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From lead generation to social media management: How RPA transforms digital marketing operations
Independent Researcher, USA.
Research Article
International Journal of Science and Research Archive, 2022, 07(02), 644–655.
Article DOI: 10.30574/ijsra.2022.7.2.0270
Publication history:
Received on 13 October 2022; revised on 20 December 2022; accepted on 24 December 2022
Abstract:
The application of robotic process automation, commonly known as RPA, is disrupting the digital marketing realm by reducing and automating repetitive high-volume tasks across myriad functions. In this paper, the effects of RPA in significant core digital marketing processes like lead generation, social media, and email marketing are analyzed. RPA travails to advance data intake, lead management, and customer relations in marketing and sales effectiveness and velocity. For example, in Lead generation, RPA, especially in identification and filtering, uses the internet to do the job, leading to a lot of time-saving.. In social media management, RPA tools can automate scheduling, specifically monitoring the brand mentions and generating responses to relevant posts to help marketers move from the tactical to the strategic level. Similarly, it describes how effective email marketing is supported by RPA, which increases the level of campaign customization and performance monitoring.
Some of the specific applications and benefits of applying RPA in digital marketing are presented in the context of this research, as well as the clearly prominent cost and time savings from automating routine tasks. With AI and ML now slowly integrating into the field of RPA, we can expect the potential of RPA in digital marketing to increase even further as more businesses adapt to the technology. The information presented in this article is meant to help educate a reader on how RPA enhances the processes involved with digital marketing to improve business results and customer experiences.
Keywords:
Social Media; Lead Generation; RPA; ML; RPA Tools; Digital Marketing; Machine Learning
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
