Abstract: In the fast-evolving digital landscape, personalization has become a cornerstone of effective marketing strategies. A Personalized Digital Marketing Recommender Engine leverages advanced technologies such as artificial intelligence (AI), machine learning (ML), and data analytics to analyze customer behavior and deliver tailored recommendations that resonate with individual preferences.
This study presents a comprehensive model for implementing a personalized recommender engine to optimize customer engagement, enhance decision-making, and drive sales. The proposed model integrates real-time data processing with diverse selling strategies, including up-selling, crossselling, and consultative selling, while clustering items, customers, and unique selling propositions (USPs) to generate actionable insights.
By gathering, storing, and processing transactional data, the engine delivers highly relevant marketing information, ensuring seamless personalization across online and offline platforms.
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