Research on the Application Strategies of Precision Marketing and User Engagement in the Big Data-Driven Retail Industry

Ao Liu

doi:10.71222/9ept8k28

Authors

Ao Liu College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou, China Author

DOI:

https://doi.org/10.71222/9ept8k28

Keywords:

big data, retail industry, precision marketing, user engagement, data analysis

Abstract

With the rapid development of big data technology, the retail industry is facing unprecedented opportunities and challenges in precision marketing and user engagement. In-depth analysis of user behavior, purchase habits, and preferences helps enterprises personalize marketing strategies, thereby effectively improving customer loyalty and sales conversion rates. This paper discusses the precision marketing strategy in the retail industry based on big data analysis, including data collection, data mining, user portrait construction, and precision marketing implementation. It examines how big data facilitates user life-cycle management and enhances user value through engagement, and assesses the practicality and effectiveness of the proposed strategy using case studies. Specifically, the study first establishes a comprehensive theoretical framework that integrates big data analytics with contemporary marketing paradigms, highlighting the critical role of real-time data processing in enabling dynamic customer segmentation. Subsequently, the paper proposes a multi-dimensional user portrait construction methodology that leverages machine learning algorithms to capture granular behavioral patterns across diverse digital touchpoints. The research further explores the application of predictive analytics in optimizing promotional campaigns, inventory management, and personalized recommendation systems within the retail ecosystem. Through detailed case studies of leading retail enterprises, the paper evaluates the measurable impact of data-driven precision marketing on key performance indicators such as customer retention rates, average transaction values, and overall revenue growth. Additionally, the study addresses the ethical considerations and privacy concerns associated with large-scale consumer data utilization, proposing a balanced framework for responsible data governance. This paper puts forward a series of practical suggestions to help retail enterprises achieve sustainable development in the era of big data, contributing valuable insights for both academic researchers and industry practitioners seeking to harness the transformative potential of data-driven marketing strategies.

References

1. G. Santoro, F. Fiano, B. Bertoldi, and F. Ciampi, "Big data for business management in the retail industry," Management Decision, vol. 57, no. 8, pp. 1980–1992, 2019.

2. N. Verma, D. Malhotra, and J. Singh, "Big data analytics for retail industry using MapReduce-Apriori framework," Journal of Management Analytics, vol. 7, no. 3, pp. 424–442, 2020.

3. M. A. E. A. Youssef, R. Eid, and G. Agag, "Cross-national differences in big data analytics adoption in the retail industry," Journal of Retailing and Consumer Services, vol. 64, p. 102827, 2022.

4. J. Kameswari, P. Ramesh, V. Bhavikatti, B. Omnamasivaya, G. Chaitanya, T. Bastray, et al., "Analyzing the role of big data and its effects on the retail industry," in Web Intelligence, vol. 22, no. 1, pp. 45–63, Mar. 2024.

5. N. J. Ogbuke, Y. Y. Yusuf, K. Dharma, and B. A. Mercangoz, "Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society," Production Planning & Control, vol. 33, no. 2–3, pp. 123–137, 2022.

6. M. Kholod, A. Celani, and G. Ciaramella, "The analysis of customers’ transactions based on pos and rfid data using big data analytics tools in the retail space of the future," Applied Sciences, vol. 14, no. 24, p. 11567, 2024.

7. S. K. Singh and M. Del Giudice, "Big data analytics, dynamic capabilities and firm performance," Management Decision, vol. 57, no. 8, pp. 1729–1733, 2019.

8. A. I. Aljumah, M. T. Nuseir, and M. M. Alam, "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Business Process Management Journal, vol. 27, no. 4, pp. 1108–1125, 2021.

9. A. Lutfi, M. Alrawad, A. Alsyouf, M. A. Almaiah, A. Al-Khasawneh, A. L. Al-Khasawneh, et al., "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, vol. 70, p. 103129, 2023.

10. A. Seetharaman, I. Niranjan, V. Tandon, and A. S. Saravanan, "Impact of big data on the retail industry," Journal of Corporate Ownership & Control, vol. 14, no. 1, pp. 506–518, 2016.

11. E. Aktas and Y. Meng, "An exploration of big data practices in retail sector," Logistics, vol. 1, no. 2, p. 12, 2017.

12. J. Aversa, T. Hernandez, and S. Doherty, "Incorporating big data within retail organizations: A case study approach," Journal of Retailing and Consumer Services, vol. 60, p. 102447, 2021.

13. S. Ying, S. Sindakis, S. Aggarwal, C. Chen, and J. Su, "Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance," European Management Journal, vol. 39, no. 3, pp. 390–400, 2021.