Volume 8 • Issue 1 • PP: 26-34 • 2021
Deep Learning Model for Digital Sales Increasing and Forecasting: Towards Smart E-Commerce
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In this paper, we have proposed a system that will be able to forecast the sales of the e-commerce systems by using the techniques of the deep learning, the main goal of this paper is to help the business and the top management level of the company in decision making in order to provide the workplace the effectiveness and the efficiency in the workplace and to provide an efficient and effective system that it is intelligence to forecast and increase the sales of an e-commerce system, this paper will start with building an e-commerce website using different programming languages which are HTML, CSS, Django, JavaScript Bootstrap, and it this e-commerce website will have a specific database that contains different tables for the product list, the orders, and for the user information and many other tables, then the deep learning algorithms such as Deep Belief Networks and Convolutional Neural Networks will be applied in order to provide an effective system for digital marketing usage, so, it will be able to function as a marketing manager.
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References
[1] Bauer, J., & Jannach, D. (2018). Optimal pricing in e-commerce based on sparse and noisy data. Decision Support Systems, 106, 53–63.
[2] Chen, Y., Lee, J. Y., Sridhar, S., Mittal, V., McCallister, K., & Singal, A. G. (2020). Improving cancer outreach effectiveness through targeting and economic assessments: Insights from a randomized field experiment. Journal of Marketing, 84(3), 1–27.
[3] Guo, J., Zhang, W., Fan, W., & Li, W. (2018). Combining geographical and social influ- ences with deep learning for personalized point-of interest recommendation. Journal of Management Information Systems, 35(4), 1121–1153.
[4] Pitt, C. S., Bal, A. S., & Plangger, K. (2020). New approaches to psychographic con- sumer segmentation: Exploring fine art collectors using artificial intelligence, au- tomated text analysis and correspondence analysis. European Journal of Marketing.
[5] Sha Nazim, S, & Rajeswari, M (2019). Creating a Brand Value and Consumer Satisfaction in E-Commerce Business Using Artificial Intelligence with the Help of Vosag Tech- nology. International Journal of Innovative Technology and Exploring Engineering, 8(8), 1510–1515.
[6] Vetterli, C., Uebernickel, F., Brenner, W., Petrie, C., & Stermann, D. (2016). How Deutsche bank’s IT division used design thinking to achieve customer proximity. MIS Quarterly Executive, 15(1), 37–53.
[7] Wirth, N. (2018). Hello marketing, what can artificial intelligence help you with. Interna- tional Journal of Market Research, 60(5), 435–438.
[8] Wunderlich, N. V., Heinonen, K., Ostrom, A. L., Patricio, L., Sousa, R., Voss, C., & Lem- mink, J. (2015). “Futurizing” smart service: Implications for service researchers and managers. Journal of Services Marketing, 29(6/7), 442–447.
[9] Xishu Li, Ying Yin, David Vergara Manrique, Thomas Bäck, Lifecycle forecast for consumer technology products with limited sales data, International Journal of Production Economics, Volume 239, 2021, 108206, ISSN 0925-5273, https://doi.org/10.1016/j.ijpe.2021.108206,
[10] Yong-Hak, J. (2013). Web of science. Thomson Reuters. Zhang, H., Cao, X., Ho, J. K., & Chow, T. W. (2016). Object-level video advertising: an optimization framework. IEEE Transactions on Industrial Informatics, 13(2), 520–531.
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