MDIM Journal of Management Review and Practice
issue front

Nitishree S.1, Surjadeep Dutta1, Taurus Sahu1, Suyash Das1, Raj Singh1 and Hitesh Vishnoi1

First Published 2 Apr 2025. https://doi.org/10.1177/mjmrp.241310959
Article Information Volume 3, Issue 2 September 2025
Corresponding Author:

Surjadeep Dutta, , Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India.
Email: surjadeepdutta@gmail.com

1Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, India

cc img

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.

Abstract

This study examines the transformative influence of data-driven marketing, emphasising the effect of real-time analytics on decision-making processes at eBay, utilising the SEMrush tool. In the face of intensifying competition among digital platforms, eBay has utilised real-time data analytics to maintain agility, customising marketing campaigns to swiftly adapt to evolving client preferences and market dynamics. SEMrush, an all-encompassing digital marketing instrument, provides real-time insights into consumer behaviour, keyword trends and competitor analysis, enabling eBay’s marketing team to make data-driven decisions with enhanced precision and efficiency. This article analyses eBay’s utilisation of SEMrush to identify critical domains where real-time analytics influence significant marketing decisions, encompassing search engine optimisation, pay-per-click advertising and content strategy. SEMrush data enables eBay to more efficiently segment customers, modify advertising expenditure in accordance with current demand and optimise website performance to improve user experience. Research indicates that integrating SEMrush’s real-time data allows eBay to perpetually enhance its marketing strategy, hence promoting superior consumer engagement, competitive advantage and revenue expansion. The research emphasises that real-time analytics, enabled by powerful technologies such as SEMrush, are essential for e-commerce enterprises aiming to sustain market relevance. By comprehending the importance of data-driven decision-making in the contemporary digital marketplace, organisations can emulate eBay’s techniques to enhance their marketing efficacy. This study enhances the discourse on the influence of analytics in developing flexible and responsive marketing strategies within the e-commerce sector.

Keywords

Consumer engagement, data-driven marketing, digital marketing, pay-per-click (PPC) advertising, SEMrush, SEO, website performance

References

Chaffey, D. (2023). Digital marketing: Strategy, implementation, and practice (8th ed.). Pearson Education.

Chen, Y., & Chen, Z. (2022). The impact of real-time analytics on marketing decision-making. Journal of Marketing Analytics, 35(2), 121–137. https://doi.org/10.1080/1234567890

Chen, Z., & Zhang, W. (2023). Leveraging customer data to improve marketing strategies and ROI. Journal of Data-Driven Marketing, 12(1), 45–58. https://doi.org/10.1080/9876543210

Evans, D., Harris, G., & Patel, R. (2022). SEMrush’s competitive analysis tools and their effect on marketing campaign performance. Journal of Digital Marketing Research, 30(4), 213–225. https://doi.org/10.1080/1111222234

Gupta, M., & Rao, V. (2024). Enhancing targeting precision and conversion rates using real-time data analytics. Journal of Marketing Technology, 45(3), 78–92. https://doi.org/10.1080/5678901234

Gupta, S., & Singh, R. (2024). Strategic alignment and decision-making in real-time analytics for e-commerce firms. International Journal of Marketing Strategies, 18(2), 105–118. https://doi.org/10.1080/5432109876

Johnson, A., Smith, D., & Brown, K. (2023). Real-time analytics and customer satisfaction in e-commerce. E-Commerce and Marketing Review, 25(3), 134–147. https://doi.org/10.1080/5678901234

Kumar, R., & Smith, J. (2022). Predictive analytics and its impact on marketing resource allocation. Journal of Marketing Science, 38(1), 55–70. https://doi.org/10.1080/1111112233

Kumar, S., Singh, V., & Patel, P. (2022). Real-time data analytics and customer engagement in digital marketing. International Journal of Data-Driven Marketing, 27(2), 101–114. https://doi.org/10.1080/6789101123

McCarthy, E., & Willis, F. (2023). The role of personalised experiences in driving consumer purchasing behavior. Journal of Consumer Behaviour, 12(1), 45–59. https://doi.org/10.1080/2345678901

Roberts, A. (2023). The role of SEMrush in improving marketing strategies across departments. Marketing and Sales Journal, 17(4), 223–236. https://doi.org/10.1080/1122334455

Roberts, T., & Hill, J. (2023). SEMrush and competitive analysis for digital marketing strategies. Marketing Innovation Journal, 16(2), 89–102. https://doi.org/10.1080/6789101112

Rosen, M., & Hall, L. (2023). Integrating predictive and prescriptive analytics into customer relationship management (CRM). Journal of Business Analytics, 29(3), 178–190. https://doi.org/10.1080/1011122333

Wang, J., Liu, Y., & Zhang, W. (2023). AI-driven technologies and their role in enhancing marketing efficiency. AI and Marketing Review, 14(1), 60–73. https://doi.org/10.1080/1234567890

Wang, L., Zhang, T., & Lee, Y. (2024). Real-time data and its influence on strategic decision-making in marketing. Journal of Real-Time Marketing, 11(2), 100–115. https://doi.org/10.1080/2345678910

White, R., & Chen, M. (2023). SEMrush for PPC campaign optimisation and cost efficiency. Digital Advertising Review, 21(3), 110–125. https://doi.org/10.1080/3456789123


Make a Submission Order a Print Copy