Market Grooming

In the era of generative artificial intelligence (AI), big data analytics, business analytics and mega global digital corporations, the profession of marketing is at a crossroads between ‘Prosumer-Marketing’ and ‘Market Grooming’. Whereas prosumer (producers + consumers) marketing means a process of exchange in which producers and consumers have equal, just, control, voluntary, fully aware engagement and control over the process of design, development and exchange of goods, services and values. On the other hand, ‘Market Grooming’ is a one-sided, unethical process of conditioning or influencing, deceiving, or persuading or manipulating and even exploiting customers by the marketing organisations, without customers' voluntary consent, permissions, awareness, etc. As the consumers have asymmetric access to information, asymmetric and lesser favourable levels of control, and lesser power in the process of exchange, as customers trust the marketers or are dependent on popular brands, the markers tend to exploit the situation. The process of market grooming has become easier due to the power of AI, generative AI, ChatGPT, TikToketing, machine learning and big data analytics leading to the development of sophisticated predictive models and persuasive models. This chapter explores and analyses a range of techniques in marketing such as permission marketing, flywheel marketing, subliminal marketing, neuromarketing, cyberstalking, ethical marketing, etc., in the era of AI. The arguments for high concerns pertaining to potential market grooming are supported by theories of ethics, theories of digital marketing and models of AI. The chapter concludes with some strategic recommendations.

Algorithmic trading has evolved beyond traditional methods by incorporating machine learning techniques to analyse extensive datasets. The integration of machine learning and ATS has helped in enhancing the decision-making process, leading to more accurate predictions of market trends, risk assessments, and optimal execution strategies. The opaque nature of artificial trading models can create challenges in understanding the decision-making process of these systems. This lack of clear understanding raises questions about accountability, and market participants lack transparency on whether movements are economic-driven or algorithmic trading strategies. The chapter explores the development of I-driven trading and Key Characteristics of Algorithmic Trading Systems. In conclusion, the integration of machine learning into capital markets represents a major shift in how investment decisions are made, risks are managed, and how markets operate independent artificial intelligence trading systems. Its increasing use highlights the need for careful ethical consideration, regulatory flexibility and ongoing monitoring.

The growth of Artificial Intelligence (AI)–enabled marketing has led to motivating customers purchase goods and services where they are ‘nurtured’ or ‘groomed’ to make a purchase decision. Consumer grooming as the name suggests involves changing or influencing an individual's behaviour and decision-making abilities by repeated personalised messaging. We have entered an era where AI is driving marketing in almost all industries and influencing customer decision-making. The healthcare industry is quite a concern as it involves the health of the poor and vulnerable impacted by AI decision-making, also deeply affecting the conventional doctor–patient relationships. AI in healthcare marketing involves using marketing gimmicks by marketing organisations where individuals are targeted with individualised medical messaging, changing the trust dynamics between patients and doctors. The marketing gimmicks often impact the healthcare decision-making of patients, leading to induced healthcare purchases through these marketing messages, rather on advisory of doctors or other healthcare professionals. As a result of this constant patient grooming or medical brainwashing, patients end up making a wrong decision regarding their healthcare. Therefore, it is required that stakeholders in the health ecosystem prioritise more transparency, authenticity and patient empowerment to mitigate the challenges of patient grooming in the healthcare sector. The establishment of more stringent controls on medical marketing techniques, the development of health literacy, and the cultivation of open communication channels within the healthcare ecosystem are all necessary because of this. In the end, AI-driven marketing presents prospects for personalised healthcare experiences; yet its unregulated expansion raises substantial ethical and patient safety issues.

Organisations using advanced technology, like ChatGPT, for executing their marketing practices are proliferating, but such fast growth also comes with different adverse impacts of ChatGPT. This interaction of ChatGPT with the humanly implemented marketing 5.0 approach complements the marketing effectiveness. However, while considering the brighter aspects of this techno-marketing integration, marketers should also keep its dark side in mind. Therefore, this chapter investigates the integration of AI-enabled ChatGPT into marketing 5.0 practices. However, both the concepts under study are growing in terms of literature, and the research gap is even more extended when considering their associated views. Furthermore, significantly less literature is available emphasising the negative aspects of this advanced technology. This chapter bridges these gaps by reviewing the literature and presenting the gold-plating effect of ChatGPT usage while implementing marketing 5.0 practices. It also proposes a framework for showing the relationship between ChatGPT utilisation and practicing marketing 5.0, depicting the dark side of this techno-marketing integration. It also emphasised the need for conscious and learned associations between the concepts under study.

Artificial intelligence (AI) has transformed the field of hiring, enabling employers to collect and analyse massive amounts of data to understand and predict the suitability of candidates. However, AI can also have subconscious effects on candidates’ and employers needs through biased data, which can stem from human biases, algorithmic errors or external factors. For example, Amazon scrapped an AI-based recruitment programme that favoured male candidates over female candidates due to the historical patterns in the resumes it analysed. This paper examines how AI can shape candidate's needs through biased data from various sources and types, and what are the consequences for candidate's welfare and rights. We review the literature on AI applications in hiring, the origins and kinds of bias in AI systems, and the potential risks and benefits for candidates. We also suggest some guidelines for reducing bias in AI and enabling candidates to make informed and ethical choices online. We argue that AI can be a double-edged sword for candidate's needs and that more research and regulation are required to ensure its fair and accountable use.

Predictive analytics stands as a cornerstone of customer-centric strategies, offering invaluable insights. While customer analysis has been conducted for years, the manual handling of data has limited its effectiveness. Using predictive analytics tools, marketers have the potential to manipulate customers unethically, grooming them to purchase products they wouldn't otherwise consider buying. This research investigates the intricate dynamics of consumer behaviour and the transformative impact of predictive machine learning algorithms. Employing a mixed-methods research design combining quantitative and qualitative techniques, the study explores the application of unsupervised K-means clustering and supervised random forest algorithms. Through real-world case studies and data analysis, insights are gained into the predictive modelling of customer behaviour in diverse industries. Findings reveal the effectiveness of these techniques in segmenting customers based on income and spending behaviour, with a prediction accuracy of 84%. Furthermore, the study underscores the importance of integrating qualitative insights to enrich understanding and validity. The study also critically explores the potential risks associated with unethical marketing that led customers to purchase products without their voluntary and fully informed consent. This research contributes to advancing the understanding of consumer behaviour forecasting and predictive machine learning applications, paving the way for future research endeavours in this domain.

The concept of ‘intelligence’ used to differ between human and machines, until the disruption of artificial intelligence (AI). The field of AI is advancing far more rapidly than the establishment of rules and regulations, which is causing certain fear. However, slowing down this progression to avoid economic crisis is not an option because of open-source AI, which facilitates faster development processes and collective contributions to codes and algorithms. Public policies, such as the ‘European Union AI Act (EU AI)’, ‘Whitehouse AI’, and the G7's ‘Hiroshima Artificial Intelligence Process’ (HAP), are already drafted. Regulators need to adopt a dynamic approach given AI's rapid advancement, and they need to eventually strive for international harmonisation in their rules and regulations for better collaborations. The EU's AI Act is the ‘world's first comprehensive law’ and it focuses on five main pillars similar to other countries drafts: ensuring AI usage is safe, transparent, traceable, non-discriminatory and environmentally friendly. They portray four risk categories against which citizens can file complaints: (1) Unacceptable risk (2) High risk (3) Generative AI (4) Limited risk. The US AI policies include ‘The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People’ and the ‘Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence’. This conceptual study extensively reviews the concept of AI and compares pioneering draft laws while providing recommendations on ethics and responsible AI. The contribution of this study is that it sheds light on the evolving evolution of AI and the challenges posed by the rapid advancement of AI technology, emphasising the necessity for flexible and adaptive regulatory frameworks. This is the first paper to explore AI from the academic and political perspective.

Artificial intelligence (AI) has several advantages for enhancing marketing strategies. It raises ethical problems about customer priority, market share consolidation and consumer manipulation. This study examines ethical issues from a modern viewpoint, using insights from AI and previous literature review. The implementation of AI in marketing may result in unanticipated ramifications, including the reinforcement of preexisting prejudices, infringement upon customer privacy, restriction of competitive forces and meddling with consumer conduct. This research seeks to enhance the industry by offering a contemporary viewpoint on the ethical issues related to AI utilisation in predictive marketing, based on insights from previous literature review in the field.

This research, titled ‘AI in Digital Marketing: The Ethical Implications of Digital Influence on Markets and Consumers,’ conducts a comprehensive examination of the nuanced role played by artificial intelligence (AI) in shaping consumer behaviour and influencing decision-making processes. While the incorporation of AI in marketing offers commendable advantages, such as providing personalised content and optimising strategies to enhance customer experiences and market efficiency, it concurrently introduces ethical considerations. This study meticulously scrutinises the latent potential for market grooming, wherein AI subtly guides consumers towards decisions that may not align with their best interests. By delving into instances of data privacy breaches, algorithmic biases and the unintended consequences of hyper-personalisation, this research contributes substantively to the ongoing discourse on the responsible utilisation of AI. The study underscores the imperative need for regulatory frameworks aimed at ensuring ethical practices in the dynamically shifting digital landscape. It endeavours to strike an equitable balance between the constructive contributions and potential pitfalls of AI in the realm of marketing. Through this research, we aim to shed light on the ethical dimensions associated with the digital manipulation of markets and consumers, providing insights that can inform industry practices, policymaking and public awareness.

This study investigates the intricate relationship between personalisation and automation in Artificial Intelligence (AI), focussing on their impact on human interactions. The purpose is to discern patterns significantly influencing modern society, using notable examples from e-commerce, social media and digital advertising. The research employs a multifaceted approach, drawing insights from real-world examples of AI implementation. Noteworthy instances include Amazon's use of AI algorithms for personalised product recommendations, Netflix's application of AI in content recommendations and Tesla's Full Self-Driving feature in autonomous vehicles. The findings reveal the dual nature of personalisation and automation. In e-commerce, personalised recommendations, such as those on Amazon, can lead to impulse buying and potential financial strain. Similarly, social media algorithms, like Facebook's echo chamber and advertising strategies, exemplified by Google's ‘skippable’ and ‘non-skippable’ ads, strategically influence user behaviour and decision-making. The research also highlights the success of Netflix's personalised content delivery and the potential safety challenges in Tesla's Full Self-Driving feature. The study underscores the importance of a balanced approach to personalisation and automation, especially in ethical considerations, user privacy and data security.

Marketing is sometimes viewed as manipulative and as enticing consumers to live beyond their means. Artificial intelligence (AI)-powered systems can change the image of the marketing discipline and improve the marketing decision-making process. This chapter argues that embedding AI in the marketing process can help to alleviate public and consumer concerns about the marketing discipline. AI has the potential to make the marketing process transparent, but this is dependent on trust and privacy variables. Openness about using AI in the customer experience and how it is applied will put marketing on an objective framework. However, marketing decisions will be a mix of data and information mediated by intuition, reasoning, experience and empathy and these are qualities that are associated with marketers. AI customer experience requires decisions that are objective (personalisation) and those that are empathy related.

The global popularity of short video platforms has surged with the rapid development of mobile internet and 5G technology. DOUYIN, among other platforms, has amassed a massive user base in China. This study presents a theoretical framework based on media dependency theory and user stickiness perspectives. It identifies three key factors that affect user stickiness: platform algorithms, content resources and user interaction. An interpretive philosophy and inductive qualitative approach were adopted to conduct an in-depth case study of DOUYIN. Thematic analysis of secondary data from various sources was used. The findings demonstrate DOUYIN’s innovative approach to utilising advanced algorithms, diverse content and social interactions to enhance user engagement. DOUYIN utilises machine learning techniques to create user profiles and comprehend video content. It subsequently provides real-time personalised recommendations and optimises the algorithms based on user feedback. DOUYIN also incorporates PGC-, UGC- and PUGC-generated content, supported by a creator incentive system. Moreover, DOUYIN enables interactions between users, creators and the platform through commenting, sharing and live streaming features.

The research fields of consumer behaviour and neurology are connected to the emerging subject of neuromarketing. The learning of how the human mind reacts to marketing stimulus is called neuromarketing, which integrates concepts from neuroscience and economics. It looks for the underlying brain mechanisms and affective states that shape the behaviour of consumers. Neuromarketers use methods like eye tracking, biometrics, brain imaging (fMRI and EEG) and eye tracking to try and understand how consumers make decisions, what grabs their attention and how they emotionally interact with companies, products and ads. Market grooming is the process of creating and manipulating the existing market towards a specific product, service or idea. It is the practice that helps the marketer to groom the product through various stages of marketing, be it market research, product development, advertising campaigns or creating favourable conditions for the product. All practices are performed to groom the market for a specific product, when they are combined with neuromarketing, it becomes a perfect blend for the success of product in the actual market. The study concludes that market grooming along with neuromarketing can present a significant potential for enhancing the understanding of consumer decision behaviour by increasing the validity and precision of assessing customer responses to marketing activities.