Decoding Consumer Behaviour: Leveraging Big Data and Machine Learning for Personalized Digital Marketing
DOI:
https://doi.org/10.56294/dm2025700Keywords:
Consumer Behaviour, Data and Metadata, Machine Learning, Digital Marketing, PersonalisationAbstract
Introduction
Big data analytics and machine learning have transformed digital marketing by enabling data-driven insights for personalization. This study investigates the role of engagement metrics, sentiment analysis, and consumer segmentation in enhancing marketing effectiveness. Specifically, it examines how these technologies process consumer interaction data to uncover actionable insights, segment audiences, and drive purchase conversions.
Method
The study employed a mixed-methods approach, integrating big data analytics and machine learning techniques. Descriptive statistics highlighted engagement patterns, while k-means clustering segmented consumers based on behavioural and emotional data. Sentiment analysis, conducted using Natural Language Processing (NLP), captured consumer emotions as positive, neutral, or negative. Regression analysis evaluated the influence of social media activity, click-through rates, session duration, and sentiment scores on purchase conversion rates.
Results
Descriptive analysis revealed significant variability in consumer engagement and sentiment, with 37.5% of consumers expressing positive sentiment. Clustering identified three distinct consumer segments, reflecting differences in engagement and sentiment. Regression analysis showed that sentiment had a positive but statistically insignificant relationship with purchase conversions, while other metrics, such as click-through rates and session duration, exhibited minimal impact. The overall explanatory power of the regression model was low (R-squared = 0.001), indicating the need for additional factors to understand purchase behaviour.
Conclusion
The findings emphasize the potential of big data analytics and machine learning in consumer segmentation and sentiment analysis. However, their direct impact on purchase conversion is limited without integrating broader variables. A holistic, adaptive framework combining behavioural, emotional, and contextual insights is essential for maximizing marketing personalization and driving outcomes in dynamic digital environments.
References
1. Tripathi, A., Bagga, T., Sharma, S., & Vishnoi, S. K. (2021). Big Data-Driven Marketing enabled Business Performance : A Conceptual Framework of Information, Strategy and Customer Lifetime Value. In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (p. 315). https://doi.org/10.1109/confluence51648.2021.9377156
2. Mohammad, A. A. S., Alolayyan, M. N., Al-Daoud, K. I., Al Nammas, Y. M., Vasudevan, A., & Mohammad, S. I. (2024). Association between Social Demographic Factors and Health Literacy in Jordan. Journal of Ecohumanism, 3(7), 2351-2365.
3. Mohammad, A. A. S., Khanfar, I. A., Al Oraini, B., Vasudevan, A., Suleiman, I. M., & Fei, Z. (2024). Predictive analytics on artificial intelligence in supply chain optimization. Data and Metadata, 3, 395-395. http://dx.doi.org/10.56294/dm2024395
4. Saheb, T., & Amini, B. (2021). The impact of artificial intelligence analytics in enhancing digital marketing: the role of open big data and AI analytics competencies. In Research Square (Research Square). Research Square (United States). https://doi.org/10.21203/rs.3.rs-714137/v1
5. Mohammad, A. A. S., Shelash, S. I., Saber, I. T., Vasudevan, A., Darwazeh, Almajali,R. N. R., Fei, Z. (2025). Internal Audit Governance Factors and their effect on the Risk-Based Auditing Adoption of Commercial Banks in Jordan. Data and Metadata, 4, 464. http://dx.doi.org/10.56294/dm2025464
6. Mohammad, A. A. S., Al-Hawary, S. I. S., Hindieh, A., Vasudevan, A., Al-Shorman, H. M., Al-Adwan, A. S., Alshurideh, M. T., Ali, I. (2025). Intelligent Data-Driven Task Offloading Framework for Internet of Vehicles Using Edge Computing and Reinforcement Learning. Data and Metadata, 4, 521. https://doi.org/10.56294/dm2025521
7. Mohammad, A. A. S., Alshebel, M., Al Oraini, B., Vasudevan, A., Shelash Mohammad, S. I, Jiang, H., Al Sarayreh, A. (2024). Research on Multimodal College English Teaching Model Based on Genetic Algorithm. Data and Metadata, 3, 421. https://doi.org/10.56294/dm2024421
8. Mohammad, A. A. S., Al-Daoud, K. I., Al Oraini, B., Shelash Mohammad, S. I., Vasudevan, A., Zhang, J., Hunitie A. M. F. (2024). Using Digital Twin Technology to Conduct Dynamic Simulation of Industry-Education Integration. Data and Metadata, 3, 422. https://doi.org/10.56294/dm2024422
9. Mohammad, A. A. S., Masadeh, M., Vasudevan, A., Barhoom, F. N. I., Mohammad, S. I., Abusalma, A., & Alrfai, M. M. (2024). The Impact of the Green Supply Chain Management Practices on the Social Performance of Pharmaceutical Industries. In Frontiers of Human Centricity in the Artificial Intelligence-Driven Society 5.0 (pp. 325-339). Springer, Cham. https://doi.org/10.1007/978-3-031-73545-5_28
10. A LAKSHMI, PRIYANKA, M , H., Prasanna, Mrs. S. G., & Yadav, D. (2023). A Study on Artificial Intelligence in Marketing. In International Journal For Multidisciplinary Research (Vol. 5, Issue 3). https://doi.org/10.36948/ijfmr.2023.v05i03.3789
11. Guerrini, A., Ferri, G., Rocchi, S., Cirelli, M., Martínez, V. P., & Grieszmann, A. (2023). Personalization @ scale in airlines: combining the power of rich customer data, experiential learning, and revenue management. In Journal of Revenue and Pricing Management (Vol. 22, Issue 2, p. 171). Palgrave Macmillan. https://doi.org/10.1057/s41272-022-00404-8
12. Mohammad, A. A. S., Mohammad, S. I., Vasudevan, A., Al-Momani, A. A. M., Masadeh, M., Kutieshat, R. J., & Mohammad, A. I. (2024f). Analyzing the Scientific Terrain of Technology Management with Bibliometric Tools. In Frontiers of Human Centricity in the Artificial Intelligence-Driven Society 5.0 (pp. 489-502). Springer, Cham. https://doi.org/10.1007/978-3-031-73545-5_41
13. Mohammad, A. A. S., Alshurideh, M. T., Mohammad, A. I., Alabda, H. E., Alkhamis, F. A., Al Oraini, B., & Kutieshat, R. J. (2024g). Impact of Organizational Culture on Marketing Effectiveness of Telecommunication Sector. In Frontiers of Human Centricity in the Artificial Intelligence-Driven Society 5.0 (pp. 231-244). Springer, Cham. https://doi.org/10.1007/978-3-031-73545-5_21
14. Boshers, J. (2022). Jordan Digital Marketing Country Profile. https://istizada.com/jordan-online-marketing-country-profile/
15. Mohammad, A. A. S., Al Oraini, B., Mohammad, S., Masadeh, M., Alshurideh, M. T., Almomani, H. M., & Al-Adamat, A. M. (2024h). Analysing the Relationship Between Social Content Marketing and Digital Consumer Engagement of Cosmetic Stores. In Frontiers of Human Centricity in the Artificial Intelligence-Driven Society 5.0 (pp. 97-109). Springer, Cham.
16. Guangming , Cao, & Charles , B. (2021). Big Data, Marketing Analytics, and Firm Marketing Capabilities. https://www.tandfonline.com/doi/full/10.1080/08874417.2020.1842270
17. Miklosik, A., & Nina , E. (2020). Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review. https://ieeexplore.ieee.org/ielx7/6287639/8948470/09103568.pdf
18. Mohammad, A. A. S., Barghouth, M. Y., Al-Husban, N. A., Aldaihani, F. M. F., Al-Husban, D. A. A. O., Lemoun, A. A. A., & Al-Hawary, S. I. S. (2023a). Does Social Media Marketing Affect Marketing Performance. In Emerging Trends and Innovation in Business and Finance (pp. 21-34). Singapore: Springer Nature Singapore.
19. Hannah , H. C., & Anirban , M. (2023). Machine Learning and Consumer Data. https://arxiv.org/pdf/2306.14118.pdf
20. Mohammad, A. A. S., Al-Qasem, M. M., Khodeer, S. M. D. T., Aldaihani, F. M. F., Alserhan, A. F., Haija, A. A. A., & Al-Hawary, S. I. S. (2023b). Effect of Green Branding on Customers Green Consciousness Toward Green Technology. In Emerging Trends and Innovation in Business and Finance (pp. 35-48). Singapore: Springer Nature Singapore.
21. Sung, E., Bae, S., Han, D. D., & Kwon, O. (2021). Consumer engagement via interactive artificial intelligence and mixed reality. In International Journal of Information Management (Vol. 60, p. 102382). Elsevier BV. https://doi.org/10.1016/j.ijinfomgt.2021.102382
22. Journal of Marketing Analytics. (2019). In Journal of Marketing Analytics. Palgrave Macmillan. https://doi.org/10.1057/41270.2050-3326
23. Punetha, N., & Jain, G. (2023). Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews. In Applied Intelligence (Vol. 53, Issue 17, p. 20152). Springer Science+Business Media. https://doi.org/10.1007/s10489-023-04471-1
24. Widayati, C., Ali, H., Permana, D., & Riyadi, M. (2019). The Effect of Visual Merchandising, Sales Promotion and Positive Emotion of Consumers on Impulse Buying Behavior. In Journal of Marketing and Consumer Research. https://doi.org/10.7176/jmcr/60-06
25. Gupta, S., & Israni, D. (2024). Machine Learning based Customer Behavior Analysis and Segmentation for Personalized Recommendations (p. 654). https://doi.org/10.1109/icssas64001.2024.10760319
26. Ali, I., Mohammed , R., Nautiyal, Anup , & Kumar Som, B. (2024). Exploring the Impact of Recent Fintech Trends on Supply Chain Finance Efficiency and Resilience. https://doi.org/10.52783/eel.v14i1.1185
27. Namrata, Chaudhary, & Drimik, R. C. (2023). Expanding Click and Buy rates: Exploration of evaluation metrics that measure the impact of personalized recommendation engines on e-commerce platforms. https://arxiv.org/pdf/1901.08901.pdf
28. Pandey, S., Aly, M., Bagherjeiran, A., Hatch, A., Ciccolo, P., Ratnaparkhi, A., & Zinkevich, M. (2011). Learning to target (p. 1805). https://doi.org/10.1145/2063576.2063837
29. Erdem, Ş., Durmuş, B., & ÖZDEMİR, O. (2017). The Relationship with Ad Clicks and Purchase Intention: An Empiricial Study of Online Consumer Behaviour. In European Journal of Economics and Business Studies (Vol. 9, Issue 1, p. 25). https://doi.org/10.26417/ejes.v9i1.p25-33
30. Tokuç, A. A., & Dağ, T. (2024). Customer Purchase Intent Prediction using Feature Aggregation on E-Commerce Clickstream Data (p. 1). https://doi.org/10.1109/idap64064.2024.10711144
31. Gupta, S., & Maji, S. (2020). Predicting Session Length for Product Search on E-commerce Platform. https://doi.org/10.1145/3397271.3401219
32. Mere, K., Puspitasari, D., Asir, M., Rahayu, B., & Mas’ud, M. I. (2024). Peran Konten Interaktif dalam Membangun Keterlibatan Konsumen dan Memperkuat Kesetiaan Merek: Tinjauan pada Platform Media Sosial dan Situs Web Perusahaan. In Journal of Economic Bussines and Accounting (COSTING) (Vol. 7, Issue 3, p. 5455). https://doi.org/10.31539/costing.v7i3.9361
33. Nastišin, Ľ., & Fedorko, R. (2021). Metrics of Engagement on Social Networks and Their Relationship to the Customer’s Decision-Making Process Under e-Commerce Conditions. In Springer proceedings in business and economics (p. 74). Springer International Publishing. https://doi.org/10.1007/978-3-030-76520-0_8
34. Tan, K. L., Lee, C. P., & Lim, K. M. (2023). A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. In Applied Sciences (Vol. 13, Issue 7, p. 4550). Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/app13074550
35. Lim, J., & Anitsal, M. (2019). RETAIL CUSTOMER SENTIMENT ANALYSIS: CUSTOMERS’ REVIEWS OF TOP TEN U.S. RETAILERS’ PERFORMANCE. In Global Journal of Management and Marketing (Vol. 3, Issue 1, p. 124). https://doi.org/10.47177/gjmm.03.01.2019.124
36. Pritam, K. (2024). Advancements and Methodologies in Natural Language Processing and Machine Learning: A Comprehensive Review. In International Journal for Research in Applied Science and Engineering Technology (Vol. 12, Issue 4, p. 1495). International Journal for Research in Applied Science and Engineering Technology (IJRASET). https://doi.org/10.22214/ijraset.2024.63359
37. Jain, R., Singh, R., Jain, S., Ahluwalia, R., & Gupta, J. (2023). Real time sentiment analysis of natural language using multimedia input. In Multimedia Tools and Applications (Vol. 82, Issue 26, p. 41021). Springer Science+Business Media. https://doi.org/10.1007/s11042-023-15213-3
38. Monil, P. (2020). Customer Segmentation using Machine Learnin. In International Journal for Research in Applied Science and Engineering Technology (Vol. 8, Issue 6, p. 2104). International Journal for Research in Applied Science and Engineering Technology (IJRASET). https://doi.org/10.22214/ijraset.2020.6344
39. Ye, J. (2021). Analysis on E-commerce Order Cancellations Using Market Segmentation Approach (p. 33). https://doi.org/10.1145/3450588.3450596
40. Kansal, T., Bahuguna, S., Singh, V. K., & Choudhury, T. (2018). Customer Segmentation using K-means Clustering. In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) (p. 135). https://doi.org/10.1109/ctems.2018.8769171
41. Rashti, S. K. K., Davodiroknabadi, A., Zohoori, S., Nayebzadeh, S., & Ardalani, H. (2024). Modelling Brand Engagement in Social Media (Based on Sentiment Analysis and Customer Data) (Vol. 3, Issue 2, p. 196). https://doi.org/10.61838/kman.ijimob.3.2.24
42. Zhao, B. (2022). Research on Using Market Segmentation to do Recommendation in E-commerce. In Advances in economics, business and management research/Advances in Economics, Business and Management Research. Atlantis Press. https://doi.org/10.2991/aebmr.k.220307.492
43. Meire, M., Hewett, K., Ballings, M., Kumar, V., & Poel, D. V. den. (2019). The Role of Marketer-Generated Content in Customer Engagement Marketing. In Journal of Marketing (Vol. 83, Issue 6, p. 21). SAGE Publishing. https://doi.org/10.1177/0022242919873903
44. Martínez-García, M., & Hernández-Lemus, E. (2022). Data Integration Challenges for Machine Learning in Precision Medicine. In Frontiers in Medicine (Vol. 8). Frontiers Media. https://doi.org/10.3389/fmed.2021.784455
45. Kumo, W. (2023). Leveraging Consumer Behavior Research for Effective Marketing Strategies. In Advances in Business & Industrial Marketing Research (Vol. 1, Issue 3, p. 117). https://doi.org/10.60079/abim.v1i3.196
46. Santini, F. de O., Ladeira, W. J., Pinto, D. C., Herter, M. M., Sampaio, C. H., & Babin, B. J. (2020). Customer engagement in social media: a framework and meta-analysis. In Journal of the Academy of Marketing Science (Vol. 48, Issue 6, p. 1211). Springer Science+Business Media. https://doi.org/10.1007/s11747-020-00731-5
47. Majzoubi, M., & Zhao, E. Y. (2022). Going beyond optimal distinctiveness: Strategic positioning for gaining an audience composition premium. In Strategic Management Journal (Vol. 44, Issue 3, p. 737). Wiley. https://doi.org/10.1002/smj.3460
48. Bharathi, V Kalmath, Harris, S., Akshay, A.-, Shivshankarachar, Y.-, & Kruthi, V. P.-. (2024). Case Study on Transforming Financial Decision-Making with Big Data and Advanced Analytics. In International Journal For Multidisciplinary Research (Vol. 6, Issue 5). https://doi.org/10.36948/ijfmr.2024.v06i05.29218
49. So, K. K. F., Li, J., He, Y., & King, C. (2023). The Role of Customer Engagement in Sustaining Subjective Well-being After a Travel Experience: Findings From a Three-Wave Study. In Journal of Travel Research (Vol. 63, Issue 5, p. 1280). SAGE Publishing. https://doi.org/10.1177/00472875231182109.
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Copyright (c) 2025 Anber Abraheem Shlash Mohammad, Suleiman Ibrahim Shelash Mohammad, Badrea Al Oraini, Ayman Hindieh, Asokan Vasudevan, Muhammad Turki Alshurideh (Author)
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