Data Protection in the era of AI-powered marketing

Por: Paola Cardozo Solano - PhD Researcher & Lecturer. Vrije Universiteit (VU) Amsterdam, The Netherlands

About this series:

Aiming to better connect with their audiences, companies are relying more on Artificial Intelligence (AI) to understand consumer preferences and predict their desires and behaviors. After all, the ultimate goal of marketing is to influence consumer behavior, so it is not surprising to see the rapid adoption of AI systems to support this objective. AI’s vast data-processing capabilities make it possible to deeply understand current and potential customers, allowing businesses to tailor products, offers and strategies. However, this new approach to marketing raises important questions about its impact on our fundamental rights and freedoms.

This series of short articles on AI-based marketing for the Intellectual Property Department blog is based on the research conducted during my master’s studies in Law and Digital Technologies at Leiden University in The Netherlands. My aim in sharing these publications is to invite reflection on the use of AI systems in marketing, particularly due to their potential adverse impact on individual autonomy and rights such as privacy, intimacy, and personal data protection. My research approaches the topic from a European perspective and takes the General Data Protection Regulation (GDPR) as a normative reference for analysis.

This series examines how AI is transforming marketing and the significant challenges it presents for data protection and privacy. It explores the ways AI tools—specifically, two prominent applications in marketing: chatbots and Customer Relationship Management (CRM) software—interact with the GDPR, with a focus on the role of data protection by design. Additionally, insights from a survey of fourteen leading CRM and chatbot providers will be shared, assessing the tools and features they provide to support data protection-compliant practices. Finally, this series will address the limitations of the GDPR in providing effective protection for data subjects in the context of AI-based marketing.

First edition. Approaching AI-Driven Marketing

The need to protect data subjects’ rights is more stringent than ever. Legal scholars warn that “human dignity itself is at stake in data-driven marketing” (Henne, 2022, p. 203), as people’s decisions are strongly influenced by sneaky practices that exploit their personal information to monetize content or promote the purchase of goods and services. Artificial intelligence (AI) intensifies those concerns as it has the capacity to analyze immense volumes of data, making it possible to hyper-target individual consumers like never before (Hoffmann et al., 2013). AI marketing practices are potentially harmful to mental privacy (Malgieri, 2021), cognitive autonomy, the right to form opinions, and even to human rights and democracy (Committee of Ministers, 2019).

The “Shelter Pet Project,” a real-life example, serves as a compelling demonstration of how these strategies operate and the profound intrusion they pose on individual autonomy and freedom. In this initiative, designed by The Humane Society in partnership with AdTheorent (a digital advertising company), the aim was to stimulate the adoption of 2.4 million pets in shelters. To achieve this objective, AdTheorent identified the audiences who had previously visited theshelterpetproject.org. Then, using its AI targeting tool, it comprised 1.3 trillion connections to map the real-world relationships of individuals who had shown interest in pet adoption, directing advertisements to households, family members, and friends, with the goal of influencing the core audience through their connections (AdTheorent, 2019).

This case exemplifies how AI can provide unprecedented possibilities for companies and marketers to extract highly valuable insights from heterogeneous and enormous amounts of data, ranging from IP addresses, site pixel data, and location data to cookies and interactions on social media. AI uses large volumes of data as raw material for generating predictions, consumer behavior analysis, recommendations personalization, content automation, audience targeting, and for creating chatbots. It reduces companies’ costs while offering high success rates in engaging or maintaining audiences. Moreover, it benefits users as they will have a better customer experience, reducing search costs.

As noble as the purpose of encouraging pet adoption is, many concerns arise about the means used to drive audience behavior, involving harmful consequences to fundamental rights such as the rights to privacy and data protection. This brings us to the serious legal and ethical drawbacks AI-driven marketing entails, such as excessive collection of personal data, profiling, surveillance, arbitrary exploitation of personal information, discrimination, and processing of sensitive data or special categories of data without consent.

Authors like Vlačić et al. (2021) argue that the success of AI relies on how strongly ethical principles such as transparency, justice, fairness, privacy, data protection, and employment opportunities are respected. These principles will also have a significant impact on AI adoption rates in the upcoming future. One of the responses that have emerged to provide a timely response to consumers and facilitate risk mitigation is the privacy by design model, which is considered optimal to adopt privacy from the system’s design stage rather than as an afterthought.

Before examining the relationship between data protection—particularly, data protection by design—and AI in marketing, it is essential to understand the intricacies of each field and how they intersect. Becoming familiar with the potential of this emerging approach and analyzing how data is processed to meet marketing goals is critical.

Understanding Marketing and AI
Although often confused, marketing and advertising are two different concepts. Marketing is the genus, while advertising is one of its species; in other words, advertising is a component of marketing and one of the tactics companies use to promote their products and services. Marketing is the “process responsible for identifying, anticipating, and satisfying customer requirements profitably” (The Chartered Institute of Marketing, 2015, p. 3). The marketing discipline seeks to determine consumers’ needs and influence their behavior (American Marketing Association – AMA, s. f.).

Data enables marketing to gain insights into individuals at cognitive, emotional, and behavioral levels, allowing companies to engage with consumers’ thoughts and feelings, thereby seeking to form a long-lasting bond with them (Martin & Murphy, 2017). Without a doubt, data is the best input for achieving the purposes and promises of marketing since “[b]y using data we can offer the right product, right value at the right touchpoint to the end-user [using the] right approach” (Quach et al., 2022, p. 1314).

The drive to deeply understand consumer behavior has fueled a widespread adoption of analytics in marketing (Martin & Murphy, 2017), enabling companies to better interpret and anticipate consumer needs. This shift leads us to the role of AI. The seminal work of McCarthy et al. (1956) defines AI as making a machine behave in ways that would be considered intelligent if a human were behaving similarly. AI can be designed to exhibit several forms of intelligence, ranging from physical and mechanical to thinking and even feeling (Huang & Rust, 2021).

More recent definitions consider AI as machine-based systems that operate with varying levels of autonomy, generating outputs such as predictions or recommendations to influence the environment and achieve specific goals defined by humans (OECD, 2019). Machine Learning (ML) is a subset of AI used to build systems that learn or make predictions based on data (De Mauro et al., 2022). ML focuses on algorithms “that computer systems use to perform a specific task without being explicitly programmed” (Mahesh, 2019, p. 1). In other words, AI systems are designed to perform tasks that ordinarily require human intelligence, such as speech recognition and emotion detection.

Natural language processing (NLP) is built on insights derived from disciplines such as linguistics, mathematics, computer science, and psychology, aimed at analyzing text (Kibble, 2013). One of the NLP applications supports conversational agents like chatbots, which are understood as programs capable of carrying on conversations mimicking extended or specific human-human interactions (Jurafsky & Martin, 2008).

The capabilities of AI are particularly beneficial for predicting and influencing consumer behavior, which is why the emergence of AI marketing is not surprising. The application of AI in marketing is not only happening currently; its scope is astounding. In 2019, AI was utilized in 80% of digital advertising (IAB, 2019). Marketing has embraced AI, pushing the boundaries to achieve unprecedented levels of customization, measurability, and consumer interactivity (Piñeiro-Otero & Martínez-Rolán, 2016). According to Ma & Sun (2020), AI and ML algorithms have proven effective for processing text, image, audio, and video data on a large scale, making them ideal for the increasing complexity of products and services. Tasks such as segmentation and generating recommendations or predictions that would take a human weeks, months, or even years can now be performed rapidly and efficiently (Huang & Rust, 2021, as cited in Pitt et al., 2021).

The AI revolution in marketing
AI brings an enormous set of benefits and functionalities to the marketing discipline. For instance, AI capabilities can assist in consumer behavior analysis, business predictions, personalized recommendations, website customization, content automation, audience targeting, and facilitating company-customer interaction through chatbots. In addition, AI helps reduce costs for companies while delivering high success rates in engaging and retaining audiences. Moreover, it benefits users by enhancing their customer experience and minimizing search costs. Here is an overview of the primary ways AI is integrated into marketing initiatives:

• AI-based Customer Relationship Management (CRM): AI helps create long-term relationships with customers predicting their behavior using demographic and psychographic variables (Vlačić et al., 2021). For example, “advertisers can use AI to identify the consumers who would be most receptive to campaigns and have a high lifetime loyalty value for their brand” (IAB, 2019, p. 10).
• Communication with potential and actual customers: chatbots make efficient communication, engagement, and customer support with large numbers of customers offering a 27/7 availability and a low error margin (Davenport et al., 2020) (James, 2021).
• Analysis of consumer behavior: AI-enabled analytics can process complex data and transform it into marketing insights providing personalized, segmented, and targeted recommendations related to products or services (Vlačić et al., 2021). Predictions point to a transition where AI will help retailers “to identify customers’ preferences and ship items to customers without a formal order, with customers having the option to return what they do not need” (Davenport et al., 2020, p. 2).
• Content and advertisement generation: AI systems can automate ad creation which is now usual in social media. Natural language processing and generation can “write ad copy that performs as well or better than a human-written copy” (IAB, 2019, p. 9).
• Marketing intelligence or Business intelligence: marketers can have more accurate results for product reputation management, pricing strategy, competitor analysis, and community dynamic analysis (Aslam et al., 2021).

The impact of AI can be observed in two key aspects of the marketing discipline: the ‘Marketing Process’ and the ‘Marketing Mix’. The Marketing Process consists of five steps that businesses follow to develop strategies for attracting and retaining potential customers:

Figure 1. The 5-step Marketing process. Adaptation of Gomez Albrecht et al., 2022, p. 15.

Now, once the strategy is defined by following the steps of the marketing process, the Marketing Mix comes into play, determining how a business can employ tactics to persuade potential customers to make purchases of its goods or services (Gomez Albrecht et al., 2022). The Marketing Mix relates to the 4 P’s of marketing: product, price, promotion and place (Gomez Albrecht et al., 2022). Figure 2 showcases the impact of AI on the Marketing Mix:

Figure 2. Elements of the 4 P’s of Marketing – Marketing mix. Adaptation of Jarek & Mazurek (2019).

In summary, the transformative power of AI in the marketing discipline is evident, as this suite of technologies has boosted its strategies, enabling brands to reach audiences more effectively.
The vast majority of the AI examples provided involve the processing of a large amount of customer and potential customer data on a large scale, involving both B2C and B2B segments. Notably, then, marketing “has become a data-centric ecosystem” (Saura, 2021). Decisions on every step of the business strategy can be impacted by this set of technologies all along the customer journey, from the search stage to the loyalty relationship with the brand (Saura et al., 2021).
Today, more than ever, it is crucial for these software solutions, extensively utilized at every stage of marketing, to ensure compliance with data protection legislation, given the vast amount of personal data they process and the growing expectations for transparency from consumers worldwide. Thus, the developers of AI-based solutions for Marketing must offer GDPR-compliant solutions that guarantee data subjects transparency, the possibility to exercise their rights, and exercise control over their information.
Stay tuned for the next edition of this series, where the critical concerns surrounding AI Marketing and data protection will be introduced. The upcoming post will focus on the tensions between AI Marketing and the principles of the GDPR, examining how these concerns arise not only in B2C interactions but also in the less-explored realm of B2B interactions.

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