In the pursuit of the perfect digital service, interfaces are increasingly adapting to each user, taking into account everything from their habits to their current mood. Websites and apps strive to anticipate users' desires, offering exactly what they need. This hyper-personalization can provide a sense of comfort and care, or it can lead to feelings of algorithm fatigue, surveillance, and irritation. Dmitry Yakovlevsky, the founder of the international design laboratory Twenty Five and a filmmaker, discusses the fine line between caring service and intrusive control.
The promise of perfect convenience
Imagine an interface that seems to be reading you. In the movie "Minority Report," Tom Cruise's character encounters advertising screens that recognize him and address him by name, offering products that perfectly match his tastes. While this may seem like science fiction, it is now a reality. Hyperpersonalization involves tailoring the user experience to the individual in real-time, with apps adapting content, design, and recommendations based on your actions, context, and even biometrics.
When this personalization works correctly, it really improves the user experience. An app can suggest the right feature at the right time, a store can recommend a dream item, and a news feed can filter out noise. Users feel understood and valued, and they enjoy the feeling of being seen and heard.
It's no surprise that personalized services are gaining traction. According to Deloitte, around 75-80% of consumers are more likely to purchase from brands that offer personalized content. By 2025, a customized approach has become the default expectation, with users expecting a personalized experience from brands and expressing dissatisfaction if they don't receive it. Businesses have responded to this demand, and hyperpersonalization has become a key strategy for enhancing customer loyalty and engagement.
How algorithms adapt to us
Modern hyper-personalization technologies aim to take into account not only the user's habits and interests, but also their current emotional state, behavior, and even the external context. This allows for the creation of content that is tailored to the specific needs and preferences of each user. There are several key methods that systems can use to make assumptions about the user and adjust the content accordingly.
- Analysis of biometric data
Using the device's camera or wearable gadgets (smartwatches, fitness trackers), systems can capture facial micro-movements, heart rate, stress levels, and other parameters. This allows for the assessment of not only basic emotions (joy, sadness, anxiety, etc.), but also the overall physical state of the user. By leveraging this real-time data, interfaces can be customized to provide more personalized recommendations and content.
- Speech characteristics recognition
Intonation, pauses, tone, and speed of speech can tell us a lot about how a person is feeling. Voice assistants analyze speech to better understand the user's emotional state and provide more appropriate responses. For example, Amazon incorporates algorithms into its devices that detect the emotional tone of speech to enhance user experience with empathy and adaptability.
- Behavioral analysis in the interface
Changes in user behavior (typing speed, erratic cursor movements, frequency of switching between windows and other interactions with the application) may signal their emotional state (for example, frustration, fatigue, or stress). A study by Esa Unggul University (Indonesia) has shown that user behavior can be used to accurately predict their moods and even expectations from the interface. This allows interfaces to adapt and offer the best solutions at the right moment.
- The peer-to-peer method
Algorithms can analyze the actions of other users who have similar characteristics (age, interests, and habits). For example, if an algorithm notices that a large number of users with similar profiles are listening to specific music, reading articles on a particular topic, or watching certain videos, the system may suggest that you might be interested in similar content. This approach is commonly used in streaming services like Spotify, which uses peer-based models to create "emotional" playlists or mood-based recommendations.
- Contextual data analysis
Some algorithms take into account external factors (time of day, day of the week, weather or news events) to create the most relevant content. For example, if it is rainy outside, algorithms can offer calm or melancholic music, and on a holiday day, more cheerful and energetic tracks.
- Analysis and forecasting of needs based on previous actions
Algorithms can predict a user's future needs by analyzing their previous actions. For example, if a user regularly purchases sports products, the system may start suggesting new offers related to an active lifestyle. In the case of news apps, algorithms can recommend articles based on the user's previous interests, creating a personalized flow of information that aligns with their preferences. This enhances user engagement and satisfaction with the interface.
The reverse side of algorithms
However, there is another side to the coin. The constant adaptation of the interface to your needs can lead to overload and fatigue over time. Users may feel that their every action is being tracked, predicted, and used for recommendations, which can become overwhelming. This has even led to the concept of "algorithm fatigue," which refers to the mental exhaustion that can result from prolonged interaction with recommendation systems.
Researchers have identified several reasons for this phenomenon. Firstly, it is the isolation of the information bubble: when algorithms endlessly push similar content, the user gets tired of monotony. Secondly, it is the lack of transparency: people get tired of not understanding how and why the system makes decisions. Ironically, the better the user understands the principles of algorithms, the more irritated they become: knowledgeable users notice the intrusion faster, and fatigue builds up more quickly. As a result, instead of convenience, there is an effect of oversaturation, where recommendations begin to be ignored or cause resentment. Indeed, there is a direct correlation: when people experience algorithmic fatigue, they are more likely to resist and reject the system's suggestions.
In addition to fatigue, excessive personalization can make users feel like their privacy is being violated. When an interface knows too much about you, it can create a creepy feeling, as if someone is watching you. For example, if a music app suddenly adjusts its playlist based on your current mood, sensing whether you're feeling sad or happy, it can be both impressive and slightly unnerving. In the marketing world, there are cases where hyper-personalization can become hyper-creepy. The line is very thin: an algorithm that tries to help can easily become manipulative.
The use of AI to subtly influence consumer behavior blurs the line between care and exploitation, risking to touch upon a person’s vulnerabilities. Simply put, an intrusively personalized service risks to be perceived no longer as a convenience, but as a hidden control of the user. Such excessive care is fraught with loss of trust and loyalty: once people feel a loss of autonomy, they leave — and it is almost impossible to regain their trust.
There's also an interesting effect known as "authenticity fatigue." When consumers encounter perfectly curated and flawlessly designed interactions, they may feel a sense of unnaturalness. In 2024, experts observed a growing dissatisfaction with overly polished and refined digital experiences that lack spontaneity and "humanity." Users may yearn for more spontaneous and imperfect interactions that bring a sense of authenticity. Paradoxically, excessive use of algorithms can discourage interest, even if everything is formally designed for the user.
Neutral design as a respite
Against the backdrop of over-personalization, there is a growing interest in more neutral design approaches. Many users sometimes just want a stable, straightforward interface that is the same for everyone, without endless adjustments to their profile. This neutral design provides a sense of control, as nothing changes unexpectedly, and there is no need to worry about the system making assumptions about you behind the scenes. It is no coincidence that there is a resurgence of demand for a chronological feed instead of an algorithmic one on social media, as it is often easier for people to decide what to read themselves rather than trusting a hidden algorithm. Some services are introducing options to disable personalized recommendations or privacy modes that allow users to temporarily "escape" from the bubble. Digital detox now includes not only avoiding gadgets, but also avoiding intrusive algorithms — users purposefully limit personalization for themselves in order to take a break from information overload.
Brands are also noticing that a neutral design can attract an audience that is tired of total personalization. Simple and straightforward interfaces without excessive recommendations evoke nostalgia for a time when apps were more tools than companions. Of course, few people would dare to completely abandon personal settings, and they don't need to. The key is to strike a balance by giving users the option to choose when they want a personalized experience and when they want a standardized one. Ultimately, a hybrid approach may be the best solution: the interface is neutral and universal by default, but it can be customized to meet the user's preferences in important areas.
Trust and privacy are worth their weight in gold
The key factor that defines the line between care and control is user trust. By personalizing the experience, a company inevitably collects and analyzes user data. If this happens in a covert or excessive manner, trust is undermined. This triggers a sense of privacy anxiety in the user's mind: "How does the app know so much about me? Is it spying on me?" In an era of data breaches and increasing regulation, it is vital for brands to demonstrate that they respect their customers' privacy.
Users are willing to share information about themselves only if they see a clear benefit and improvement in communication. Simply put, they are not opposed to personalization, but only under the conditions of transparency and value. Moreover, trust is significantly increased when people are given the opportunity to participate in decision-making: they are openly asked for permission, the purpose of the data is explained, and the benefits for the user are highlighted. The concept of transparency is no longer an abstraction; it is a practical tool. According to the CMSWire review, companies should clearly explain how and what data they collect and request informed consent from users. This is not only a legal requirement, but also a basis for maintaining trust.
Thus, the line between convenience and privacy violation is drawn along the line of user control over the situation. As long as the user feels that the personalization is working for them (solving their problems), it is perceived as a sign of care. However, if the user suspects that the system is working on them (manipulating or using their data for its own purposes without clarity), it creates a sense of control and surveillance. It is important for brands to constantly ask themselves if they have crossed this line. Is their concern turning into intrusion?
How not to overdo it: recommendations for brands
To ensure that hyper-personalization remains a benefit rather than a problem, companies should follow a few principles. Here are some recommendations for implementing a personalized approach without going overboard.
- Transparency and honesty. Be open with users about what data you collect and why. Honest explanations and demonstrations of benefits increase willingness to share information. Avoid hidden practices – secrecy breeds mistrust.
- Put the control in the user's hands. Allow them to customize the level of personalization. Implement switches or modes ("personal/neutral") that allow users to disable recommendations, select content types, or prevent the collection of certain data. When individuals have choices, they feel in control rather than being manipulated by an algorithm.
- Minimization and relevance. Collect and use only the data that is really needed to improve the experience. Personalization should be relevant: it is better to show a little less, but in the right place, than to over-adapt every user's whim. Algorithms that overwhelm people with pseudo-care at every turn are more likely to cause irritation.
- Variety instead of a bubble. Even if you know your customer's preferences, introduce something new from time to time that is outside their usual range of interests. This expands their experience and prevents them from becoming bored with repetitive content. By providing a diverse range of content and using transparent algorithms, you can reduce the effect of algorithmic fatigue and maintain the user's interest.
- Ethics and security. Consider personalization from an ethical perspective: do not cross the line in sensitive data (health, finance, etc.) without explicit consent. Use data responsibly and keep it secure. Respect for privacy is an integral part of customer care.
Ultimately, hyperpersonalization is a powerful tool that can make interacting with technology truly comfortable. An interface that understands and anticipates can become a digital assistant for the user. However, it's important to remember that, like any form of care, personalization should be done in moderation. The best brands find a balance by being an invisible helper rather than an overbearing supervisor. It's the balance between being attentive and respecting the user's boundaries that determines whether a hyperpersonal interface becomes a source of joy or exhaustion.
To read the full article, click on the link: https://www.forbes.ru/tekhnologii/536324-licnoe-delo-gde-prohodit-gran-mezdu-zabotoj-o-pol-zovatele-i-kontrolem-algoritmov