Casimir Timonen Personalization in social media marketing through generative AI: Impact on engagement metrics and brand loyalty Vaasa 2025 School of Marketing and Communication Bachelor’s thesis in Digital Marketing Bachelor’s Program in Digital Marketing 2 UNIVERSITY OF VAASA School of … Author: Casimir Timonen Title of the Thesis: Personalization in social media marketing through generative AI: Impact on engagement metrics and brand loyalty Degree: Bachelor’s degree in digital marketing Programme: Bachelor’s degree in digital marketing Supervisor: Waleed Akhtar Year: 2025 ABSTRACT: Gen AI is rapidly evolving and has introduced new capabilities towards digital marketing. Gener- ative AI has enabled large-scale personalized content creation. This study explores how Gener- ative AI-driven personalization affects user engagement and brand loyalty. The research is based on a literature review and focuses on two questions: How AI-generated personalized content influences engagement metrics, and how it impacts loyalty in social media. The research shows that well thought personalization can improve user interaction and support long-term brand relationships, while also addressing the concerns of data privacy and algorithmic bias. KEYWORDS: Personalization, social media, Generative AI, User Engagement, Brand Loyalty, Digital Marketing 3 Contents 1 Introduction 4 2 RQ1: How does generative AI personalized content influence user engagement metrics on social media platforms? 6 2.1 Social Media Engagement Metrics 6 2.2 Generative AI in Content Creation 7 2.3 Mechanisms of generative AI-driven Personalization 9 2.4 Personalization Impact on Engagement 12 3 RQ2: What are the effects of generative AI-driven personalized content on brand loyalty within social media marketing? 14 3.1 Personalized AI Content and Long-Term Loyalty 14 3.1.1 Customer Retention 15 3.2 Challenges and Ethical problems with generative AI-driven personalized content 16 3.2.1 Data Privacy and Transparency 16 3.2.2 Algorithm Bias and Over-personalization 17 4 Discussion 18 4.1 Theoretical implications & Answers to research questions 18 4.2 Practical implications 19 4.3 Limitations and future research 20 5 Conclusion 21 References 22 4 1 Introduction The constant digital developments of society have changed marketing practices, ena- bling highly customized consumer engagement (Gao & Liu, 2023). In this digital environ- ment, firms leverage personalization to make advertising more effective (Loureiro et al., 2023). Research shows that customizing messages to individual consumers can substan- tially boost engagement rates (Bleier & Eisenbeiss, 2015; Weidig et al., 2024). Social me- dia platforms offer opportunities for personalized marketing (Lee et al., 2024). Brands increasingly recognize social media important for connecting with consumers, under- standing their needs, and building strong brand communities (Bleier & Eisenbeiss, 2015; Santos et al., 2022). Recent advances in generative AI have expanded content creation capabilities, allowing organizations to produce varied and high-quality marketing at a scale (Gao et al., 2023; Heitmann, 2024). Together, these trends have created a founda- tion for generative AI-driven personalization in social media marketing (Grewal et al., 2024). Even though AI has revolutionized interactive marketing, the personalization aspect of AI marketing is still underexplored (Gao et al., 2023). Companies have only begun to ex- periment with using generative AI to personalize content, but optimal practices and im- plications are still being explored (Mogaji & Jain, 2024). The central problem of this study is to understand how generative AI can be used to create personalized social media con- tent and what effects this has on marketing outcomes, especially focusing on how AI- generated personalization influences consumer response on social media platforms. A review of the literature reveals a knowledge gap. Recent analyses show that there is a scarcity of research on applying new technologies, such as generative AI, to enhance digital marketing strategies (Islam et al., 2024). While personalization in advertising is generally known to improve effectiveness (Pfiffelmann & Pfeuffer, 2022), very few stud- ies focus on generative AI-driven personalization in social media context. Through a lit- erature review this thesis aims to fill this gap in the literature. 5 The aim of the study is to address the following research questions (RQ): RQ1: How does AI-generated personalized content influence user engagement metrics on social media platforms? RQ2: What is the impact of generative AI-driven personalized content on brand loyalty within social media marketing? The two main research questions work together to create a good understanding of per- sonalization in social media marketing through generative AI. RQ1 examines how personalized generative AI content impacts customer interactions in social media, narrowed down into metrics such as Likes, Views, Comments and shares. Exploring how users react towards generative AI-driven personalization. RQ2 examines the effects of AI-personalized content on brand loyalty in a social media marketing context. If customers have personalized interactions with a brand, how it af- fects users’ and does this help build an emotional connection towards brands. 6 2 RQ1: How does generative AI personalized content influence user engagement metrics on social media platforms? To evaluate the influence of AI-generated personalized content on user engagement within social media platforms, this section has four central themes that are considered: engagement metrics specific to social media environments, the role of generative AI in content creation, the mechanisms of generative AI-driven personalization, and effects of personalization on user engagement. 2.1 Social Media Engagement Metrics Likes as a metric show the most basic form of social media engagement, signaling user appreciation and agreement with the content. Liking content shows passive engagement, but they are important in boosting the visibility of posts in algorithm-driven feeds (Dolan et al., 2019). The amount of likes also serves as a numerical measurement of content approval, enhancing the contents social proof and credibility (Dolan et al., 2019; Bag et al., 2023). Sharing shows users’ direct approach to share content with their own peers. This metric measures how much organic reach content gathers and how well it matches the users’ interests (Gao & Liu, 2023). This effect is important in viral marketing, where generative AI-driven personalization can create content that is appealing and relevant what makes users share the post with their peers, leading to higher engagement levels and organic reach (Huang & Rust, 2021). AI tools can identify the media content that users are more likely to share by analyzing past data and user behaviors (Lemon & Verhoef, 2016; Ng et al., 2023). Designing content that people want to share doesn’t just improve organic reach, but it also builds stronger relationships between brands and their audiences, shares are a key metric for measuring the success of personalized generative AI-driven marketing efforts (Chatterjee et al., 2023; Al-Emran & Shaalan, 2023). 7 Comments give users the possibility to express their opinions and give feedback, this form of engagement is active participation, making it an metric used for evaluating how content affects the audience (Shahbaznezhad et al., 2021). Personalized AI-driven con- tent gathers more comments by addressing specific user interests and encouraging con- versational topics, which generates higher levels of engagement, which helps brands to engage directly with their audience and strengthen their relationship with users (Kumar et al., 2019). Views indicate how often a piece of content is seen, making them a fundamental meas- ure of reach and visibility. This metric can also be used as a base for other metrics to compare user engagement rates (Wang & Fan, 2023). Although views are considered a passive form of engagement, they are critical in building awareness and laying the groundwork for deeper user interactions (Singh et al., 2023). 2.2 Generative AI in Content Creation Generative AI has changed the method of content creation, especially for social media content. These systems use advanced models such as large language models (LLMs) to analyze large amounts of data to create new content (Rai & Gai, 2023). This is what makes generative AI a valuable tool for content creators. Analyzing data to create as en- gaging content as possible is the main target of commercial content creators (Zhou & Wang, 2023). Generative AI operates by recognizing patterns in data and utilizing them to create new content in response to user prompts (Ye et al., 2024). For example, models such as DALL-E can generate original mixes of text, images and various media types to assist marketers in developing new content concepts (Feuerriegel et al., 2024). This abil- ity to mix up different elements of content into something completely new is what makes generative AI appealing to content creators. These tools make brainstorming easy by of- fering many ideas to help with the cold start problem where users struggle to start with a task (Wang & Yao, 2023). 8 While generative AI is effective, it is still not perfect (Cheng & Fu, 2023). For example, some outputs might need human review to make sure the content is factual and fits the criteria of the prompts. Another problem is the risk of bias in the generated content which can cause serious issues if there is no review of the material produced by genera- tive AI (Ferrara, 2023). Even with these limitations, the idea generation process has been made faster with generative AI, especially for creative content creation work. Another way generative AI is being utilized in content creation is blog writing (Sivarajah & Irani, 2023). AI has sped up blog writing and improved productivity. With the constant development of generative AI marketers and writers have begun utilizing it to create personalized content for their customers. By analyzing large volumes of data, these tools notice the audience trends that enable the creation of personalized content with each reader group (Pesovski et al., 2024). Idea generation or drafting the first versions of blogs can be difficult with content crea- tors, generative AI can help with this with eliminating the lack of inspiration (Bilgram & Laarmann, 2023). Generative AI can suggest topics, create outlines, give recommenda- tions for text development and draft basic structure for the text, which saves time and streamlines the writing process, which is valuable in the fast environment of marketing (Kshetri et al., 2024). By analyzing readers' interests, gen AI can adjust the tone, style, or depth of a blog to suit different audiences. For example, a blog targeting younger consumers might have a casual and fun tone, while one aimed at academic people might focus on more technical details. This personalization makes the content more engaging and relevant to readers (Gao & Liu, 2023; Pesovski et al., 2024). In addition to creating content, AI can also analyze metrics such as page views, reading duration, and shares to suggest changes that enhance readability and effectiveness. In a 9 example situation, if readers of a blog have a lower engagement rate or abandon the blogs at a certain point, generative AI might help with segmenting the text into simpler parts to help with engagement by making the text easier to follow. Generative AI also can create data visualization and trend analysis that can improve productivity, these fea- tures make it a powerful tool for content marketing analysis (Kshetri et al., 2023). Even with all the benefits of generative AI, it’s to be used carefully, AI-generated content can lack creativity and can cause the content to be mediocre, making sure the content is in line with the user's plans ensures the best outcome, the best results come from com- bining AI’s efficiency with human creativity and judgment (Bahn & Strobel, 2023). As AI generated content becomes more common, the internet is flooded with content that lacks depth and actual insight (Cao et al., 2025). As content creators aim for engagement rather than actual insight the content can become incredibly generic. Reliance on AI can lead to lack of originality, where human perspectives are nonexistent and content is based on what’s the most optimal to generate clicks and engagement (Prentice et al., 2020). 2.3 Mechanisms of generative AI-driven Personalization Generative AI-driven personalization relies on several mechanisms that allow social me- dia content to be tailored to users. Basically, personalization begins with data analysis. You enter vast amounts of user data, for example former click behavior, liked content, search history and demographics to detect patterns and preferences of the user (Hard- castle et al., 2025). Through machine learning algorithms and predictive analytics, AI identifies what content users have found engaging previously and makes predictions to- wards future interests. This leads to user profiling; AI can create detailed profiles and segment users based on their interaction history. Data is incredibly important in this part, as the more data the AI is given access to, the more personalized the content can be (Alzoubi et al., 2025). In a simpler context, AI acts as an analytical machine that goes through all the data given to it and categorizes each user into segments to predict what type of content users might be interested in, which works as the foundation for 10 personalization in marketing (Alzoubi et al., 2025). AI-personalization has revolutionized interactive marketing by tailoring content across the customer journey, yielding more relevant user experiences (Gao & Liu, 2023; Kumar et al., 2024). By examining patterns in how users engage with posts or ads, AI can segment audiences and even predict what each segment is likely to respond to, ensuring that content aligns with the audience’s interests (Alzoubi et al., 2025). This mechanism is important for social media marketing because when content matches user’s interests, it draws more attention and engage- ment. This is why knowing how AI analyzes data to profile users is an important thing to explaining why personalized content often outperforms generic content on engagement metrics. Another mechanism of AI-personalization is behavioral prediction to personalize content (Madanchian, 2024). AI systems don’t just catalog past behavior, but they also forecast future behavior. Using predictive modeling, AI can predict what a user is likely to prefer next, for a practical example when predicting social media posts if the user’s view time on for example, a short video is longer on a certain theme, this information can be used to make a prediction what type of content the user has enjoyed or engaged with (Zar- rinkalam et al., 2020). This allows marketers or content creators to deliver content ac- tively. A study showed that messages generated by a large language model (ChatGPT) to match the profiles of users, these messages were found to be much more engaging com- pared to messages that were not personalized (Matz et al., 2024). By predicting the as- pects of the user’s psychology or preferences they found that personalizing the messages through AI increased the likelihood of user interactions (Matz et al., 2024). This shows that the behavioral prediction mechanism can consider each user as its own segment, compared to traditional marketing personalization where users are segmented into larger groups. For the RQ1, this is important as it focuses on causality, if the engagement rises after personalization, one of the reasons would be most likely that the Generative- AI has aligned the content successfully with user profiles. 11 Next important mechanism for AI-personalization is recommendation systems, these de- termine the content shown to users based trained algorithms (Masciari et al., 2024). Platforms like TikTok and Instagram rely on these algorithms to maintain their high en- gagement by personalizing the content feed for users. Recommendation systems pow- ered by AI continuously refine suggestions by learning from the user’s feedback (Hard- castle et al., 2025). This includes what type of content is clicked, skipped or rewatched. This makes sure that the content is always evolving with the user behavior. Recommen- dation systems are relevant to understanding engagement metrics as they directly influ- ence what content is seen by the user which affects the potential for likes, shares, com- ments and view time. And the last relevant mechanism for understanding the mechanisms of generative AI personalization is adaptive content generation (Ahn et al., 2024). In adaptive content generation generative AI creates new, specific content instantly instead of choosing con- tent previously made, this level of personalization makes content feel even more rele- vant, which increases user engagement (Matz et al., 2024). AI tools such as GPT models can deliver highly personalized content by responding prompts in real time, this has shown that to improve user attention and user engagement (Matz et al., 2024; Yuan & Liu, 2025). These mechanisms work as a feedback loop. Every user engagement whether it is clicks, scrolls, or comments feeds back into the AI, which updates the predictions and content in real time, this ability to analyze data in real time allows personalization to be effective even when user preferences change, as users perceive AI-personalized content as more relevant and trustworthy, which directly supports engagement (Teepapal, 2025). Behavioral prediction, recommendation systems, adaptive content generation and feed- back loops are important personalization mechanisms that can help us explain why users engage more with AI-tailored content (Sudenaz, 2024). These mechanisms help us 12 answer RQ1, as they directly influence how personalized AI-generated content influence engagement metrics in social media platforms. 2.4 Personalization Impact on Engagement Personalized content has shown that user engagement is improved on digital platforms by personalizing marketing messages making them more compelling to each user. In so- cial media, customized advertisements can catch the user’s attention much more effi- ciently compared to traditional one advertisement fits everyone. Recent studies support this showing that relevance drives measurable improvements in engagement metrics. For example, a study on e-commerce found that data-driven personalized content in- creased customer interactions, as users were more likely to engage with the content shown as they felt the content to be personally meaningful (Oualid et al., 2024). They found that non-personalized content had problems with keeping users engaged, where AI-generated personalization added a feeling of being cared for which led to higher engagement levels (Oualid et al., 2024). This supports the marketing principle of where consumers see content that they feel connected to, the users tend to engage more with the content which in a social media environment would be like, sharing or commenting with the content (Hollebeek & Macky, 2019). This supports the importance of personalization for social media content. With all the advances with generative AI, the potential for individual personalization has become much more accessible. Traditional personalization such as segmenting or recommendation algorithms is limited whereas generative AI can instantly create customized messages for as many users as necessary. A study showed good evidence that generative AI-driven personalization has boosted user engagement outcomes in multiple experiments, they used ChatGPT to gen- erate marketing messages towards random users’ psychological profiles (Matz et al., 2024). In multiple situations these personalized messages created by generative AI showed more influence on consumer behavior compared to non-personalized messages (Matz et al., 2024). Simplified, users that received the AI-personalized content were 13 much more likely to engage with the content compared to the people who received ge- neric content. This finding illustrates the capabilities of personalization on a bigger scale. The ability to match content to the users in social media helps to get to the right content within the large amount of content in social media platforms by prompting the users to pay attention to the personalized content. In a study where AI-personalized social media content was investigated by consumer re- sponses there were findings that personalization improved users perceived usefulness of posts (Teepapal, 2025). When content felt personally relevant, the users tend to view it as more useful and credible, which is connected to more engaging behavior (Teepapal, 2025). These positive feelings are relevant for sustaining interactive users. The study also showed some ethical considerations on how people had privacy concerns knowing that their data had been analyzed by an AI. This can be considered as conditional, where too much personalization could become uncomfortable towards users. While personalized generative AI can create a higher engagement, there still is not enough data to know where to draw the line in personalization. Personalization engagements might be fading as user preferences change (Oualid et al., 2024). As the generative AI world keeps expanding, the long-term success of personali- zation depends on adapting to new user preferences. But for now, literature strongly suggests that personalization has a positive impact on social media engagement in situ- ations where it is used carefully without being too invasive. 14 3 RQ2: What are the effects of generative AI-driven personal- ized content on brand loyalty within social media marketing? Generative AI-driven personalization has become a key strategy for fostering brand loy- alty on social media. Brand loyalty refers to a customer’s preference for a brand, charac- terized by repeat purchases and positive views on the brand (Dawes, 2022). In digital marketing loyal customers not only continue to engage with a brand’s content but also often share it and recommend the brand to others, increasing organic reach and revenue (Alzoubi et al., 2025). This is why it is important to examine how generative AI-driven personalized content influences brand loyalty in social media marketing. This section will explore the significance of brand loyalty in the digital era, How AI-personalized content effects long-term loyalty, the customer retention mechanisms that AI enables and the challenges and ethical problems with generative AI-driven personalization. 3.1 Personalized AI Content and Long-Term Loyalty Personalization has long been known to enhance marketing effectiveness (Bleier & Ei- senbeiss, 2015), and generative AI-driven personalization takes this to a new level in the social media context. By leveraging artificial intelligence, and all the mechanisms men- tioned earlier in this thesis, brands can deliver content uniquely tailored to each user’s preferences and behavior. This high degree of relevance can significantly influence long- term brand loyalty, as personalized content improves the customer’s experience and re- lationship with the brand: when consumers consistently encounter messages and offers that align with their interests or needs, they are more likely to feel an emotional connec- tion and satisfaction with the brand (Obiegbu & Larsen, 2024). In a study, they found that generative AI-driven personalization tends to enhance customer satisfaction and emotional connection with brands, which ultimately leads to higher retention rates (Tamilmani et al., 2025; Khamoushi et al., 2025). 15 When an AI algorithm gets to know a customer by learning their preferences and inter- acting accordingly creating a loyalty loop, the customer feels seen and understood by the brand, which activates positive emotions and deepens the bond with the customer, ultimately leading to stronger loyalty (Obiegbu & Larsen, 2024). It is important to still acknowledge that generative AI-driven personalization is not an instant method to greater brand loyalty. If the algorithm’s personalization attempts fail, for example, in a way where the AI-personalized recommendations are irrelevant to the user, AI-personalization has the opposite effect, leading to a de-personalization experi- ence that lowers the customer’s loyalty (Seyed & Hassan, 2025). Customers might feel disappointed if the brand appears to misunderstand them, leading them to disengage or switch to competitors. This is why the quality of the AI personalization matters greatly; effective personalization builds loyalty, but poor personalization can damage it. Overall, when done correctly, generative AI-driven personalized content is an effective tool for long-term brand loyalty by engaging customers in a relevant, individualized way to build strong emotional connections with customers. 3.1.1 Customer Retention The effectiveness of generative AI-driven personalization in retaining customers is deter- mined by the ability to create content that aligns closely with individual preferences and past interactions. This personalized approach has been linked to increased customer sat- isfaction, creating a positive brand association and reducing the likelihood of consumers switching brands (Tamilmani et al., 2025). Generative AI-driven personalization has increased the ability of marketers to keep high retention rates by delivering users’ fresh and personalized content (Rossi et al., 2025). Generative AI-driven applications, such as personalized AI chatbots, can handle cus- tomer inquiries and respond with tailored content to individual needs. This instant, per- sonalized assistance improves the user experience and boosts customer retention by 16 managing user problems fast and effectively, creating trust and long-term loyalty (Arora et al., 2025). 3.2 Challenges and Ethical problems with generative AI-driven personal- ized content 3.2.1 Data Privacy and Transparency Generative AI-driven personalization on social media requires personal data, and most users are unaware how their data is being collected and used, which raises privacy con- cerns. Research shows people object to sensitive personal information being collected for personalization and wish for transparency in algorithms (Kozyreva et al., 2021). This study also found users favor personalization only in the situation where minimal use of personal data is used, the personalization respects people’s preferences, and the per- sonalization avoids sensitive areas like politics (Kozyreva et al., 2021). Another study sim- ilarly shows a need for privacy as a default in AI marketing, addressing that real-time tracking of user behavior is common and should be ethically managed (Kumar et al., 2024). If platforms lack transparent policies or user controls, consumers may feel that their privacy is being violated. When consumers understand how AI tailors’ content and feel in control of personaliza- tion, they trust the brand more whereas a lack of transparency can create negative feel- ings towards the brand (Feng & Kim, 2025). If users suspect misuse of their data, brand credibility can suffer. Ethical guidelines for generative AI also highlight trust-related prin- ciples such as user autonomy and accountability (Hermann & Puntoni, 2024. The litera- ture suggests that social media marketers must prioritize user privacy and clear commu- nication, personalized content should be accompanied by explanations of why it’s shown, and users should be able to adjust settings, or else trust and brand loyalty may decline (Tyler et al., 2019). 17 3.2.2 Algorithm Bias and Over-personalization AI personalization on social media platforms can create ethical biases and manipulation. Algorithms learn from historical data, so if training data reflect social prejudices, the AI may recommend or target content unequally. A biased model might disproportionately show certain ads or posts to users of a particular gender, age, or ethnicity, reinforcing stereotypes (Kidd & Birhane, 2023). In this study, the authors warn that AI-based per- sonalization can prolong algorithmic bias and undermine consumer autonomy, meaning users may be steered toward content that reflects the algorithm’s biases rather than their actual preferences (Kidd & Birhane, 2023). Over-personalization is another practical problem. When algorithms aggressively tailor content, users can be trapped in repetitive content on social media feeds. For example, if an AI only feeds a user content like what they have already engaged with, the user's experience can become repetitive or stale. Research shows that generative AI-driven personalization in digital marketing often lacks ethical standards and suggests that brands risk pushbacks from consumers if content is over-personalized (Hayes et al., 2021). 18 4 Discussion 4.1 Theoretical implications & Answers to research questions This study takes part in the subject of AI-assisted digital marketing by examining how generative AI can affect personalized content on social media and how it impacts user engagement metrics and brand loyalty. AI has been vastly studied from the technological and practical viewpoints while this thesis is more focused towards the psychological and behavioral effect towards consumers, filling gap towards the current literature. Research shows that AI-generated personalized content has a positive influence on user engagement metrics in likes, shares, comments and viewers. Answering RQ1, personali- zation increases user engagement by creating more directed content with the user’s in- terests and identities. The mechanisms of generative AI-driven personalization, behav- ioral prediction, adaptive content generation and recommendation systems explain the increased interaction. The findings support that user engagement is more likely when the content is aligned with the user’s behavior and preferences in real time. The findings suggest that generative AI-driven personalization contributes to short-term engagement, long-term emotional connection and customer retention answering RQ2. Users that receive content that is relevant and appreciated by the user’s, the attachment towards the brand deepens. However, the findings also suggest that poor personaliza- tion can lead to a loss of brand trust. The success of AI-personalization depends on the accuracy of the personalization and the ethical use of data collected. 19 4.2 Practical implications This study provides multiple practical implications for people managing generative AI- driven personalization in social media platforms. Generative AI can be used to create personalized content at scale, improving personalization accuracy without the need for manual customization. With the data provided by users, generative AI can increase en- gagement through content that users can relate based on their preferences and behav- iors. Personalization is not only a technical process; the emotional and psychological effects must be considered also. Brands that successfully personalize their content towards their audience’s values and interests can improve their customer-brand relationship. To sus- tain this improved customer-brand relationship, companies must keep refining their per- sonalization strategies to match the constantly changing user behavior. Another important practical implication would be the importance of timing and platform features, engagement metrics are affected by the content and distribution, so the need for integration of personalization is important rather than considering it as an additional attribute. With the increased use of AI, it is also important to consider the ethical use of data. Over- personalization and lack of transparency can cause loss of trust in the brand. Brands must consider the balance in their personalization with transparency with how the users’ data is being used. 20 4.3 Limitations and future research This thesis offers useful insight into how generative AI can support personalization in the social media context, there are still several limitations. This thesis being a literature re- view there is a lack of empirical testing. In future research towards generative AI-driven personalization in social media, quantitative methods such as A/B testing in AI-person- alized and non-personalized content should be considered. Another limitation would be the progress in generative AI. The literature review is based mostly on literature from 2020 onwards, but some of the literature reviews base their findings on older models of LLM’s such as GPT-3. Future research needs to maintain relevance with the develop- ments in LLM’s. The thesis also focuses on the beneficial side of generative AI-driven personalization, it does not explore the potential negative effects in depth. For example, negative effects that could be exposed would be content fatigue, over-personalization. data privacy eth- ical problems and algorithmic bias. While the role of human interaction in AI-generated content was mentioned, further research on how AI-tools affect human creativity and efficiency could be researched. 21 5 Conclusion The purpose of this thesis was to investigate the effects of AI-generated personalized content on social media, with user engagement and brand loyalty. The focus being on how generative AI personalization affects the behavioral response of users. The key find- ings of the research were that generative AI can considerably enhance user engagement by personalizing content to individual behaviors and preferences through behavioral pre- diction, adaptive content generation and recommendation systems. Personalization in- creases key engagement metrics in social media. In addition, generative AI-driven per- sonalization contributes to brand loyalty by creating relevant content towards users by improving user experience which improves retention. The thesis also goes through briefly the importance of ethical considerations, including data privacy, algorithmic bias, and over-personalization, which may have negative effects towards user trust and long-term brand relationships. This thesis contributes to the growing research on generative AI-driven personalization, by applying these findings into marketing strategies marketers can understand the ben- efits and ethical and practical challenges of generative AI-driven personalization in social media. 22 References Ahn, S., Yim, H., Lee, Y., & Park, S. (2024). Dynamic and Super-Personalized Media Eco- system Driven by Generative AI: Unpredictable Plays Never Repeating the Same. IEEE transactions on broadcasting, 70(3), 980-994. https://doi.org/10.1109/TBC.2024.3380474 Alzoubi, H. M., Shameem, B., Mushtaq, S., Kurdi, B. A., Joghee, S., & Hamadneh, S. (2025). 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