Jayaweera, Apurwa and Farooq, Muhammad ORCID: https://orcid.org/0000-0002-3744-3230 (2023) Leveraging AI-driven digital marketing strategies to automate the lead generation mechanism of the real estate industry in the United States. In: Delener, N, and Schweikert, Christina, (eds.) 2023 GBATA readings book. Global Business and Technology Association

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Abstract

This paper examines the use of AI and digital strategies for lead generation in the United States real estate industry.
Traditional methods of lead generation are labour-intensive and time-consuming in the real estate industry. They also produce low-quality leads. The paper highlights how AI technologies can automate lead generation while enhancing customer engagement and providing personalized experiences. The paper examines real-world cases, assesses AI technology comprehensively, and discusses both the benefits and challenges that AI can bring to digital marketing in real estate. This study provides valuable insights to digital marketers and agents. It lays the foundation for future research on AI-assisted leads generation and proposes practical guidelines for integrating AI into digital real estate marketing. The paper explores the limitations and inefficiency of traditional methods for lead generation and the need to innovate and efficiently approach the fast-growing real estate industry. The paper emphasizes AI-powered creative ad generation as a way to produce engaging and effective ads on a large scale. It also highlights the benefits of AI chatbots in customer support and lead qualification. The paper also discusses how to integrate AI platforms with automation software like Zapier to improve lead generation, and lead qualification and streamline data transfers. Combining AI with Zapier provides scalable solutions to reduce errors and manual tasks, which makes it ideal for real estate businesses of any size.

Keywords: Artificial Intelligence (AI), digital marketing, lead generation, lead nurturing, real estate

Item Type:	Book Section
Status:	Published
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/8825

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