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International Journal of Advance Scientific Research SCIENTIFIC IDEAS. INTERNATIONAL PERSPECTIVES.
ISSN 2750-1396 12 issues per year · Monthly
Crossref DOI: 10.37547/ijasr
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Articles Published:

Machine Learning-Based Business Analytics for Strategic Marketing and Consumer Behavior Prediction

Vol. 6 No. 11 (2026): Volume 06 Issue 11 Pages 15-33
Authors
  • Dr. Lukas Schneider Schneider
    Department of Business Administration, Rhine-Westphalia Institute of Management Studies, Berlin, Germany
RESEARCH OVERVIEW

Abstract

The rapid growth of digital commerce, social media, online transactions, and customer relationship management systems has generated substantial volumes of consumer data. Traditional marketing approaches often struggle to process these data efficiently, identify complex purchasing patterns, and predict changing customer preferences. Machine learning (ML)-based business analytics offers an opportunity to improve strategic marketing by transforming historical and real-time customer data into predictive insights. This paper presents a conceptual and technical framework for integrating machine learning, business analytics, market segmentation, and consumer behavior prediction into a unified marketing decision-support system. The proposed framework consists of data acquisition, preprocessing, feature engineering, consumer segmentation, predictive modeling, model evaluation, and marketing decision optimization. Relevant literature on consumer behavior modeling, machine learning-based market segmentation, predictive analytics, and prescriptive analytics is examined to establish the theoretical foundation. Classification, regression, clustering, and ensemble learning techniques are discussed according to their suitability for different marketing objectives. The paper also proposes an evaluation methodology using accuracy, precision, recall, F1-score, mean absolute error, and business-oriented indicators. A conceptual comparison of analytical approaches illustrates how predictive and prescriptive capabilities can support customer targeting, campaign personalization, customer retention, and resource allocation. Particular attention is given to data privacy, algorithmic fairness, interpretability, and organizational implementation challenges. Since the paper does not use a collected experimental dataset, the proposed framework and comparative discussion should be interpreted as a research design rather than as validated empirical findings. The study provides a structured foundation for future empirical investigations into intelligent marketing systems and data-driven business decision-making.

Keywords

  • Machine Learning
  • Business Analytics
  • Strategic Marketing
  • Consumer Behavior Prediction
  • Market Segmentation
SOURCES & CITATIONS

References

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3. Bertsimas, D., & Kallus, N. ( 2020 ). From predictive to prescriptive analytics. Management Science, 66 ( 3 ), 1025–1044.
4. Bhatt Mishra, D., Naqvi, S., Gunasekaran, A., & Dutta, V. ( 2023 ). Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges. Annals of Operations Research, 1–40.
5. Deshpande, P. S., Sharma, S. C., Peddoju, S. K., Deshpande, P. S., Sharma, S. C., & Peddoju, S. K. ( 2019 ). Predictive and prescriptive analytics in big-data era. Security and data storage aspect in cloud computing, 71–81.
6. Foxall, G., Oliveira-Castro, J., James, V. K., Yani-De-Soriano, M. M., Sigurdsson, V. : Consumer Behavior Analysis and Social Marketing: The Case of Environmental Conservation. Behavior and Social Issues, vol. 15, pp. 101–125 ( 2006 ).
7. Frazzetto, D., Nielsen, T. D., Pedersen, T. B., & Šikšnys, L. ( 2019 ). Prescriptive analytics: a survey of emerging trends and technologies. The VLDB Journal, 28, 575–595.
8. Jiang, Y. : Prediction of Consumer Behavior Based on Machine Learning Algorithm. Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China.
9. Kalpana Anshu, S. K. Singh, R. Kumari.: A Machine Learning Model for Effective Consumer Behaviour Prediction. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), pp. 1–5 ( 2021 ).
10. Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. ( 2020 ). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70.
11. Lin, J., Holland, C. P., Argyris, N., Prinz, A., Hengesbach, C. : A Machine Learning Approach to Online Market Segmentation. SSRN Electronic Journal, pp. 1–18 ( 2021 ).
12. Lipstein, B. : A Mathematical Model of Consumer Behavior. Journal of Marketing Research, vol. 2, pp. 259–265 ( 1965 ).
13. Mathur, N., Kumar, S., Joshi, T., Dhuliya, P. : Analyzing Consumer Behavior Predictions: A Review of Machine Learning Techniques. 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM), pp. 1–5 ( 2022 ).
14. Prakash Assistant, Sunitha Devi, S. Malli Babu, Kumbala Pradeep Reddy, P. Pavan Kumar, Mankala Satish.: Predicting Consumer Behaviour with Artificial Intelligence. 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), pp. 698–703 ( 2023 ).
15. Prakash, S. Malli Babu, P. Pavan Kumar, Sunitha Devi, Kumbala Pradeep Reddy, Mankala Satish.: Predicting Consumer Behaviour with Artificial Intelligence. 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), pp. 687–692 ( 2023 ).
16. Regmi, S. R., Meena, J., Kanojia, U., Kant, V. : Customer Market Segmentation using Machine Learning Algorithm. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1348–1354 ( 2022 ).
17. Regmi, S. R., Meena, J., Kanojia, U., Kant, V. : Customer Market Segmentation using Machine Learning Algorithm. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1348–1354 ( 2022 ).
18. Soltanpoor, R., & Sellis, T. ( 2016 ). Prescriptive analytics for big data. In Proc. of Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, Sep. 28-29, 2016, 27 (pp. 245–256 ). Springer International Publishing.
19. Tesic, D., Bogetić, Z. : Models of Consumer Behavior: A Literature Review. ZBORNIK RADOVA EKONOMSKOG FAKULTETA BRČKO, pp. 21–38 ( 2022 ).
20. Yao, S. : Method and Research of E-commerce Consumers' Purchasing Behavior Forecast and Analysis Based on Machine Learning. 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture, pp. 1–8 ( 2021 ).
21. Zhao, J., Xue, F., Khan, S., Khatib, S. F. A. : Consumer Behaviour Analysis for Business Development. Aggression and Violent Behavior, vol. 101591, pp. 1–12 ( 2021 ).
22. Anshu, K., Singh, S. K., Kumari, R. : A Machine Learning Model for Effective Consumer Behaviour Prediction. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), pp. 1–5 ( 2021 ).
23. Attaran, M., & Attaran, S. ( 2019 ). Opportunities and challenges of implementing predictive analytics for competitive advantage. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings, 64–90.
24. Bertsimas, D., & Kallus, N. ( 2020 ). From predictive to prescriptive analytics. Management Science, 66 ( 3 ), 1025–1044.
25. Bhatt Mishra, D., Naqvi, S., Gunasekaran, A., & Dutta, V. ( 2023 ). Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges. Annals of Operations Research, 1–40.
26. Deshpande, P. S., Sharma, S. C., Peddoju, S. K., Deshpande, P. S., Sharma, S. C., & Peddoju, S. K. ( 2019 ). Predictive and prescriptive analytics in big-data era. Security and data storage aspect in cloud computing, 71–81.
27. Foxall, G., Oliveira-Castro, J., James, V. K., Yani-De-Soriano, M. M., Sigurdsson, V. : Consumer Behavior Analysis and Social Marketing: The Case of Environmental Conservation. Behavior and Social Issues, vol. 15, pp. 101–125 ( 2006 ).
28. Frazzetto, D., Nielsen, T. D., Pedersen, T. B., & Šikšnys, L. ( 2019 ). Prescriptive analytics: a survey of emerging trends and technologies. The VLDB Journal, 28, 575–595.
29. Jiang, Y. : Prediction of Consumer Behavior Based on Machine Learning Algorithm. Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China.
30. Kalpana Anshu, S. K. Singh, R. Kumari.: A Machine Learning Model for Effective Consumer Behaviour Prediction. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), pp. 1–5 ( 2021 ).
31. Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. ( 2020 ). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70.
32. Lin, J., Holland, C. P., Argyris, N., Prinz, A., Hengesbach, C. : A Machine Learning Approach to Online Market Segmentation. SSRN Electronic Journal, pp. 1–18 ( 2021 ).
33. Lipstein, B. : A Mathematical Model of Consumer Behavior. Journal of Marketing Research, vol. 2, pp. 259–265 ( 1965 ).
34. Mathur, N., Kumar, S., Joshi, T., Dhuliya, P. : Analyzing Consumer Behavior Predictions: A Review of Machine Learning Techniques. 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM), pp. 1–5 ( 2022 ).
35. Prakash Assistant, Sunitha Devi, S. Malli Babu, Kumbala Pradeep Reddy, P. Pavan Kumar, Mankala Satish.: Predicting Consumer Behaviour with Artificial Intelligence. 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), pp. 698–703 ( 2023 ).
36. Prakash, S. Malli Babu, P. Pavan Kumar, Sunitha Devi, Kumbala Pradeep Reddy, Mankala Satish.: Predicting Consumer Behaviour with Artificial Intelligence. 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), pp. 687–692 ( 2023 ).
37. Regmi, S. R., Meena, J., Kanojia, U., Kant, V. : Customer Market Segmentation using Machine Learning Algorithm. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1348–1354 ( 2022 ).
38. Regmi, S. R., Meena, J., Kanojia, U., Kant, V. : Customer Market Segmentation using Machine Learning Algorithm. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1348–1354 ( 2022 ).
39. Soltanpoor, R., & Sellis, T. ( 2016 ). Prescriptive analytics for big data. In Proc. of Databases Theory and Applications: 27th Australasian Database Conference, ADC 2016, Sydney, NSW, Sep. 28-29, 2016, 27 (pp. 245–256 ). Springer International Publishing.
40. Tesic, D., Bogetić, Z. : Models of Consumer Behavior: A Literature Review. ZBORNIK RADOVA EKONOMSKOG FAKULTETA BRČKO, pp. 21–38 ( 2022 ).
41. Yao, S. : Method and Research of E-commerce Consumers' Purchasing Behavior Forecast and Analysis Based on Machine Learning. 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture, pp. 1–8 ( 2021 ).
42. Zhao, J., Xue, F., Khan, S., Khatib, S. F. A. : Consumer Behaviour Analysis for Business Development. Aggression and Violent Behavior, vol. 101591, pp. 1–12 ( 2021 ).
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