CREATION OF AI-BASED CUSTOMER BEHAVIOR ANALYTICS MODELS TO HELP BUSINESSES IMPROVE MARKET FORECASTING AND PERSONALIZED SERVICES

• Maria Gonzalez

  Department of Information Systems, National Autonomous University of Mexico, Mexico

• Sofia Morales

  Department of Information Systems, National Autonomous University of Mexico, Mexico

• Javier Torres

  Department of Information Systems, National Autonomous University of Mexico, Mexico

Objective: Understanding customer behavior is fundamental to effective market forecasting and personalized service delivery. This paper presents AI-based customer behavior analytics models that leverage deep learning and clustering techniques to segment customers, predict purchasing patterns, and enable personalized marketing strategies. Method: Our multi-layered approach integrates collaborative filtering, sentiment analysis, and sequential pattern mining to create comprehensive customer profiles. Results: Validation using e-commerce datasets shows prediction accuracy improvements of 31% over traditional methods, with personalized recommendations achieving a 24% increase in conversion rates. Novelty: The research contributes to customer relationship management theory and provides actionable insights for businesses seeking to enhance customer engagement through data-driven personalization.

[1] S. Begum, “AI at Scale: Predictive Analytics as a Strategic Engine for National Competitiveness in U.S. Startup and Small Business Financing,” Int. J. Res. Publ. Rev., vol. 5, no. 12, pp. 6129–6137, 2024, doi: 10.55248/gengpi.6.1025.3664.

[2] S. Begum, “Optimizing Capital Deployment in Post-Pandemic America: AI-Powered Predictive Analytics for Startup Resilience and Growth,” Int. J. Comput. Appl. Technol. Res., vol. 11, no. 12, pp. 700–710, 2022, doi: 10.7753/IJCATR1112.1030.

[3] K. P. Mishu, M. T. Ahmed, M. M. U. A. M. S. Billah, M. D. H. Gazi, S. Begum, and M. M. Hasan, “AI-Driven Supply Chain Management in the United States: Machine Learning for Predictive Analytics and Business Decision-Making,” Cuest. Fisioter., vol. 53, no. 3, pp. 5755–5768, 2024, doi: 10.48047/s7cc5r20.

[4] M. I. Jobiullah, S. Begum, J. Sarwar, V. Kumar, and A. B. Gupta, “Reimagining U.S. Cyber Defense Through Intelligent Automation,” Int. J. Sci. Res. Mod. Technol., vol. 3, no. 12, 2024, doi: 10.38124/ijsrmt.v3i12.1196.

[5] S. Begum, “Artificial Intelligence and Economic Resilience: A Review of Predictive Financial Modelling for Post-Pandemic Recovery in the United States SME Sector,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 7, 2025, doi: 10.38124/ijisrt/25jul1726.

[6] S. Begum et al., “Robotic AI Systems for Fake News Detection in IoT-Connected Social Media Platforms Using Sensor-Driven Cross-Verification,” J. Posthumanism, vol. 5, no. 11, pp. 391–405, 2025, doi: 10.63332/joph.v5i11.3688.

[7] A. R. Talukder, F. Shahrear, S. Begum, and M. I. Jobiullah, “Underwater Image Enhancement and Restoration with YOLO-Based Object Detection and Recognition,” Well Test. J., vol. 34, no. S3, pp. 727–748, 2025.

[8] E. W. Ngai, L. Xiu, and D. C. Chau, “Application of Data Mining Techniques in Customer Relationship Management,” Expert Syst. Appl., vol. 36, no. 2, pp. 2592–2602, 2009.

[9] P. C. Verhoef, W. J. Reinartz, and M. Krafft, “Customer Engagement as a New Perspective in Customer Management,” J. Serv. Res., vol. 13, no. 3, pp. 247–252, 2010.

[10] V. Kumar and W. Reinartz, “Creating Enduring Customer Value,” J. Mark., vol. 80, no. 6, pp. 36–68, 2018.

[11] K. N. Lemon and P. C. Verhoef, “Understanding Customer Experience Throughout the Customer Journey,” J. Mark., vol. 80, no. 6, pp. 69–96, 2016.

[12] R. L. Harrison and T. M. Reilly, “Mixed Methods Designs in Marketing Research,” Qual. Mark. Res. An Int. J., vol. 14, no. 1, pp. 7–26, 2011, doi: 10.1108/13522751111099300.

[13] M. Yrjölä, H. Saarijärvi, H. Nummela, and H. Karjaluoto, “Understanding Omni-Channel Shopping Value: A Mixed-Method Study,” J. Retail. Consum. Serv., vol. 39, pp. 314–330, 2017, doi: 10.1016/j.jretconser.2017.08.011.

[14] Yusnidar, D. Yudhakusuma, and F. Sari, “Personalized Marketing Strategy in Digital Business Using Data Mining Approach,” Int. J. Softw. Eng. Comput. Sci., vol. 3, no. 2, pp. 137–143, 2023, doi: 10.35870/ijsecs.v3i2.1515.

[15] A. F. Sayuti, K. Iqbal, and Z. Ali, “The Influence of Personalized Marketing on Customer Engagement and Customer Lifetime Value,” J. Markcount Financ., vol. 3, no. 1, 2024, doi: 10.70177/jmf.v3i1.2064.

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