Marketing Policy In Service Enterprises Using Deep Learning Model

Authors

  • K. Karunambiga Professor, Department of CSE, Karpagam Institute of Technology, Affiliated to Anna University, Coimbatore, Tamilnadu, India.
  • Syed Ibad Ali Assistant Professor, School of Engineering, Ajeenkya DY Patil University, Pune, Maharashtra, India.
  • Sajitha L. P. Assistant Professor, Department of Computer Science and Business System, R. M. K Engineering college, Kavaraipettai, Tamilnadu, India.
  • Allen Paul Esteban Faculty, Nueva Ecija University of Science and Technology, Cabanatuan City, Philippines
  • Marilou Pascual Faculty, Nueva Ecija University of Science and Technology, Cabanatuan City, Philippines.
  • D. Praveenadevi Assistant Professor, KL Business School, Koneru Lakshmaiah Education foundation (Deemed to be University), Andhra Pradesh, India

Keywords:

Marketing Policy, Service Enterprises, Deep Learning Model

Abstract

Consumer awareness has risen to unprecedented heights as a result of scientific and technological progress, yet the economic model of established markets is crumbling quickly as a result of the same factors. Marketing strategies in the Internet age need constant updating and optimization to reflect the changing demographics of the customer base. This research provides a high-level summary of the relationship between sales strategy and psychology. Then, it employs the deep learning technique neural network architecture to build a fusion model for preference prediction. The proposed technique is then evaluated with the conventional metrics. Collaboration and outsourcing, job complexity and autonomy, and work organization and innovation were found to have the greatest positive effects on corporate success and well-being. Organization and creativity in the workplace are also essential. The prediction of sales is shown in the result, public opinion and the internet search index results in a 17.5% improvement in the accuracy of the model predictions. This allows the enterprises to improve both the accuracy of their sales forecasts as well as the reliability of the references they provide for future negotiations.

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References

Schmidt, A., Kabir, M. W. U., & Hoque, M. T. (2022). Machine Learning Based Restaurant Sales Forecasting. Machine Learning and Knowledge Extraction, 4(1), 105-130.

Koev, S. R., Tryfonova, O., Inzhyievska, L., Trushkina, N., & Radieva, M. (2019). Management of domestic marketing of service enterprises. IBIMA Business Review, 2019, 1-13.

Polo Peña, A. I., Frías Jamilena, D. M., & Rodriguez Molina, M. A. (2011). Impact of market orientation and ICT on the performance of rural smaller service enterprises. Journal of Small Business Management, 49(3), 331-360.

Wang, C. H. (2022). Considering economic indicators and dynamic channel interactions to conduct sales forecasting for retail sectors. Computers & Industrial Engineering, 165, 107965.

Türkbayrağí, M. G., Dogu, E., & Esra Albayrak, Y. (2022). Artificial intelligence based prediction models: Sales forecasting application in automotive aftermarket. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-13.

Bi, X., Adomavicius, G., Li, W., & Qu, A. (2022). Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness. INFORMS Journal on Computing.

Yang, K. (2022). Performance of Sales Forecasting for E-Commerce System and its Importance. Journal of Internet Banking and Commerce, 27(2), 1-3.

Yeasmin, N., Amin, S. H., & Tosarkani, B. M. (2022). Machine Learning Techniques for Grocery Sales Forecasting by Analyzing Historical Data. In Artificial Intelligence in Industrial Applications (pp. 21-36). Springer, Cham.

Fahrudin, T., Wisna, N., Telnoni, P. A., & Wijaya, D. R. (2022, January). Sales Forecasting Web Application in Small and Medium Enterprise. In 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE) (pp. 213-218). IEEE.

Manjula Pattnaik, M. Sunil Kumar, S. Selvakanmani, Kaniphnath Malhari Kudale, Kumarasamy M, B. Girimurugan(2023)Nature-Inspired Optimisation-Based Regression Based Regression to Study the Scope of Professional Growth in Small and Medium Enterprises. International Journal of Intelligent Systems And Applications In Engineering Vol 11(4s), 100–108.

Dr. Somanchi Hari Krishna , Alok Upadhyay, Mallika Tewari, Anita Gehlot, Dr. B. Girimurugan, Dr. Sumit Pundir “Empirical investigation of the key machine learning elements promoting e-business using an SEM framework” International Conference on Contemporary Computing and Informatics DOI: 10.1109/IC3I56241.2022.10072712 P.No 1960-1964.IEEE

M.K.Babu, K.Anusha et.al.(2023) The mediating effect of price on the relationship between brand image and customer satisfaction towards dairy products Journal of Livestock Science Vol 14 P.No 198-203

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Published

05.12.2023

How to Cite

Karunambiga, K. ., Ali, S. I. ., L. P., S. ., Esteban, A. P. ., Pascual, M. ., & Praveenadevi, D. . (2023). Marketing Policy In Service Enterprises Using Deep Learning Model. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 239–243. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4066

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Section

Research Article