Presenting a Demand Model for Product Development Customization with a Marketing 5.0 Approach in the Business Sector Using Deep Learning Algorithms

Document Type : Original Article

Authors

1 PhD Student, Department of Industrial Engineering, No.C., Islamic Azad University, Noor, Iran, Email: safarastegarmit@gmail.com

2 Corresponding author, Assistant Professor, Department of Industrial Engineering, QaS.C., Islamic Azad University, Qaemshahr, Iran, Email: ma.sadeghpour@iau.ac.ir

3 Assistant Professor, Department of Computer Engineering, No.C., Islamic Azad University, Noor, Iran. Email: kourosh.nemati@iau.ac.ir

4 Assistant Professor, Department of Industrial Engineering, No.C., Islamic Azad University, Noor, Iran. Email: fa.harsej@iau.ac.ir

Abstract

In the present research, a demand model for product development customization with a Marketing 5.0 approach is presented. In this regard, deep learning algorithms such as Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) were used. Four features firm size, firm revenue, economic growth, and firm electricity consumption cost were considered as factors influencing demand. The results of implementing the deep learning algorithms showed that the Long Short-Term Memory neural network, with 95%, the Recurrent Neural Network with 94%, and the Deep Neural Network with 93% had the highest accuracy in demand forecasting. Furthermore, the firm's electricity cost had the most significant impact on the presented model, followed by firm revenue. The third rank belongs to firm size, and economic growth holds the final rank in terms of electricity demand in the industrial and business sectors.

Keywords

Main Subjects

References

Akter, S., Talukder, M. A. F., Ashrafi, T., & Ahmed, F. (2024). The effects of artificial intelligence and machine learning in consumer behavior, demand prediction, and chatbots in the realm of digital marketing. Business Forecasting and Marketing Intelligence, 1-25.
Bahadori Jahromi, H. (2026). From Analytics to Agility: How Smart Contract Capability and Supply Chain Digitalization Strengthen Supply Chain Performance in Financial Services. Journal of Data Analytics and Intelligent Decision-making, 2(3), 29-54. doi: 10.22091/jdaid.2026.16727.1094
Bauzon, S. K. B., Hollman, G. B., Bague, L. R., Sevilla, R. V., Reyes, R. C., & Amado, T. M. (2025, May). Deep learning-based electrical load forecasting for optimized energy demand prediction in the Luzon Grid, Philippines. In 2025 10th International Conference on Business and Industrial Research (ICBIR) (pp. 166-171). IEEE. https://doi.org/10.1109/ICBIR65229.2025.11163113
Falatouri, T., Darbanian, F., Brandtner, P., & Udokwu, C. (2022). Predictive analytics for demand forecasting–A comparison of SARIMA and LSTM in retail SCM. Procedia Computer Science, 200, 993-1003.
Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119-142.
Ghimire, S., AL-Musaylh, M. S., Nguyen-Huy, T., Deo, R. C., Acharya, R., Casillas-Perez, D., ... & Salcedo-Sanz, S. (2025). Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts. Applied Energy, 378, 124763.
Gooljar, V., Issa, T., Hardin-Ramanan, S., & Abu-Salih, B. (2024). Sentiment-based predictive models for online purchases in the era of marketing 5.0: A systematic review. Journal of Big Data, 11(1), 107.
Goyal, R., Reiche, C., Fernando, C., & Cohen, A. (2021). Advanced air mobility: Demand analysis and market potential of the airport shuttle and air taxi markets. Sustainability, 13(13), 7421. https://doi.org/10.3390/su13137421
Hasani, M., Deiranlou, M., & Karimi, H. (2025). Designing an Efficient Bike-Sharing System for the University of Bojnord. Journal of Data Analytics and Intelligent Decision-making, 1(2), 79-89. doi: 10.22091/jdaid.2025.14320.1014
He, M., Wang, H., & Thwin, M. (2025). A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model. Scientific Reports, 15(1), 3353.
Jafari, H., Etebari, A., & Jafari, M. (2025). Proposing a Developed Gram–Schmidt Algorithm to Construct Orthogonal Unit Vectors (A Mathematical and Practical Approach). Journal of Data Analytics and Intelligent Decision-making, 1(2), 61-78. doi: 10.22091/jdaid.2025.14004.1001
Jamshidi Gahrouei, A., & Asgharian, R. (2026). AI-Driven Protection Schemes for Modern Power Grids: Technologies, Challenges, and Opportunities. Journal of Data Analytics and Intelligent Decision-making, 2(1), 17-30. doi: 10.22091/jdaid.2026.15133.1032
Jha, B. K., & Pande, S. (2021, April). Time series forecasting model for supermarket sales using FB-prophet. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 547-554). IEEE.
Joshan, A. (2025). Towards a Human-Centric Industry 5.0 Enabled by the Convergence of Artificial Intelligence, Internet of Things, and Blockchain. Journal of Data Analytics and Intelligent Decision-making, 1(4), 32-49. doi: 10.22091/jdaid.2026.14749.1025
Joshan, A. (2026). Analysis of Emerging Approaches in Cybersecurity for Internet of Things (IoT) Networks with an Emphasis on Smart Urban and Domestic Infrastructures. Journal of Data Analytics and Intelligent Decision-making, 2(1), 1-16. doi: 10.22091/jdaid.2026.15424.1036
Kiani, M. S. (2026). Sports Law and Coaches' Responsibility: Challenges and New Approaches in Iran. Journal of Data Analytics and Intelligent Decision-making, 2(2), 1-9. doi: 10.22091/jdaid.2026.15415.1035
Kumar, G., Jain, S., & Singh, U. P. (2021). Stock market forecasting using computational intelligence: A survey. Archives of Computational Methods in Engineering, 28(3), 1069-1101. https://doi.org/10.1007/s11831-020-09413-5
Mansoori, A. (2025). The Application of Artificial Intelligence in Human Resource Performance Appraisal: A Conceptual Framework for Responsible Implementation. Journal of Data Analytics and Intelligent Decision-making, 1(3), 13-30. doi: 10.22091/jdaid.2025.14114.1009
Marjani, M. R., Jabbari, M., & Aghaee, M. (2026). A Hybrid Approach Based on Data Density Analysis for Group Supplier Selection Problem Considering Multi-Level Risk Assessment: A Case Study. Journal of Data Analytics and Intelligent Decision-making, 2(3), 1-28. doi: 10.22091/jdaid.2026.16147.1066
Molani Aghdam, M., & Ardeshir Larijani, I. (2026). Artificial neural network evaluating nonlinear evolutionary effect of cognitive and risk complexity in Ambidextrous strategy renewal. Journal of Data Analytics and Intelligent Decision-making, 2(2), 10-30. doi: 10.22091/jdaid.2026.15918.1050
Parizad, B., Jamali, A., & Khayyam, H. (2025). Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation. Energy and AI, 100584.
Riki, M., Mohammadi, F., & Eslami, E. (2025). Video Coding Machine Architecture for Smart Urban Traffic Optimization with Deep Learning. Journal of Data Analytics and Intelligent Decision-making, 1(3), 1-12. doi: 10.22091/jdaid.2025.14036.1003
Sakib, M., Siddiqui, T., Mustajab, S., Alotaibi, R. M., Alshareef, N. M., & Khan, M. Z. (2025). An ensemble deep learning framework for energy demand forecasting using genetic algorithm-based feature selection. PloS one, 20(1), e0310465.
Shahabi, V., Sarhangi, R., & Mehdiabadi, A. (2025). Explaining IT Governance for Implementing Branchless Banking Through Digital Transformation. Journal of Data Analytics and Intelligent Decision-making, 1(4), 1-31. doi: 10.22091/jdaid.2026.14530.1018
Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394. https://doi.org/10.1016/j.eswa.2023.122394
Zhang, Y., Li, G., Muskat, B., & Law, R. (2021). Tourism demand forecasting: A decomposed deep learning approach. Journal of Travel Research, 60(5), 981-997.
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics