Predictive AI Proactive Customer Engagement Platform and Real-Time Friction Reduction Using AI-Based Churn Prediction
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P122Keywords:
Predictive AI, Customer Engagement, Churn Prediction, Real-Time Analytics, Machine Learning, Friction Reduction, Personalization, Big Data, Cloud Computing, Customer RetentionAbstract
The customer engagement platforms have developed dramatically, and the artificial intelligence (AI) has taken its place, allowing the organizations to leave the reactive service-focused model of customer engagement and implement the proactive, predictive, and individual approach. Customer churn has been among the most significant issues in the context of digital markets that are highly competitive concerning the stability of the revenues and the long-term growth. Conventional engagement systems are not very successful in early detection of customer dissatisfaction and resolve into subsequent interventions and churn rates. The given paper provides a detailed outline of a Predictive AI-powered Proactive Customer Engagement Platform incorporating the elements of real-time analytics, machine-based churn prediction, and friction reduction systems. The proposed architecture is based on massive data of customer interaction, such as behavioral, transactional, and contextual data, to create predictive models that can detect the churn probability with a high level of precision. The system helps to determine the patterns of disengagement at an early stage by introducing both supervised and unsupervised methods of learning like gradient boosting, deep neural networks and clustering algorithms. In addition, the platform presents a real-time engine of reducing friction, which identifies bottlenecks of the customer journey dynamically (delays, service errors, or usability problems) and manages these cases with the help of automated operations. One of the most significant contributions of the work is the combination of predictive analytics and real-time orchestration systems which can enable organizations to support them with a personalized approach to engagement based on various channels, e.g. mobile applications, web platforms, and customer support systems. Reinforcement learning is also exhibited by the platform to constantly fine-tune engagement strategies on customer feedback loop and response. The methodology encompasses data ingest pipelines, feature engineering systems, model training processes, and deployment plans based on cloud-native systems. The system is being measured with the help of performance metrics which include accuracy, precision, recall, F1-score and customer retention improvement rates. The available experimental data indicate that predictive AI models can help to lower the rate of churn significantly to increase customer satisfaction and optimization of operational processes. The work is also beneficial to the expanding literature on AI-based customer engagement because the proposed study offers a scalable, real-time, and intelligent platform structure. It emphasizes the need to adopt proactive engagement strategies and provides real-life insights into the business that intends to improve customer experience by state of the art analytics and automation.
References
[1] Sethuraman, P., & Chennareddy, R. K. (2022). Machine Learning Assisted Design of Wireless Access Systems for Reliable and Low-Latency Financial and Smart Commerce Services. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 133-142.
[2] Chennareddy, R. K. (2023). Enterprise-Scale AI and Analytics Strategy for End-to-End Business Transformation across Global Organizations. International Journal of AI, BigData, Computational and Management Studies, 4(3), 134-145.
[3] Sethuraman, P. (2022). Latency-Aware Scheduling and Resource Control Algorithms for Emergency and Public Safety Wireless Networks. International Journal of Emerging Research in Engineering and Technology, 3(4), 133-140.
[4] Chennareddy, R. K., & Sethuraman, P. (2023). Enterprise and RAN-Aware Data and Analytics Platforms for Mission-Critical and Low-Latency Digital Services. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 184-192.
[5] Chennareddy, R. K. (2020). Engineering Intelligence Systems Using Big Data and Cloud Architectures for Modern Data Intensive Applications. International Journal of AI, BigData, Computational and Management Studies, 1(2), 41-50.
[6] Chennareddy, R. K. (2021). Designing Data and Analytics Ecosystems for High Volume Transaction Processing Applications. International Journal of AI, BigData, Computational and Management Studies, 2(2), 95-106.
[7] Sethuraman, P., & Chennareddy, R. K. (2022). Intelligent Vehicular Traffic Flow Prediction Using Learning-Based Spatio-Temporal Models for Data-Driven Wireless Transportation and Urban Analytics Systems. International Journal of Emerging Trends in Computer Science and Information Technology, 3(2), 111-121.
[8] Sethuraman, P., & Chennareddy, R. K. (2023). System-Level Design and Orchestration of Large-Scale Cellular Access Networks for Regulatory-Compliant Financial Services. International Journal of Emerging Research in Engineering and Technology, 4(3), 140-150.
[9] Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert systems with applications, 38(3), 2354-2364.
[10] Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1), 20-29.
[11] Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European journal of operational research, 218(1), 211-229.
[12] Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2, pp. 1-800). Cambridge: MIT press.
[13] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
[14] Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
[15] Aggarwal, C. C. (2015). Data mining: the textbook (Vol. 1, No. 3). New York: springer.
[16] Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., ... & Stoica, I. (2016). Apache spark: a unified engine for big data processing. Communications of the ACM, 59(11), 56-65.
[17] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
[18] Marutschke, D., Gournelos, T., & Ray, S. (2019). Understanding fluency and friction in customer experience management. In Predicting trends and building strategies for consumer engagement in retail environments (pp. 88-108). IGI Global.
[19] Khodabandehlou, S., & Zivari Rahman, M. (2017). Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. Journal of Systems and Information Technology, 19(1-2), 65-93.
[20] Ehikioya, S. A., & Zeng, J. (2021). Mining web content usage patterns of electronic commerce transactions for enhanced customer services. Engineering Reports, 3(11), e12411.
[21] Zou, L., Xia, L., Ding, Z., Song, J., Liu, W., & Yin, D. (2019, July). Reinforcement learning to optimize long-term user engagement in recommender systems. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2810-2818).
