A Research of the Development of New Retail and the Application of Statistics to the New Retail Market

Authors

  • Jian Lin

DOI:

https://doi.org/10.61173/vm44r365

Keywords:

New retail, time series, regression analysis, mean-variance

Abstract

New retail has been the main trend of the future retail market. Statistics plays a key role in sales and inventory of new retail. However, there is little research on combining new retail and statistics worldwide. In this paper, the concept and development of new retail are summarized, and the time series, regression, and mean-variance models from statistics are used. Case analyses are made to clarify the application and value of each of the three models in the new retail market. Besides, the limitations of the three models are also revealed. This article points out that in an increasingly competitive market environment if the market pays more attention to sustainable development, it will be particularly important to use scientific statistical methods to make decisions. This will help optimize inventory management and improve operational efficiency, effectively segment customer groups, comprehensively evaluate marketing strategies, improve customer experience, and strengthen risk management. In this way, companies will gain a significant competitive advantage, thereby enhancing market competitiveness and long-term sustainable development potential.

References

[1] Li M., Zhu Y., Su J. Exploring the driving path of marketing efficiency for new retail enterprises based on fsQCA. Business Economics Research, 2024, (16): 130-134.

[2] Wang Y., Li Z. A study on the new retail model based on big data technology. Journal of Retailing, 2018, 45: 120-127.

[3] Chen K., Pang Z. New retail: A review and future directions. Journal of Business Research, 2020, 48(11): 1207-1228.

[4] Gao S., Jiang Y. The impact of new retail on consumer buying behavior. International Journal of Retail & Distribution Management, 2021, 132: 103-112.

[5] Sun L., Zhang M. Exploring the role of artificial intelligence in new retail. Journal of Retailing and Consumer Services, 2021, 114: 106-114.

[6] Liu P., Zhao J. The future of retail: New retail models and strategies. Journal of Retailing and Consumer Services, 2022, 60(5): 1230-1248.

[7] Zhang Y., Wang S., Li J. E-commerce: A new retail paradigm. International Journal of Information Management, 2013, 33(1): 202-210.

[8] Kushwaha T., Singh S. The impact of mobile technology on retailing. Journal of Retailing and Consumer Services, 2016, 31: 12-21.

[9] Liang T., Chen H., Wang Y. Exploring the ‘new retail’ concept. Journal of Retailing and Consumer Services, 2017, 78: 286-295.

[10] Chang P., Huang M. Artificial intelligence in retail. Journal of Retailing, 2018, 45: 308-317.

[11] Huang Y., Zhang M., Li H. Management strategies for new retail. International Journal of Retail & Distribution Management, 2019, 57(8): 1609-1620.

[12] Gupta S., Jain R. Future trends in retailing. Journal of Business Research, 2020, 48(11): 1123-1129.

[13] Hyndman R. J., Athanasopoulos G. Forecasting: Principles and Practice. OTexts, 2018.

[14] Zhang J., Wang L. Sales forecasting in retail: A time series approach. Journal of Retailing and Consumer Services, 2020, 55: 102-109.

[15] Zhang Y., Liu J., Wang X. Time series forecasting for retail sales using ARIMA model. Journal of Retailing and Consumer Services, 2020, 55: 102-104.

[16] Chen K., Liu Y. Seasonality in retail sales: Implications for marketing strategies. International Journal of Retail & Distribution Management, 2021, 49(6): 675-688.

[17] Xu W., Zhang P. Evaluating marketing campaign effectiveness using time series analysis. Journal of Business Research, 2022, 138: 120-130.

[18] Chen X., Wang Y., Zhang R. Evaluating the impact of promotional activities on sales using time series analysis. Marketing Science, 2022, 41(3): 456-470.

[19] Li C., Sun Y. Incorporating external variables in time series forecasting for retail. Journal of Retailing, 2021, 97(4): 563-577.

[20] Liu Y., Chen P., Zhang J. An empirical study on the forecasting model of retail sales. Journal of Retailing and Consumer Services, 2018, 41: 351-359.

[21] Chen J., Huang Y. Sales forecasting in retailing using multiple linear regression analysis. Journal of Business Research, 2020, 113: 475-482.

[22] Nath S., Jha A., Saha P. Evaluating the effectiveness of promotional strategies in an online retail context. Journal of Business Research, 2020, 112: 295-302.

[23] Chen X., Li Y. Understanding consumer purchase behavior in new retail: A regression analysis approach. International Journal of Retail & Distribution Management, 2020, 48(5): 467- Dean&Francis ISSN 2959-6157 483.

[24] Homburg C., Jozić D., Kuehl N. Customer experience management: Toward implementing an evolving marketing concept. Journal of Marketing, 2017, 81(1): 50-68.

[25] Choi T. M., Cheng T. C. E., Wu J. RFID technology in retailing: A review and future research directions. International Journal of Production Economics, 2018, 207: 222-234.

[26] Zhang Y., Lin J. Inventory management in retail: A regression analysis approach. International Journal of Production Economics, 2023, 249: 108-120.

[27] Markowitz H. Portfolio selection. The Journal of Finance, 1952, 77: 211-214.

[28] El-Haj S. A mean-variance approach to inventory decision making. International Journal of Production Economics, 2019, 22(3): 111-115.

[29] Hwang J., Kwon S. The impact of product variety on retail performance: A mean-variance analysis. Journal of Retailing and Consumer Services, 2020, 21: 123-124.

[30] Dahl J. Operational strategies in grocery retailing: A unified framework. Journal of Retailing, 2019, 59: 130-133

Downloads

Published

2024-12-31

Issue

Vol. 2 No. 6 (2024): Volume 2, Issue 6

Section

Articles

License

Copyright (c) 2024 by the authors.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.