MARKET SEGMENTATION AND TARGETING OF OFFERS USING NEURAL DISCRIMINANT ANALYSIS
DOI:
https://doi.org/10.46565/jreas.202274443-448Keywords:
Neuronal discriminant analysis;, Neuronal network;, Customer satisfaction;, Target marketing, Market segmentation;, Offer targeting;, Statistical learning;, Data analysis;Abstract
The theme of this work is "market segmentation and offer targeting using neural discriminant analysis" for classification purposes. The data provided by modern applications are usually large in size and their classification may be disturbed. In this work, we will give a contribution of data mining to market segmentation and offer targeting using neural discriminant analysis. The main interest of this work is to offer decision-makers a better vision of their customers, allowing them to better manage and satisfy them by proposing products likely to be purchased by them; to make targeted marketing or a particular offer to a group of customers with similar characteristics and consumption behaviors; to know what to offer to the new customer who comes to the company by assigning him to a group of customers in which the habits, preferences towards a product or a group of products are known. Our approach therefore predicts the class of a new customer who comes to the company; the class is predicted after the customer has made his first purchase.. Individuals are placed in the class with the highest probability. In this case, the client will be assigned to the appropriate class with the marketing services.
References
[2]. Besse, P., del Barrio, E., Gordaliza, P., Loubes, J. M., & Risser, L. (2022). A survey of bias in machine learning through the prism of statistical parity. The American Statistician, 76(2), 188-198.
[3]. Novo, O. (2018). Scalable access management in IoT using blockchain: A performance evaluation. IEEE Internet of Things Journal, 6(3), 4694-4701.
[4]. Zainal-Abidin, R. A., Harun, S., Vengatharajuloo, V., Tamizi, A. A., & Samsulrizal, N. H. (2022). Gene Co-Expression Network Tools and Databases for Crop Improvement. Plants, 11(13), 1625
[5]. Deliano, M., Tabelow, K., König, R., & Polzehl, J. (2016). Improving accuracy and temporal resolution of learning curve estimation for within-and across-session analysis. Plos one, 11(6), e0157355.
[6]. Kapucu, F. E., Välkki, I., Mikkonen, J. E., Leone, C., Lenk, K., Tanskanen, J. M., & Hyttinen, J. A. (2020). Corrigendum: Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements. Frontiers in Computational Neuroscience, 14, 586506.
[7]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2022). The role of contemporary digital tools and technologies in Covid?19 crisis: An exploratory analysis. Expert systems, 39(6), e12834.
[8]. Novakovi?, J. D., Veljovi?, A., Ili?, S. S., Papi?, Ž., & Milica, T. (2017). Evaluation of classification models in machine learning. Theory and Applications of Mathematics & Computer Science, 7(1), 39-46.
[9]. Pamuji, A. (2021). Performance of the K-Nearest Neighbors Method on Analysis of Social Media Sentiment. JUISI, 7(1), 32-37.
[10]. Piryonesi, S. M., & El-Diraby, T. E. (2020). Data analytics in asset management: Cost-effective prediction of the pavement condition index. Journal of Infrastructure Systems, 26(1), 04019036.
[11]. Sirmacek, B., & Riveiro, M. (2020). Occupancy prediction using low-cost and low-resolution heat sensors for smart offices. Sensors, 20(19), 5497.
[12]. Guo, P., Chen, S., Wang, H., Wang, Y., & Wang, J. (2021). A Systematic Analysis on the Genes and Their Interaction Underlying the Comorbidity of Alzheimer's Disease and Major Depressive Disorder. Frontiers in Aging Neuroscience, 13.
[13]. Romano, D., Stefani, G., Rocchi, B., & Fiorillo, C. (2019). The impact of assistance on poverty and food security in a fragile and protracted-crisis context: the case of West Bank and Gaza Strip. Bio-based and Applied Economics, 8(1), 21-61.
[14]. Wang, J., Pan, H., & Liu, F. (2012). Forecasting crude oil price and stock price by jump stochastic time effective neural network model. Journal of Applied Mathematics, 2012.