This chapter explores how AI is transforming marketing strategies, customer segmentation, personalized advertising, and customer relationship management (CRM). Additionally, it investigates the ethical considerations and challenges associated with AI implementation in marketing.
TopIntroduction
Conceptualize Artificial Intelligence (AI) is almost an impossible task, not only because of its range of applications, but mostly because of its technological complexity.
AI can be applied to several fields such as medicine, engineering, education, and marketing to name a few.
For this chapter, the author will focus on the effects of AI on marketing, regarding the innovating strategies, segmentation criteria, advertisement changes, and ethical concerns. In order to do so we need first and foremost to understand the correlations (??) between marketing and the AI definition.
According to Kotler and Armstrong (2016), the simple definition of marketing is based on customer relationships to make companies and brands profitable. Despite the conceptualization of marketing suffering several mutations over time (Kotler et al., 2017), in the present, the main topic of marketing relies on creating a robust emotional bridge between customers and brands (Kotler et al., 2021).
Looking back, marketing first conceptualization was entirely focused on production (marketing 1.0), nowadays the epicenter is based on relationships and causes, and society is experiencing marketing 5.0, also known as societal marketing (Kotler et al., 2017, 2021).
This change of paradigm was necessary, because, according to Kotler et al. (2017), the rapid evolution of technology was dehumanizing the applicability of marketing strategies and it was necessary to take a step back in the way marketing viewed its consumers. It doesn't mean that the process is more human than before, but it means that because customers are more informed and the array of selection criteria has increased in terms of items and complexity, the brands need to consider looking at them not as a money machine, but rather as involved partners in the process of buying the products and services.
Before AI, marketeers had to rely on their decisions on market surveys, and purchase behaviors, but most of all on their intuition regarding report sheet analysis, hence and mistakes resulting from bad or insufficient data driven decisions were made (Campbell et al., 2020).
Although according to Deveau et al. (2023) “generative AI promises to disrupt the way B2B1 and B2C2 players think about customers experience, productivity, and growth” (2023, p. 1), it seems that is important to conceptualize AI to better understand this claim.
According to McCarthy (2007) AI is “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to be limited to biologically observable methods” (2007, p. 1).
Nowadays marketeers can understand customers' needs in real-time through large data collection (Haleem et al., 2022), Machine Learning (ML) and linguistic analysis, social media and text mining (Mariani et al., 2022), Task Automation (TA) (Davenport et al., 2021), just to name a few of the AI tools in marketing.
The advent and democratization of social media have indeed provided marketeers with enormous field of personal data almost free of charge and more accurate than market surveys, focus groups, or data collection resulting from questionnaires and surveys, but is it morally ethical to use these personal data without people's knowledge?
Understanding the customer journey brings to the brands a huge advantage to increase their sales and profits, but at what cost?
Despite all AI tools and innovations, several issues must be addressed, such as making the process as humanized as possible, whilst ensuring customers' data secure and private.
Discussing the process humanization with AI may seem contradictory at first, not only because AI tends to have a permanent and systemic penetration of marketing procedures, but because according to Dobrev (2012), it cannot understand human feelings, such as empathy, for Kotler et al. (2017) the analysis of human behavior must be based on understanding their feelings and emotions.