Understanding the role of AI in user behavior analytics and content personalization: concepts, benefits and ethical considerations

The Wall Street Journal indicated that one of the fastest growing businesses on websites is collecting data from online users⁽²⁾. Users’ data often include information about browsing history, click rate, enagement, usage time, geographic location, search history, etc.

Noticeably, user behavior tracking is a part of user behavior analytics and can be classified into twofollowing types⁽³⁾:

• Explicit tracking: this type of tracking captures users’ behavior by their active actions like providing information through registration, login, surveys, or other forms of direct interaction.

• Implicit tracking: Data is collected automatically without direct interaction from the users, such as through cookies, tracking pixels, and other tracking technologies.

• Among implicit tracking techniques, cookies is one of the most popuplar form. A web cookie is a small file or text data generated and enhanced by the server, stored by the browser. A cookie isexchanged between the browser and server during each interaction and created when a user visits a website, and the site uses them to track the user's activities⁽⁴⁾. Additionally, a tracking pixel is defined as "a 1x1 transparent image embedded in web pages, emails, or digital ads. While invisible to users, it plays a crucial role in collecting behavioral and engagement data to enhance marketing analytics and campaign performance”⁽⁵⁾.

Regarding content personalization, it can be perceived as the creation and delivery of content that is tailored to a specific user. Instead of sending universal messages to everyone, content personalization focuses on understanding each customer and creating online experiences that are suitable for them⁽⁶⁾. The relationship between user behavior analytics and content personalization can be understood as a cycle. When having users’ data for both input and output for recommendation systems, the data will be used to optimize the content displayed and generate content, thus enhancing the personal experience. During that process, AI has become a “core transformational technology” in the media industry, fundamentally changing the relationship between content producers and consumers⁽⁷⁾. AI offers incredibly useful applications in customer behavior analysis and AI-powered content personalization.

First, AI, especially machine learning and big data, are used to process a large a mount of complex data. This process helps to turn raw data into meaningful interpretation of behavioral features, building a solid bedrock for subsequent steps. AI is used to set up user behavior model through classification and clustering algorithms to distinguish between different user groups. Rehse et al (2024) figured out that models are built from data generated by user interactions logs with digital systems. For example, log analysis tools at the application layer are used to collect behavioral indicators such as access time and pattern, geographic location, device type, and number of visits. Then, these data will be cleaned, normalized, and formatted into suitable datasets to support the training of AI models⁽⁸⁾.

Generative AI, including deep learning models, can analyze a large amount of data sets and generate relevant content. Generative AI models can be trained based on large amounts of user data like browsing history, preferences, and previous interactions. These models can then help to producepersonalized content, such as product recommendations, article suggestions, or website layouts, tailored to each user's unique profile. Website content can change in real time based on user preferences, browsing history, location, and other variables⁽⁹⁾.

In addition, AI is able to analyze and determine new trends in the market, helping businesses quickly adapt and exploit new opportunities. Then AI predicts customers’ behavior and demand, thus supporting businesses to personalize experiences and improve services.

AI is also used to analyze users’ emotions and feedback, providing deeper insights of consumer satisfaction and mood. AI with natural language processing (NLP) and machine learning capabilities can analyze customer sentiment, themes, and intent from raw text data automatically. Organizations thus can quickly find out important insights^(10),(11).

User behavior analytics and content personalization with the support of AI have been widely across various sectors. On YouTube, Netflix or TikTok, AI analyzes behavior such as viewing history, interaction time, suggesting videos, movies or short content that suit personal interests. Facebook orInstagram use AI to personalize the timeline, showing content from friends or pages you are more interested in. In e-commerce, websites like Shopee or Lazada use AI to suggest products based on searches, products viewed or purchased previously, helping to increase the likelihood of purchasing and improve the user experience. Personalization also reforms the way distance education is addressed. Online course platforms such as Coursera and Udemy gather information on student progress, test scores and study time to recommend content, reading materials and pathways for each student.

2. Benefits of applying AI in analyzing user behavior and personalizing content

There have been numerous useful advantages for both content producers and users when AI is used to monitor user behavior and customize content.

First, understanding user behavior and content personalization with the support of AI improves users’ experiences.  In order to provide content recommendations that are relevant to each person's interests, AI enables the system to evaluate billions of data points from user activities. This is the key to improving user retention and satisfaction.

Netflix is a telling case study of AI in content personalization. Before AI, Netflix recommended popular movies in the same genre, a service based on straightforward algorithms that sorted movies by genre and user ratings. But the method was restrictive because it didn’t account for individual context, habits and specific behaviors. Since 2016, Netflix has been developing AI and machine learning to tailor one content personalization experience per user. Netflix utilizes content by the means of artwork personalization where for a single title, numerous image variations are passed around for the same title, and then choosing a thumbnail that appeals to a user more to suit viewing history. They gather viewing data and use machine learning models to predict which artwork will engage the user most, as well as to personalize the user interface accordingly⁽¹²⁾.

Second, AI-powered content personalization helps enhance strategic communication and marketingeffectiveness. For online platforms and advertisers, the ability to segment users by behavior and interests through AI significantly improves campaign effectiveness. According to HubSpot’s State of Generative AI report: 77% of marketers believe AI makes content personalization more effective; 96% of marketers find that personalization increases repeat purchases; 94% of marketers say personalization drives sales. There is no doubt that AI is not only saving businesses time, but also making a real impact on revenue⁽¹³⁾.

Furthermore, McKinsey and Company (2021) found that companies that excel at personalization generate 40 percent more revenue than average players. Leaders in personalization also achieve better customer outcomes. Their focus on relationships and long-term value leads to higher upselling, retention, and loyalty⁽¹⁴⁾. This means that if organizations have skillfull application of AIin content personalization, it will contribute to optimizing the effectiveness of marketing communications thanks to the ability to analyze big data instantly.

AI not only accelerates content creation rate, but also enhances content quality with deep-data analysis and daily machine learning. It helps attract compelling headlines and personalized voicetone for each audience. Thus the outcomes are a more efficient, cost-effective content creation process that delivers better content - that is not just personalized, but is also fine-tuned for maximum performance aligned with specific business objectives.

Third, AI helps foster sustainable relationship or gain more loyal users by expanding theinteraction and engagement individually. First and foremost, to reach and gain potential audiences, AI offers tools and tactics to help media companies bring in news audiences and drive deeper engagement with today’s sophisticated viewers. In the past, traditional methods of audience targeting primarily rely on demographics like age, gender, geographic,...But if only applying these methods on digital platforms, businesses often fail to accurately predict customer preferences as their behavior changes constantly. Drawing on AI-based data insights, enterprises can more accurately identify patterns and predict consumer behavior. Leveraging this AI-driven audience targeting, marketers can create more deeply personalized campaigns that reach the right audiences in a way that traditional digital communication-marketing can’t carry.

Moreover, AI can assist organizations in adapting to algorithm changes and continue connecting with their audiences, potentially expanding their reach across broader platforms such as social media, newsletters, and more⁽¹⁵⁾. AI technology enables two-way interactions with audiences through various methods. Intelligent chatbots provide instant responses and reply users immediately. Personalized experiences like real-time dashboards help deliver more relevant and customized content, enhancing user connection with the platform.

Finally, one of the greatest strengths of AI is its predictive capability. When you see the "You may also like" section on e-commerce websites, that is AI at work. AI can suggest items based on hidden behaviors that customers may not yet be aware of.  Predicting future user behavior using AI offers significant benefits for organizations. AI can figure out hidden behavioral patterns and anticipate customers’ demands and preferences before they become apparent, thus helping businesses make better decisions. This AI strength enables companies to proactively adjust marketing strategies in time, develop suitable products, and personalize user experiences. As a result, businesses enhance engagement effectiveness, increase conversion rates, while minimizing risks by making data-driven decisions that are accurate and timely⁽¹⁶⁾.

3. Risks of AI-powered content personalization and ethical considerations

While content personalization through AI offers tremendous benefits to businesses and consumers, the technology also poses risks and troubling implications that need to be carefully considered. These challenges include privacy concerns,“filter bubbles”, promoting social polarization, and other ethical concerns.

To customize meaningfully, AI systems inevitably generate, capture, and analyze masses of personal data such as browsing history, interaction behavior, preferences, identifying information. This also exposes a misuse, when security is not powerful enough. Consequence from large data breaches can bring extreme damage to users, such as identity fraud, cyber attack which can result in losing trust in experience consuming information and products in digital platforms.

The collection and analyzing activities on user behavior data, cookies for example, can constitute privacy violations, when the user is not well-informed or does not provide explicit consent for the ways users’ information will be utilized. AI can even create in-depth user profiles until they feel monitored, manipulated and uncomfortable. One study found that 84%–89% of respondents had a gap between their acceptance of personalized services and their acceptance of data collection (i.e. they were more likely to accept the service but less likely to accept data collection)⁽¹⁷⁾.

The findings of Khanh's (2025) study demonstrate that Shopee Vietnam users are capable of classifying and assessing the sensitivity of particular categories of personal data⁽¹⁸⁾.  They may be prepared to supply the standard details that are frequently asked for while purchasing on Shopee, including name, address, phone number, or email.  However, people become more cautious and guarded when sharing more private information, such location data or personal images.  Customers voice serious concerns about openness and their right to know how their personal information is handled.  The degree to which a company's privacy practices are transparent and easily understandable is reflected in people's perceptions of the security of personal information.

The “filter bubble” is noteacably one of the most alarming outcomes of content personalization in the AI age. Filter bubble is the phenomenon of users being presented with only information that fits their own world-views and biases. This results in users being exposed to information that onlymatches their view point and preferences, which reduces information diversity and diversity of perspectives.

The filter bubble also has its consequence in not only more ignorant individuals, but can also fuel social polarization. By continually exposing audiences to content that supports their existing beliefs, it undermines open dialogue and suppresses diverse perspectives within communities.Personalization systems can amplify this lack of diversity, leading to “echo chambers” effect which can lead to more polarized social patterns⁽¹⁸⁾.

Besides, consumer manipulation should be considered. Personalized AI leverages behavior data and psychological insight to deliver highly personalized content experiences that threaten consumer agency and autonomy. This also contributes to algorithmic bias, for example, AI asks whether men and women make entirely different choices but can perpetuate or exaggerate biases in training data, resulting in decisions that unfairly disadvantage particular groups of users. Thus there is a need to develop techniques for identifying and removing this bias⁽¹⁹⁾.

Finally, heavy reliance on AI algorithms for sorting and ranking the information can lead to organization loosing independence in content decision making by their own analysis. When algorithms determine what to show and what to hide, organizations rely on black boxes that readily serve economic or technical objectives rather than the true interests of their users. This declines the control on the conveyed messages that the organization has, which may jeopardize information diversity and objectivity and lessen personal control of the content receivers.

4. Conclusion

On the background of the ongoing digital technology revolution, AI now proves to be one of the pivotal technologies that reshape the way media platforms acquire, processes, and utilizes user behavior data to recommend personalized content.

AI has shown positive impact content personalization and shape customer engagement. This ability to interpret behavior, predict potential need, and produce ever-relevant content in a timely manner allows businesses to save in costs, see better conversion rates, and enjoy better user retention. Specifically, AI also enhances two-way communication with users and platforms via intelligent chatbots, interactive context, and real-time personal experiences.

However, in addition to these significant pros, this paper also poses some significant risks of applying AI in the field. Questions regarding privacy, personal data security, the danger of “filter bubbles” that restrict the diversity of information, and ethical issues around transparency and fairness in the use of AI are all major questions challenging policymakers and technologists.

Therefore, along with applying AI systems for personalized content, the adherence to ethical principles, transparency about data used for training and analysis, and their usage should be closely adjusted. Businesses and developers should strictly comply to privacy laws with rigorous trial and make a commitment to being transparent with users about how their data would be used, thus trust relationship can be built on a fundamental ground. It is of equal importance for tech companies to develop fair algorithms that mitigate bias and steers clear of the over-reliance on AI for content decisions in order to maintain diversity of information.

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