This article examines consumer behaviour’s impact on marketing campaigns' effectiveness using a recommender system and statistical analysis methods. Understanding consumer behaviour is essential in today's fiercely competitive and constantly evolving market. Our study aims to highlight the significant impact of consumer behaviour on marketing data through the innovative application of recommender systems supported by state-of-the-art machine learning and data analysis techniques. This approach addresses the formidable challenges of accurately predicting consumer behaviour. We provide a detailed introduction to recommendation systems, emphasizing their vital role in the modern marketing landscape. We then outline our theories, laying the groundwork for a deeper understanding of the relationship between marketing data and consumer behaviour. Additionally, we present a rigorous data analysis process that begins with data cleaning and progresses through univariate and bivariate analysis, culminating in advanced techniques such as the Apriori algorithm to discover association rules and thoroughly explore this symbiotic relationship. Our findings demonstrate the applicability and effectiveness of our methodology for interpreting the complex interplay between consumer behaviour and marketing data. Our conclusions highlight essential trends and offer practical recommendations for enhancing marketing strategies significantly. By elucidating the dynamic relationships between consumer behaviour and marketing outcomes, our study contributes to a more sophisticated understanding of consumer dynamics in the contemporary business environment. Furthermore, this paper underscores the importance of understanding consumer behaviour and the benefits of employing innovative data analysis methods. By decoding consumption trends, businesses can optimize their marketing strategies and improve customer satisfaction, strengthening their competitive edge in a constantly shifting market. Finally, incorporating recommender systems with artificial intelligence and machine learning tools for collaborative filtering can further refine these strategies, substantially boosting marketing efficacy.
