E-commerce Platform in Digital Economy: Analysis and Optimization of Amazon's Footwear Store
DOI:
https://doi.org/10.70267/t59bcb11Keywords:
Amazon cross-border e-commerce, men's footwear industry, data analysis, marketing optimizationAbstract
With the continuous deepening of globalization, cross-border e-commerce has become an important part of global trade. Especially driven by digital payment technology, network technology, industrial upgrading and policy support, China's cross-border e-commerce industry has developed rapidly. This research aims to analyze the sales data of men's footwear on the Amazon platform, understand market trends, user behavior patterns, and product characteristics, and then provide data-supported marketing strategies and operational decisions for e-commerce platforms. We have adopted a variety of data analysis techniques, including data cleaning, ARIMA model prediction, price band analysis, repeat purchase rate analysis, user profiling (RFM model), sentiment analysis and cluster analysis. Through comprehensive analysis of multi-dimensional information such as store sales data, product attributes, market hot words, and user evaluations, market dynamics and user preferences are revealed. We have comprehensively applied a variety of data analysis models and techniques, such as using the ARIMA model for sales prediction, the LDA model for sentiment analysis, and the RFM model combined with cluster analysis to construct a refined user profile. Through in-depth analysis of user transaction behaviors and product attributes, accurate marketing strategies can be provided for e-commerce platforms, enhance user experience, increase user stickiness, and thus achieve business growth and improvement of profitability. At the same time, the research also points out the importance of brand building, supply chain management, risk assessment and other aspects, providing strategic suggestions for the long-term stable development of cross-border e-commerce.
Downloads
References
Alsuwaylimi, A., & Amjad, A. A. (2023). Comparison of ARIMA, ANN and Hybrid ARIMA-ANN Models for Time Series Forecasting. Information Sciences Letters, 12(2), 1003-1016.
Huang, Y. (2016). Digital economy dividends: Rewards for pioneers. Big Data Era, (3), 6-9.
Kenney, M., Zysman, J., & Martin, B. (2016). The Rise of the Platform Economy. Issues in Science and Technology, 32(3), 61-69.
Kumar, A., Sharma, N., Gurna, K. K., Anand, A., Patni, J. C., Pinjarkar, L., & Latika, E. (2024). Forecasting Stellar XLM Prices: Insights from ARIMA Analysis. International Journal of Religion, 5(6), 273-288.
Liqin, W., Shenglin, M., & Shuping, L. (2024). The influence of internet celebrity anchors’ reputation on consumers’ purchase intention in the context of digital economy: from the perspective of consumers’ initial trust. Applied Economics, 1-22. (Note: This citation may need to be adjusted based on the final publication details, as it appears to be a preprint or early version.)
Ma, S., Fang, C., & Liang, Y. (2018). Digital Trade and Its Values and Research Prospects. International Trade Issues, (10), 16-30.
Meertens, Q. A., Diks, C. G. H., van den Herik, H. J., & Takes, F. W. (2020). A data-driven supply-side approach for estimating cross-border Internet purchases within the European Union. Journal of the Royal Statistical Society. Series A, Statistics in Society, 183(1), 61-90.
Song, M., Wang, R., & Li, Y. (2024). Hybrid time series interval prediction by granular neural network and ARIMA. Granular Computing, 9(1).
Oh, J., & Seong, B. (2024). Forecasting with a combined model of ETS and ARIMA. Communications for Statistical Applications and Methods, 31(1), 143-154.
Wang, Y., & Ma, S. (2021). Time series prediction based on ARIMA and LSTM hybrid model. Computer Applications and Software, (02), 291-298.
Xu, L. (2020). Research on the competitiveness evaluation of global digital economy enterprises. Shanghai Economy, (3), 77-90.
Zhai, H. (2020). Research on the challenges and countermeasures faced by China's cross-border e-commerce under the background of digital economy. Management and Technology of SMEs, (34), 30-31.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Bowen Xiong, Jianwei Sun, Xiangwen Pang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
