Optimal control of customer dynamics using machine learning method with polynomial kernel
Subject Areas :seyed Hamid Emadi 1 , Abolfazl Sadeghian 2 , مژده ربانی 3 , Hasan Dehghan Dehnavi 4
1 - Department of Management, Yazd Branch, Islamic Azad University
2 - Assistant professor, Department of Management, Yazd Branch, Islamic Azad University, Yazd, Iran
3 - استاد دانشگاه آزاد واحد یزد
4 - Associate professor, Department of Management, Yazd Branch, Islamic Azad University, Yazd, Iran
Keywords: optimal control, customer dynamics, machine learning, polynomial kernel,
Abstract :
In this research, a model of optimal control for customer dynamics based on marketing policies is investigated as a non-automatic system of differential equations. The main purpose of the model is to track and analyze the simultaneous changes in the behavior of regular, referral and potential customers of the company from the time of its inception to now. Implementing an effective marketing policy to optimize these changes and increase the number of customers is of particular importance. In line with this goal, a new supervised machine learning algorithm is presented for the numerical simulation of the problem. The proposed algorithm uses polynomial kernels. Polynomial kernels make it possible to simulate a complex function of data in a way that helps to better understand customer dynamics. Support Vector Least Squares regression provides a simple optimization method for marketing strategies, with this approach, marketing strategies can be optimized without dealing with the details of each customer and instead focusing on the overall effect of this strategy. placed on the set of customers. This research shows how machine learning techniques can help in solving complex management and marketing problems. Over time, the number of regular customers increases and the number of potential customers decreases. However, the number of referral customers shows a rapid growth at the beginning of the time period and a fluctuating increasing pattern over time.
Bakshizadeh, Nastaran, Azimi, Parham. (2019). Optimization of a supply chain network using the simulation technique and Harmony Search algorithm. Industrial Management Studies, 17(54), 67-109. https://doi.org/10.22054/jims.2019.2247.1069.
Berman, B. (2016). Referral marketing: Harnessing the power of your customers. Business Horizons, 59(1), 19-28.https://doi.org/10.1016/j.bushor.2015.08.001.
Castillo, A., Benitez, J., Llorens, J., Luo, X. R. (2021). Social media-driven customer engagement and movie performance: Theory and empirical evidence. Decision Support Systems, 145:113516. https://doi.org/10.1016/j.dss.2021.113516.
Foroudi, P., Gupta, S., Sivarajah, U., & Broderick, A. (2018). Investigating the effects of smart technology on customer dynamics and customer experience. Computers in Human Behavior, 80, 271-282. https://doi.org/10.1016/j.chb.2017.11.014.
Islam, S., Amin, S. H., & Wardley, L. J. (2024). A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques. Expert Systems with Applications, 235, 121121. https://doi.org/10.1016/j.eswa.2023.121121.
Ledro, C., Nosella, A., & Dalla Pozza, I. (2023). Integration of AI in CRM: Challenges and guidelines. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100151. https://doi.org/10.1016/j.joitmc.2023.100151.
Li, X., Zhuang, Y., Lu, B., & Chen, G. (2019). A multi-stage hidden Markov model of customer repurchase motivation in online shopping. Decision Support Systems, 120, 72-80. https://doi.org/10.1016/j.dss.2021.113516.
Lyutov, A., Uygun, Y., & Hütt, M. T. (2019). Managing workflow of customer requirements using machine learning. Computers in Industry, 109, 215-225. https://doi.org/10.1016/j.compind.2019.04.010.
Mehrkanoon, S., & Suykens, J. A. (2015). Learning solutions to partial differential equations using LS-SVM. Neurocomputing, 159, 105-116. https://doi.org/10.1016/j.neucom.2015.02.013.
Mehrkanoon, S., Falck, T., & Suykens, J. A. (2012). Approximate solutions to ordinary differential equations using least squares support vector machines. IEEE transactions on neural networks and learning systems, 23(9), 1356-1367. https://doi.org/10.1109/TNNLS.2012.2202126.
Mosaddegh, A., Albadvi, A., Sepehri, M. M., & Teimourpour, B. (2021). Dynamics of customer segments: A predictor of customer lifetime value. Expert Systems with Applications, 172, 114606. https://doi.org/10.1016/j.eswa.2021.114606.
Ortakci, Y., & Seker, H. (2024). Optimising customer retention: An AI-driven personalised pricing approach. Computers & Industrial Engineering, 188, 109920. https://doi.org/10.1016/j.cie.2024.109920.
Pakniyat, A., Parand, K., & Jani, M. (2021). Least squares support vector regression for differential equations on unbounded domains. Chaos, Solitons & Fractals, 151, 111232. https://doi.org/10.1016/j.chaos.2021.111232.
Parand, K., Hasani, M., Jani, M., Yari, H. (2021). Numerical simulation of Volterra–Fredholm integral equations using least squares support vector regression. Computational and Applied Mathematics; 40:1-5. https://doi.org/10.1007/s40314-021-01471-0