When Marketing Reads Emotions: AI Personalization and the Gen Z Consumer
Companies don't just know what you want, they know how you feel too…
Within advertising and marketing communications, emotion recognition AI exposes a new opportunity in data-driven marketing, creating the desire for brands to come off as empathetic by using AI systems to interpret human emotion for data insights. “The global emotion AI market size was estimated at USD 2,137.5 million in 2024 and is projected to reach USD 13,397.1 million by 2033.” (Figure 1)(Anon, 2024).
For Gen Z consumers, who are responsive and engage online to personalised content, there is both opportunity in using emotion AI for advertising, but also real time risks in misinterpretation, exploitation, and ethical concerns, especially to a demographic that is very aware of digital privacy.
Technologies behind emotion AI
Big Data
Big Data refers to “large and complex data sets that cannot be easily managed or analyzed with traditional data processing tools, particularly spreadsheets.” (Oracle, 2021). The characteristics of Big Data include “huge amounts of data created every day and in every form, AI tools to mine that data, and effective algorithms allowing real-time or near real-time mining.” (Tsapatsoulis & Djouvas, 2019). With emotion recognition AI, Big Data enables marketers to analyse large volumes of consumer-generated content to identify sentiment patterns and emerging trends for personalized communications in real time (Figure 2).
Machine Learning
Machine learning algorithms are trained on multiple data sets of emotional expressions, voice recordings, and text analysis, which datasets are then labeled with the corresponding emotions, facilitating AI to learn the relationships between data and emotions and to predict new ones. “The machine learning segment accounted for the largest market revenue share in 2024, due to its ability to process large, different datasets with diverse data types to identify emotional signals” (Figure 1)(Anon, 2024). Machine learning provides support for hyper-personalised advertising strategies.
Feelings to insights
Emotion recognition for data-driven insights is dependent on the use of consumer-generated content and machine learning to generate analytics. Emotion AI, through algorithms, allows for analysis of “facial expressions, vocal tones, and physiological signals to decode emotional states, enhancing user engagement and creating more intuitive interactions.” (Saif Fares Al Mazrouei, 2025).
Customer sentiment analysis is “an automated way of detecting sentiments in online interactions to assess customer opinions about a product, brand, or service” (Nur Syamimi Mat Zain et al., 2024). Traditionally, this analysis is done through manual review of customer feedback or surveys, where “Sentiment analysis classifies words into positive or negative by focusing on emotions in the text” (Liu & Zhang, 2012, as cited in Park et al., 2021).
A study was conducted on Gen Z consumers towards AI hyper-personalised advertisements. The participants showed mixed emotions towards these adam where positive responses were curiosity and excitement, although there were also responses reflecting fear, suspicion and concerns for privacy (Figure 3). This suggests a positive impact on boosting engagement, but also the creation of skepticism and fear around the technology due to transparency issues and consumer trust (Peter et al., 2025).
“While Gen Z is accepting of AI use for marketing and promotions, they do have higher levels of concern regarding personal data, psychological profiling and manipulation” (Jeffrey, 2021).
Emotion Recognition in Advertising
In the advertising industry, emotion recognition AI has been used for some time. In 2009, Rana el Kaliouby, PhD, founded the emotion AI company Affectiva, which, “With a customer’s consent, the technology uses the person’s phone or laptop camera to capture their reactions while watching a particular advertisement.” (Somers, 2019). This method generates real-time feedback from the customer, allowing marketers to interpret the type of engagement they are receiving.
In 2023, Coca-Cola was able to boost its campaign engagement rate through real-time emotional data “using AI emotional analytics, analyzing facial expressions to refine their ad strategy and drive a 25% engagement surge, as documented in a Harvard Business Review case study.” (Dzine Pixel, 2025). With the technology of emotion AI, the brand could identify which elements of their advertisement were effective emotionally and which flopped, allowing them to make adjustments to their messaging, visuals, and tone. This not only increased their engagement but also helped them create more personalized experiences for different audience segments.
Opportunities for Brand in Advertising for Gen Z
Emotion recognition AI presents major opportunities for marketers in advertising. Gen Z consumers are especially engaged with digital content and personalisation. By transforming raw emotional data into actionable insights, brands can deliver personalization at scale. “As expectations for personalization rise, integrating AI into marketing strategies may create lasting customer relationships and help organizations stay competitive in a digital world.” (Zahara, 2025). Not just personalization, but emotion AI can enhance engagement and retention as well as product innovation. The potential for this is huge, but the success of it largely depends on the accuracy in interpreting emotions and integrating these insights responsibly so that consumers like privacy aware Gen Z’s aren’t turned away by it.
Predictive analytics extends the opportunities of personalization further. It is defined as “advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques, and machine learning.” (IBM, 2022). With predictive analytics and emotion AI, brands can anticipate Gen Z’s reactions and generate tailored messaging to different audience segments creating personalisation at scale. Today, especially for this particular segment, personalisation is no longer a luxury but a necessity as it builds loyalty and engagement.
In terms of enhancing customer service and retention, Emotion AI can offer real-time support enhancement, such as during calls or chats, the AI can detect emotions like frustration or confusion to coach the agent in real time on how to interact better with the customer. When dissatisfaction is detected, they can trigger personalized responses. Companies can also take advantage of emotion AI to start tracking “emotional satisfaction” of consumers to improve deeper-rooted issues. “Emotion is at the core of loyalty. AI helps you deliver more empathetic, personalized support — at scale — by recognizing how customers feel and adjusting responses accordingly.”, which helps advertisers to build better connections with the Gen Z demographic (Nice, 2026).
Risks and Ethics of Emotional Data: Bias, Consent, and Trust
While emotion AI recognition enhances personalisation, its primary purpose is to help serve business goals, often at the risk of bias, consumer privacy concerns and consent. For Gen Z’s, who are hyper aware of digital ethics, these systems present risks to them.
Algorithmic bias and brand reputation
Bias in emotion recognition comes from how algorithms are trained on unrepresentative or incorrect data (Kim 2016). Furthermore, theories of data integration highlight how heterogeneous data can amplify errors and bias if not managed carefully (Putrama & Martinek, 2024). The result of this could be “tone-deaf” communications, damaging brand reputation. So while some argue that machine training can allow for “more empathetic and engaging user experience” (Saif Fares Al Mazrouei, 2025), in the context of advertising, this empathy is a tool for emotional insights rather than for consumers’ well-being.
Integrity
Privacy is a huge concern with emotion-reading AI. Nissenbaum’s (2004) theory of contextual integrity argues that data collection must be appropriate to its specific context. This means that companies must use consumers’ data only in the exact context they have told them they will, and with explicit consent. For example Apple, limits biometric FaceID data to the device itself (Apple, n.d.), while Spotify, uses behavioural information like listening patterns solely used for personalising the customers account and tailoring music recommendations (Spotify, 2025).
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General Data Protection Regulation (GDPR)
Data privacy considerations are compulsory adherence to the General Data Protection Regulation (GDPR) is mandatory. Under Article 9 (GDPR), processing emotional and biometric data to identify people is prohibited unless there are safeguards and explicit consent (GDPR, Art. 9). The GDPR also requires data minimisation, meaning that only what is specifically needed is collected. When trying to build trust with Gen Z audiences, it is essential that brands practice data minimisation as they are a very aware segment in terms of digital data. Not only is it a legal requirement, but a way for forming trust, transparency, and brand loyalty.
Finding balance
In advertising, using emotion AI with Gen Z audiences requires a good balance between personalized insights and ethical obligations around privacy and data security. AI-driven analytics must be carefully monitored to avoid bias or misinterpretation of human emotions, especially because of the heterogeneity of this demographic.
By implementing safeguards, ensuring transparency, and adhering to GDPR requirements, companies can leverage emotion AI to build trust and deliver more meaningful customer interactions.
Ultimately, the potential emotion AI can enhance advertising personalization, customer engagement, and loyalty, but only if it is used responsibly, ethically, and with respect for consumer consent and privacy.
References
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