The AI Revolution in SMEs: Intelligent Personalization and Segmentation in Digital Marketing

Luca Castelli (), Armando Salvador (), Paulo Sartorato () and Eva Olmedo ()
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Luca Castelli: EAE Business School
Armando Salvador: EAE Business School
Paulo Sartorato: EAE Business School
Eva Olmedo: EAE Business School

A chapter in Business Entrepreneurship and Innovation, 2025, pp 143-156 from Springer

Abstract: Abstract The artificial intelligence (AI) and machine learning revolution is transforming the way small and medium-sized enterprises (SMEs) approach customer segmentation and personalization in digital marketing. This study investigates how SMEs are adapting their segmentation and personalization strategies by integrating AI and machine learning tools into their digital marketing activities. The methodology combines qualitative interviews with industry experts and analysis of successful SME cases that have implemented AI solutions, with the aim of understanding current practices and best adoption strategies. The results reveal significant benefits of AI adoption in digital marketing, including greater accuracy in audience segmentation, scalable content personalization, and improved campaign efficiency. Barriers such as resource scarcity, lack of specialized knowledge, and data management challenges are also identified, which can hinder implementation in SMEs. The study provides recommendations to overcome these obstacles, emphasizing the need for training, gradual investments, and strategic partnerships to facilitate the effective integration of these technologies. It concludes that the incorporation of AI and machine learning can increase the competitiveness and efficiency of SMEs in the digital marketing environment by optimizing data-driven decision-making and improving the customer experience.

Keywords: SMEs; Intelligence; Marketing; Segmentation; Personalization; Chatbots; Automation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:seschp:978-3-032-05730-3_9

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DOI: 10.1007/978-3-032-05730-3_9

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