for Targeted Product Recommendations using Clustering with K-Means Algorithm (C-KMA), seeks to address these limitations. This technique employs unsupervised machine learning to segment consumers based on their behavioral data, identifying distinct groups with similar preferences and purchasing patterns. By clustering consumers, businesses can deliver tailored product recommendations that resonate with each segment’s unique needs and preferences. The proposed method allows businesses to dynamically adjust marketing strategies, targeting consumers with relevant products, promotions, and content. The algorithm not only improves recommendation accuracy but also enhances customer retention and loyalty through personalized experiences. Findings from the implementation of the C-KMA method demonstrate improved targeting efficiency and increased conversion rates. By focusing on the identification of consumer segments and their specific preferences, businesses experience enhanced consumer satisfaction and a measurable boost in growth, with a more streamlined marketing approach that yields higher ROI.