Modern marketing increasingly relies on artificial intelligence as a tool for understanding consumer behavior and emotions. Algorithms can analyze huge amounts of data in real time, revealing hidden patterns in audience preferences and reactions. The question arises: Can AI “feel” and understand consumers better than a real human marketer, leveraging their empathy and intuition? In this study, the MIM:AGENCY team will analyze the capabilities of AI in behavioral analysis, compare the abilities of algorithms and humans in recognizing emotions and motivations, and explore the general application of AI.

AI at the service of marketers: consumer behavior analysis

One of the key functions of artificial intelligence in marketing is data-driven consumer behavior analysis. AI algorithms can quickly process large amounts of customer information – from demographics and purchase history to online activity – to identify trends, patterns, and audience preferences. Machine learning and predictive models make it possible to build a 360° customer profile and understand their journey: what they are looking for, what they respond to, and why they choose one product over another.

In practice, this is implemented through web analytics tools: services such as Hotjar or Smartlook build heat maps of clicks and scrolls, and AI automatically highlights areas of increased attention and even points of “rage clicks” – places where the user clicks repeatedly out of frustration.

Another important role of AI is hyper-personalization of marketing. Algorithms can segment audiences much more accurately than traditional methods. Instead of large generalized groups, they build micro-segments or even target a “single audience.” By analyzing the digital footprints of each customer – search queries, social media reactions, purchase history – AI is able to automate the “understanding” of individual needs. As noted by Kyivstar Business Hub, the use of AI has finally made it possible to implement an approach where an algorithm analyzes a specific person’s purchase and viewing history and offers exactly what they need at that moment.

Personalized content has now become the norm: 71% of consumers expect a brand to interact with them based on their personal needs, and as many as 76% are disappointed when this does not happen.

AI also predicts behavior. Thanks to predictive analytics, marketers can anticipate what a customer will do next and plan their strategy accordingly. Algorithms trained on historical data predict the likelihood of customer churn, the optimal time for re-engagement, or the most promising products for upsell. McKinsey experts note that predictive analytics using AI has moved from being a trend to being a necessary tool for business competitiveness.

So, the key capabilities of AI in consumer behavior analysis can be summarized as follows:

  • Big Data processing and insights – AI quickly analyzes large amounts of customer data, identifies patterns, and generates deep insights that are not available with manual analysis.
  • Hyper-personalization – algorithms automate the individual approach: from product recommendations to dynamic website content for each visitor.
  • Prediction – AI models future consumer actions (purchases, churn, campaign response) and suggests optimal solutions to marketers based on probabilities.
  • Understanding the emotional component – by analyzing tone, language, and behavioral signals, AI assesses the emotional state and needs of the audience, which is especially valuable for emotional branding.

Humans vs. AI: who is better at “reading” emotions and motivations?

Humans have traditionally been considered irreplaceable in understanding emotions – after all, empathy, intuition, and emotional intelligence (EI) are unique to us. It would seem that computers cannot feel another person’s experiences as deeply. However, recent studies cast doubt on this assumption. In 2025, researchers from the Universities of Geneva and Bern tested popular language models (GPT-4, Claude, etc.) on standardized emotional intelligence tests – and got an unexpected result.

AI models performed better than the average person: they chose the “emotionally intelligent” response in 81% of cases, while humans did so in only 56% of cases. In other words, at least in the test format, the algorithm was able to offer an adequate emotional response to the situation more often than the human volunteers did.

At first glance, this is sensational: AI “understands” emotions better than we do, as confirmed by the test scores. But experts urge us not to take these results literally. First, the EI tests were in the form of multiple-choice questions (questionnaires with suggested responses), and the AI could simply find the statistically correct answer without feeling it. People themselves often disagree in their interpretation of other people’s emotions, so “outperforming” a person in a test does not mean having a deeper empathetic understanding.

So, who is better at understanding the consumer – a person or a machine? The answer, it seems, lies in a competent combination of their strengths. Here is a brief comparison:

  • Intuition and context vs. data and scale. People have contextual understanding and can read social cues, irony, and humor. AI, on the other hand, works with large data sets, demonstrating stability and a lack of emotional fluctuations.
  • Creativity vs. algorithmization. Humans are creative and able to adapt to unique situations. AI is limited by training data and acts according to probabilistic models, although it can discover non-trivial correlations.
  • Live vs. simulated empathy. Humans are capable of genuine empathy, while AI only imitates emotion, which sometimes makes communication mechanical. In delicate cases, live communication remains more effective.
  • Biases and errors. Humans are prone to subjectivity, while AI is prone to statistical errors related to data quality. Therefore, human supervision and model correction are necessary.

The optimal strategy is synergy between humans and AI: algorithms collect facts and quickly generate recommendations, while humans check how well they correspond to the real, deep needs and emotions of the audience.

Emotional branding and personalization with AI

Emotional branding aims to establish a deep connection with the consumer through feelings – to inspire trust, loyalty, and a sense of belonging to the brand’s story. Artificial intelligence is actively entering this field, helping brands to fine-tune their communication to the emotions of their audience. The main idea: AI allows brands to better understand their consumers and tailor their offerings to their needs, which ultimately forms a stronger emotional connection and long-term loyalty. According to surveys, more than 80% of companies worldwide plan to implement artificial intelligence in their branding strategies, recognizing its importance in maintaining competitiveness.

How exactly can AI enhance a brand’s emotional impact? First and foremost, through personalization of content and experience. A classic example is the streaming service Netflix, which uses recommendation algorithms to retain users by offering them exactly the movies and TV shows they will like.

AI helps marketers and brand managers better understand which emotional triggers work on their target audience. For example, by analyzing large volumes of commercials, it can be found that humor in videos or nostalgic music significantly increases viewer engagement. Research by Realeyes showed that car ads that appeal to the senses through stories and jokes (such as the Volkswagen ad “The Force”) received a much higher emotional response and more positive comments than the purely informational Ford Fiesta ad.

Another trend is interactive campaigns with Emotion AI. Brands are experimenting with engaging consumers in games where their own emotions become part of the experience. For example, Coca-Cola recently launched a promotional campaign called “Transform your feelings into art,” where users interacted via webcam: AI recognized facial expressions and generated unique digital art based on those emotions.

In this way, consumers literally saw their “mood” in the creative reflection of the brand, which created a wow effect and emotional attachment (since the result was associated with the person’s personal experiences). In Portugal, McDonald’s used a similar idea: interactive billboards with a built-in “Mood de Mac” camera tailored advertisements to the mood of people passing by – AI categorization of faces determined whether a person looked happy, surprised, or neutral, and the content of the ad changed accordingly.

Thanks to AI, brands can also control the emotional tone of their communication at all points of contact. For example, AI can analyze thousands of outgoing brand messages (posts, emails, support responses) and determine whether their tone matches the desired image (friendly, inspiring, expert). If the tone shifts somewhere, the algorithm will notice and notify the team. There are already integrations where a “make response friendlier” button has been added to the CRM for customer support: when clicked, the text of the letter is run through the ChatGPT language model, which rewrites it in warmer tones.

At the strategic level, AI is becoming part of brand planning. Decisions that were previously made based on the marketer’s intuition are now backed up by data and modeling. For example, when choosing which values to associate with the brand in a new campaign, you can analyze social networks: what excites the target audience, which emotional triggers (environmental responsibility, nostalgia, competitive spirit) resonate most strongly. Neuromarketing, complemented by AI, is also evolving—measuring unconscious reactions (pulse, brain activity) to branded content with subsequent AI analysis helps to choose the optimal colors, musical tones, and words that resonate with the subconscious of consumers.

Armed with such data, marketers can convince top management to invest in the emotional component of branding, proving its ROI.

AI in UX design: tracking emotions and improving experience

AI becomes a useful ally here, helping to make the digital experience more responsive to human conditions and needs.

AI algorithms can monitor user behavior metrics and detect signs of frustration or excitement. We have already mentioned “rage clicks” – repeated clicks that indicate frustration (for example, when a user clicks on an inactive element, hoping that it will work). Previously, UX researchers manually searched for such patterns in session recordings. Now, some AI analytics services themselves highlight areas with a lot of “rage clicks” and even draw conditional areas on the site map based on user engagement.

Dynamic interface personalization improves UX with AI. Websites and applications can tailor content to user interests in real time. If the system “sees” that a visitor was looking for jackets on the site, the next time they visit, they will immediately see a banner with a new collection of discounted jackets on the home page, replacing the standard universal banner.

Of course, such systems must be implemented carefully and ethically, with the permission of users, so as not to violate privacy. But the direction is interesting: AI + UX = a proactive interface that adapts itself to a person’s mood and needs, instead of requiring the person to adapt to the program.

In summary, AI in UX design is about data, personalization, and responsiveness. Algorithms analyze how we use a product, where obstacles or negative emotions arise, and help designers quickly improve the experience. In the future, interfaces themselves will become “smarter” – they will have elements of empathy, albeit artificial.

Personalized advertising and AI campaigns

Advertising enhanced by artificial intelligence allows marketers to achieve a new level of accuracy and effectiveness in interacting with their audience. This applies to both media buying (purchasing ad impressions) and the creative part – creating messages.

In terms of advertising planning and targeting, AI has already become indispensable in the industry thanks to programmatic technology. Programmatic advertising is when algorithms bid in real time for the purchase of each banner or video clip online, determining the optimal price and audience. AI analyzes a bunch of parameters – the context of the page, the user’s demographics, their behavior – and decides whether to show this particular ad to this particular person and how much to pay for it. As a result, advertising becomes much more targeted and less wasteful: the budget is spent only on those people who are highly likely to be interested in the product.

AI also optimizes the display time: for example, if it finds that a particular user interacts most with the app in the evening, the algorithm will assign them push notifications in the evening hours to maximize the chance of a click.

In terms of creating advertising content, AI has opened up the possibility of large-scale personalization of advertising. Previously, the most that resources allowed was to create 3-5 ad variants for several large segments (for example, separate creatives for men and women). Now, algorithms are capable of generating hundreds of creative variations on the fly: substituting different product images, changing the text to suit the interests of a specific user, and even adjusting colors or design elements according to the preferences of the segment. Such dynamic creatives significantly increase the relevance of advertising.

Example:

In everyday marketing, more practical things also bear fruit. Take personalized mailings, for example. Everyone has received an email addressed to them by name at some point – this is a simple level of personalization. But today, this is not enough. Brands are going further: they generate individual content for each email depending on the customer’s profile. For example, in its Blush Launch campaign, British cosmetics brand Benefit divided its audience into segments (regular buyers, VIPs, and those who signed up for the waiting list for new products) and sent different series of AI-generated emails to each segment. During the launch, AI tracked customer actions (who from the VIP segment had already bought something, who hadn’t) and sent them appropriately tailored emails with next steps. The result was a +50% increase in CTR (clicks) compared to a regular mailing.

Another area is chatbots in marketing and sales. Whereas chatbots used to be just a support tool, they are now a full-fledged marketing channel. An AI bot can act as a consultant, salesperson, and even an element of entertainment for the user. In e-commerce, chatbots recommend products just as well as salespeople: they gather information about the customer’s tastes through a few questions and offer a range of products that suit them.

We talked more about assistants in the article: “What will happen to marketing in the era of AI agents?”

Ukrainian experience: AI cases in marketing

Ukrainian businesses are not standing aside from the global trends in AI implementation. Despite the challenges of recent years, companies in Ukraine are actively experimenting with AI in marketing – from automating routine tasks to creating creative personalized solutions. According to a 2023 survey, Ukrainian companies most often use AI services such as ChatGPT (for text generation) and Midjourney (for image creation) in their work.

The Ukrainian market confirms the global trend: artificial intelligence in marketing is moving from the experimental stage to everyday practice. Many solutions that have already become commonplace in the West are being adapted by Ukrainian companies – sometimes even faster, as competition forces them to look for innovative ways to attract and retain customers. At the same time, Ukrainian businesses are showing ingenuity: in addition to “typical” applications (chatbots, recommendations, content generation), non-trivial ones are also appearing, such as modifying the tone of letters or AI sorting of search results. This shows that understanding of AI’s capabilities is growing, and marketers are taking a creative approach to solving local problems with global technologies.

Conclusions: advantages and limitations of AI in “understanding” the consumer

Artificial intelligence brings unprecedented opportunities to marketing for analyzing and interacting with the audience. It can process huge amounts of data and reveal insights that remain invisible to humans. Algorithms learn from the behavior of millions of consumers, enabling them to predict trends and needs with high accuracy. They provide personalization at scale, where each customer receives attention as if they were the only one, taking into account their history, context, and even mood.

AI acts quickly, 24/7, and without fatigue: it does not make human mistakes due to lack of attention or emotional exhaustion.

In routine tasks (answers to frequently asked questions, segmentation, mailing), it is much more efficient, freeing up people’s time for creative strategic tasks. Finally, AI opens the door for marketers to “emotional analytics” – measuring and understanding consumer feelings based on data, allowing for deeper connections between the brand and its audience. These are all significant advantages that are already increasing marketing ROI and customer experience quality.

Sources:

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