Consumer Behaviour: Analysing Marketing Campaigns through Recommender Systems and Statistical Techniques
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- Sidi Mohamed Ben Abdellah University, Morocco
- Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
- ENSIAS, Mohammed V University in Rabat, Morocco
- Faculty of Science, IPSS Laboratory, Mohammed V University in Rabat, Morocco
* Corresponding author
Pages: 1–12
Received: 10 January 2024
Revised: 12 June 2024
Accepted: 20 June 2024
Abstract
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.
Keywords: apriori; decision analytics; machine learning; marketing data; recommendation system.
How to Cite: Cherkaoui, N., El Handri, K., Medard, D. Y, T., El Hassani, Y., & Errafyg, A. (2024). Consumer Behaviour: Analysing Marketing Campaigns through Recommender Systems and Statistical Techniques. Marketing and Management of Innovations, 15(3), 140–151. https://doi.org/10.21272/mmi.2024.3-01
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References
- Aaker, D. A., & Moorman, C. (2023). Strategic market management. John Wiley & Sons. [Google Scholar]
- Al-Maolegi, M., & Arkok, B. (2014). An improved Apriori algorithm for association rules. arXiv preprint arXiv:1403.3948. [Google Scholar] [CrossRef]
- Arndt, J. (1986). Paradigms in consumer research: a review of perspectives and aapproaches. European Journal of Marketing, 20(8), 23-40. [Google Scholar] [CrossRef]
- Battalio, R. C., Fisher Jr, E. B., Kagel, J. H., Basmann, R. L., Winkler, R. C., & Krasner, L. (1974). An experimental investigation of consumer behaviour in a controlled environment. Journal of Consumer Research, 1(2), 52-60. [Google Scholar] [CrossRef]
- Ben Ticha, S. (2015). Recommandation personnalisée hybride(Doctoral dissertation, Université de Lorraine). [Google Scholar]
- El Handri, K., & Idrissi, A. (2019). Étude comparative de Topk basée sur l’algorithme de Fagin en utilisant des métriques de corrélation dans la qualité de service de Cloud Computing. In EGC(pp. 359-360). [Google Scholar]
- El handri, K., & Idrissi, A. (2020). Comparative study of Topk based on Fagin’s algorithm using correlation metrics in cloud computing QoS. International Journal of internet Technology and Secured Transactions, 10(1-2), 143-170. [Google Scholar] [CrossRef]
- El Handri, K., & Idrissi, A. (2022). Correlations and Hierarchical Clustering Investigation Between Weather and SARS-CoV-2. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 15(6), 859-867. [Google Scholar][CrossRef]
- El Handri, K., Idrissi, A., & Er-Rafyg, A. (2023a). Top KWS Algorithm in the Map-Reduce Paradigm for Cloud Computing QoS Recommendation System. In Modern Artificial Intelligence and Data Science: Tools, Techniques and Systems(pp. 3-13). Cham: Springer Nature Switzerland. [Google Scholar] [CrossRef]
- El Handri, K., Rachdi, M., & El Bouchti, K. (2023b). Tweet Recommendation System Based on TA Algorithm and Natural Language Processing. In The International Conference of Advanced Computing and Informatics(pp. 197-203). Cham: Springer International Publishing. [Google Scholar] [CrossRef
- Er-Rafyg, A., Idrissi, A., & El Handri, K. (2023). Improvement of Courses Recommendation System using Divide and Conquer Algorithm. In Modern Artificial Intelligence and Data Science: Tools, Techniques and Systems(pp. 37-47). Cham: Springer Nature Switzerland. [Google Scholar] [CrossRef]
- Ez-Zahraouy, H. (2023). Dynamics Behaviour of Vehicular Traffic Flow in a Scale-Free Complex Network Check for updates Siham Lamzabi, Kaoutar El Handri, Marwa Benyoussef. Modern Artificial Intelligence and Data Science: Tools, Techniques and Systems, 1102, 261. [Google Scholar]
- Handri, K. E. L., & Idrissi, A. (2020). Efficient Top-kws algorithm on synthetics and real datasets. International journal of Artificial Intelligent (IJAI).
- Handri, K. E. L., & Idrissi, A. (2022). System collaboratif d’aide à la décision à base des recommandations multi critères. Fascicule de brevet. [Link]
- Hegland, M. (2007). The apriori algorithm–a tutorial. Mathematics and computation in imaging science and information processing, 209-262. [Google Scholar] [CrossRef]
- Idrissi, A., El Handri, K., Rehioui, H., & Abourezq, M. (2016). Top-k and skyline for cloud services research and selection system. In Proceedings of the International Conference on Big Data and Advanced Wireless Technologies(pp. 1-10). [Google Scholar] [CrossRef]
- Ogiemwonyi, O., & Jan, M. T. (2023). The correlative influence of consumer ethical beliefs, environmental ethics, and moral obligation on green consumption behaviour. Resources, Conservation & Recycling Advances, 19, 200171. [Google Scholar] [CrossRef]
- Solomon, M., Russell-Bennett, R., & Previte, J. (2012). Consumer behaviour. Pearson Higher Education AU. [Google Scholar]
- Tamuliene, V., & Pilipavicius, V. (2017, December). Research in customer preferences selecting insurance services: a case study of Lithuania. Forum Scientiae Oeconomia, 5(4), 49-58. [Google Scholar] [CrossRef]
- Tran, D. T., & Huh, J. H. (2023). Forecast of seasonal consumption behaviour of consumers and privacy-preserving data mining with new S-Apriori algorithm. The Journal of Supercomputing, 79(11), 12691–12736. [Google Scholar] [CrossRef]
- Trinquecoste, J. F. (1999). Pour une clarification théorique du lien marketing-stratégie. Recherche et Applications en Marketing (French Edition), 14(1), 59–80. [Google Scholar] [CrossRef]
- Zaki, K., & Shared, H. (2023). Modelling Sustainable Marketing with Retail Consumers’ Purchasing Intentions: Evidence from the MENA Region. Virtual Economics, 6(4), 25–43. [Google Scholar] [CrossRef]
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