International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Predicting Selling Price of First Time Product for Online Seller using Big Data Analytics

  • Received : 2021.02.05
  • Published : 2021.02.28
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Abstract

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

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References

  1. https://www.statista.com/statistics/792047/india-e-commerce-market-size/ accessed in Septeember, 2020.
  2. N. D. G. Vadivu, "Big Data Analytics for Gold Price Forecasting Based on Decision Tree Algorithm and Support Vector Regression (SVR)," Int. J. Sci. Res., vol. 4, no. 3, pp. 2026-2030, 2015, [Online]. Available: https://www.ijsr.net/archive/v4i3/SUB152560.pdf.
  3. S. Jeon, B. Hong, J. Kim, and H. J. Lee, "Stock price prediction based on stock big data and pattern graph analysis," IoTBD 2016 - Proc. Int. Conf. Internet Things Big Data, no. IoTBD, pp. 223-231, 2016, doi: 10.5220/0005876102230231.
  4. Z. Peng, "Stocks Analysis and Prediction Using Big Data Analytics," Proc. - 2019 Int. Conf. Intell. Transp. Big Data Smart City, ICITBS 2019, pp. 309-312, 2019, doi: 10.1109/ICITBS.2019.00081.
  5. M. Price and S. Challenge, "Price Prediction using Machine Learning Regression - a case study," 2021.
  6. P. Y. Zhao and Y. M. Shi, "Predicting the likelihood of purchase by big data," AIP Conf. Proc., vol. 1834, no. April, 2017, doi: 10.1063/1.4981598.
  7. K. J. Ferreira, B. H. A. Lee, and D. Simchi-Levi, "Analytics for an online retailer: Demand forecasting and price optimization," Manuf. Serv. Oper. Manag., vol. 18, no. 1, pp. 69-88, 2016, doi: 10.1287/msom.2015.0561.
  8. Subrahmanyan, Saroja, "Using quantitative models for setting retail prices," Journal of Product & Brand Management 9 (2000): 304-320. https://doi.org/10.1108/10610420010347100
  9. Michael Levy, Dhruv Grewal, Praveen K. Kopalle, James D. Hess, Emerging trends in retail pricing practice: implications for research, Journal of Retailing, Volume 80, Issue 3, 2004, Pages xiii-xxi, ISSN 0022-4359 https://doi.org/10.1016/j.jretai.2004.08.003
  10. Christopher, M., "Logistics and Supply Chain Management," Pearson UK, 2016.
  11. Ozer, O., R. Philips, The Oxford Handbook of Pricing Management, Oxford University Press, 2012.
  12. Caro, F., J Gallien, "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research 60(6), 1404-1422. https://doi.org/10.1287/opre.1120.1102
  13. Talluri, KT, GJ Van Ryzin, "The Theory and Practice of Revenue Management," Springer, 2005.
  14. M. Seyedan and F. Mafakheri, "Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities," J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00329-2.
  15. V. Sandhiya, T. Revathi, A. Jayashree, A. Ramya, and S. Sivasankari, "Stock Market Prediction on Bigdata Using Machine Learning Algorithm," vol. 7, no. 4, pp. 10057-10059, 2017.