Abstract
A modern effective business model involves the use of an appropriate pricing strategy. However, not only a short-term profitability matters but also long-term clients’ loyalty. The main purpose of this paper is to present a specific transactional pricing strategy for a second-hand goods resale exchange platform, which allows to avoid possible negative outcomes of being associated with consumer discrimination. Using simulation modeling approach, it was shown how customer segmentation combined with transactional pricing can help to gain higher profitability. The model is based on the work of intelligent agents that recreate the full product lifecycle. Changing the input parameters of the model, it is possible to simulate different scenarios of a company’s activity and market conditions. The model supports the inclusion of any number of products, while its intelligent agents’ methods are still flexible to replace with other techniques. The simulation model has shown that the use of transactional pricing can increase the profitability of a business, while keeping its clients loyal.
References
1. Carta, S., Medda, A., Pili, A., Recupero, R., & Saia, R. (2018). Forecasting
E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data. Future Internet, 11(5). doi: 10.3390/fi11010005
2. Den Boer, A. V. (2015). Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions. Surveys in operations research and management science, 20(1), 1-18. doi: 10.1016/j.sorms.2015.03.001
3. Dolgui, A., & Proth, J.-M. (2010). Supply Chain Engineering: Useful Methods and Techniques. New York, NY: Springer.
4. Elmachtoub, A. N., Gupta, V., & Hamilton, M. (October 1, 2018). The Value of Personalized Pricing. Retrieved from https://ssrn.com/abstract=3127719. doi: 10.2139/ssrn.3127719
5. Facebook. (2019). Advertising Policies. Retrieved from https://www.facebook.com/policies/ads/
6. Faruqui, A. (2010). The Ethics of Dynamic Pricing. The Electricity Journal, 23(6), 13-27. doi: 10.1016/j.tej.2010.05.013
7. Gladkih, I. V. (2013). Tsenovaia strategiia kompanii: oriientatsiia na potrebitielia [A Customer Oriented Pricing Strategy of a Company]. Saint-Petersburg: Vysshaiia Shkola Menedzhmenta.
8. Kannan P. K., & Kopalle, P. K. (2001). Marketing in the E-Channel. International Journal of Electronic Commerce, 5(3), 63-83. doi: 10.1080/10864415.2001.11044211
9. Liu, W. W., Liu, Y., & Chan, N. H. (2018). Modeling EBay Price Using Stochastic Differential Equations. Journal of Forecasting, 38(3), 63-72. doi: 10.1002/for.2551
10. Obermiller, C., Arnesen, D., & Cohen, M. (2012). Customized Pricing: Win-Win or End Run? Drake Management Review 1(2), 12-28.
11. Office of Fair Trading. (2013). Personalized Pricing: Increasing Transparency to Improve Trust. Retrieved from https://one.oecd.org/document/DAF/COMP(2018)13/en/pdf
12. Organization for Economic Co-operation and Development. (2018). Personalized Pricing in the Digital Era. Retrieved from http://www.oft.gov.uk/shared_oft/markets-work/personalised-pricing/oft1489.pdf
13. Ponomarenko, V. S., & Klebanova, T. S. (2019). Tools for Modeling Systems in the Information Economy. Kharkiv: VSHEM-KHNEU.
14. Sahay, A. (2007). How to Reap Higher Profits with Dynamic Pricing. MIT Sloan Management Review, 48(4), 53-60.
15. Tong, Y., Wang, L., Zhou, Z., Chen, L., Du, B., & Ye, J. (2018). Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach. Special Interest Group on Management of Data, Jun 10-15.
16. Tseng, K.-K., Lin, R. F.-Y., Zhou, H., Kurniajaya, K.J., & Li, Q. (2017). Price Prediction of E-Commerce Products Through Internet Sentiment Analysis. Electronic Commerce Research, 18(1), 65-88. doi: 10.1007/s10660-017-9272-9
17. Tyralis, H., & Papacharalampous, G. (2017). Variable Selection in Time Series Forecasting Using Random Forests. Algorithms, 10(4), No. 114. doi: 10.3390/a10040114
18. Weatherford, L. R., & Bodily, S. E. (1992). A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, And Pricing. Operations Research, 40(5), 831-844. doi: 10.1287/opre.40.5.831
