The swift progression of digital technology has allowed organisations to amass extensive customer data, fostering new avenues for data-informed decision-making. Artificial Intelligence (AI) and Machine Learning (ML) have surfaced as potent methodologies for examining consumer behaviour, forecasting forthcoming trends, and enhancing organisational performance via predictive analytics. Nonetheless, numerous organisations continue to have difficulties in converting intricate and high-dimensional data into practical insights through traditional analytical methods. This research introduces a predictive analytics framework utilising AI and ML for the analysis of customer behaviour and the assessment of business performance. The structure encompasses data gathering, preprocessing, feature extraction, model creation, validation, and performance evaluation. A variety of machine learning algorithms, such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks, are utilised to assess predicted performance. Model efficacy is evaluated by metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrix assessment, whilst feature significance analysis determines the elements that substantially affect consumer behaviour. The suggested approach illustrates the capability of AI-enhanced predictive models to enhance consumer segmentation, forecast behaviour, and inform critical business decisions. Ensemble learning and neural network architectures provide robust forecasting capabilities by adeptly identifying intricate linkages in consumer data. The results underscore the significance of consumer demographics, buying history, interaction trends, and transaction frequency in improving predictive precision and business results.This study presents a cohesive predictive analytics architecture that facilitates astute decision-making and enhances corporate efficiency. The suggested methodology provides actionable insights for entities aiming to utilise AI and ML for customer analysis, operational productivity, client retention, and sustainable business expansion.

References:

1.Bianchi, G. (2024). Advancing business intelligence with big data and AI: The convergence of customer insights and predictive analytics for competitive advantage. Journal of AI-Assisted Scientific Discovery, 4(1).

2.Rosário, A. T., Cruz, R., Moniz, L. B., & Casaca, J. A. (2024). Predictive analytics for customer behavior. In A. T. Rosário, R. Cruz, & L. B. Moniz (Eds.), Data-Driven Marketing for Strategic Success (pp. 37–72). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-3455

3.ch002

4.Dias, F. S., & Lauretta, G. A. (2024). The transformative impact of AI and deep learning in business: A literature review. arXiv. https://doi.org/10.48550/arXiv.2410.23443

5.Abbasi, M., Nishat, R. I., Bond, C., Graham-Knight, J. B., Lasserre, P., Lucet, Y., &Najjaran, H. (2024). A review of AI and machine learning contribution in business process management (process enhancement and process improvement approaches).Business Process Management Journal, 31(4), 1414–1452. https://doi.org/10.1108/BPMJ-07-2024- 0555

6.Neware, A., Dubey, A., & Tiwari, A. (2025). Predicting customer behavior using machine learning and deep learning: A comprehensive review.Journal of Android and IOS Applications and Testing, 10(3), 44–54.

7.Chen, S., Xu, Z., Xu, D., & Gou, X. (2024). Customer purchase prediction in B2C e-business: A systematic review and future research agenda. Expert Systems with Applications, 252(Part B), Article 124261. https://doi.org/10.1016/j.eswa.2024.124261

8.Xu, Y., Ma, Y., Hu, R., & Wang, H. (2024). Predictive analytics techniques in consumer behaviour: A literature review. Advances in Economics, Management and Political Sciences, 97, 20 31. https://doi.org/10.54254/2754-1169/97/20231516

9.Bujor, D., &Ene Constantin, A. B. (2025). AI-driven predictive customer analytics for forecasting behavior, churn and future buying patterns. Proceedings of the International Conference on Business Excellence, 19(1), 981–994. https://doi.org/10.2478/picbe-2025-0077

10.Madanchian, M. (2024). Generative AI for consumer behavior prediction: Techniques and applications. Sustainability, 16(22), 9963. https://doi.org/10.3390/su16229963

11.Dr. J. Anvar Shathiket al. ”Deep Ensemble Learning For Accurate Prediction Of Neurodegenerative Disorders Using Temporal Clinical Data . (2025). International Journal of Environmental Sciences, 11(4s), 1246-1253. https://doi.org/10.64252/8dbg7v71

12.Anvar ShathikJ, B. R. Chandra, M. D. Nandeesh, T. C. Maniunath, S. Pothala and N. R. Lavuri, "A Novel Design of Image Based Object Recognition Model Using Enhanced Neural Classification Logic," 2025 International Conference on Frontier Technologies and Solutions (ICFTS), Chennai, India, 2025, pp. 1-8, doi: 10.1109/ICFTS62006.2025.11031570.

13.R. R, J. Anvar. Shathik, J. Sivakumar, P. Manothini, G. S. J. Asha and M. Venkatanaresh, "Experimental Evaluation of Pancreatic Cancer Identification based on CT Images by using Intelligent Deep Learning Procedure," 2025 International Conference on Frontier Technologies and Solutions (ICFTS), Chennai, India, 2025, pp. 1-9, doi: 10.1109/ICFTS62006.2025.1103180

14.J. Anvar Shathik, S. Hashini, A. Pandiaraj, N. Ramshankar, N. G and A. K B, "Identification of Different Medicinal Plants Through Image Processing," 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG), Chennai - 600077, Tamil Nadu,India, 2024,pp.1-5,doi: 10.1109/ICSTSDG61998.2024.11026565.

15.Raju, K., Ramshankar, N., Anvar Shathik J et al. Blockchain Assisted Cloud Security and Privacy Preservation using Hybridized Encryption and Deep Learning Mechanism in IoT-Healthcare Application. J Grid Computing 21, 45 (2023). https://doi.org/10.1007/s10723-023-09678-7

16.Madhavikatamaneni, R. K. S, Anvar Shathik J and K. PoornaPushkala, "A Healthcare System for detecting Stress from ECG signals and improving the human emotional," 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), Coimbatore, India, 2022, pp. 1-8, doi: 10.1109/ICACTA54488.2022.9753564.

17.Vijaya Vardan Reddy S P; Armstrong Joseph J; Priscilla M; Anvar Shathik J; R.Thandaiah Prabu, "HDP-IoT: An IoT Framework for Cardiac Status Prediction System using Machine Learning," 2022International Conference on Inventive Computation Technologies (ICICT), Nepal,2022, pp. 855-861,doi: 10.1109/ICICT54344.2022.9850897.

18.K. Vikranth, Anvar Shathik J, and K. Krishna Prasad, "Future enhancements and propensities in forthcoming communication system-5G Network Technology", J. Phys, pp. 12006, 2020

19.Yarramsetti, S., Anvar Shathik J, & Renisha, P. S. (2021). Intelligent estimation of social media sentimental features using Deep Learning with Natural Language Processing Strategies. International Journal of Innovative Technology and Exploring Engineering, 10(6), 74-

20.Ramshankar, N., AnvarShathik, J.., Raju, K. et al. Integrated deep learning and blockchain-based framework for cloud manufacturing with improved customer satisfaction. Knowl Inf Syst 67, 5301 5334 (2025). https://doi.org/10.1007/s10115-025-02373-x

21.Mr.G. Silambarasan ,J.AnvarShathik (2017) “Ensemble text classifier: a document classification technique to predict and categorizes regularised and novel classes using incremental learning “ , International Journal of Applied Engineering Research , 2017, Volume 12, issue -22 , Pages12454 12459

22.Mr.G. Silambarasan , J. Anvar Shathik (2019) “ Automated Ensemble Framework for Integration of Ontology Based Large Scale Semantic Knowledge Base “ Journal of Engineering and Applied Sciences 14(2)