After decades of analyzing past consumer behavior, digital marketing has entered a whole new phase, where companies now view the consumer as a series of future possibilities, rather than a collection of historical data.
These companies now seek to predict the future behavior of the consumer in order to predict what he might buy, when he might buy it, what circumstances lead him to make the decision, and what is the ideal moment to present the product to him.
In this new world, data turns into predictive models, predictive models turn into business tools, while human behavior itself becomes the stuff of an industry based on anticipation and anticipation.
Thus emerges a new form of marketing called “hyper-personalization,” which aims to discover what the consumer wants before he knows it by relying on artificial intelligence that turns him from a source of data into a constant subject of prediction.
From monitoring the past to anticipating desires
For the past two decades, consumers have become accustomed to targeted advertising and recommendation systems that suggest movies, products or articles based on what they have done previously. The idea is relatively simple: if a large number of consumers buy a particular product, it may be relevant to other consumers with similar interests.
This philosophy appeared on e-commerce sites and video and music streaming platforms, and became an essential part of the modern digital experience, but the traditional recommendation remained linked to the past because of its connection to what the consumer did previously.
But what is happening today is radically different, as artificial intelligence is no longer content with analyzing the past, but rather is trying to read the present, anticipate the future, and move from the stage of negative recommendation to proactive prediction of the consumer’s desires before he himself realizes them.
“Ultra-personalization” is based on a philosophy that searches for what the consumer might do next instead of searching for what he did previously, using artificial intelligence and predictive analytics to provide personalized experiences based on accurate behavioral data that go beyond traditional personalization based on purchasing history only.
Artificial intelligence plays a major role through its ability to analyze thousands of precise behavioral signals in real time and link thousands of variables to each other, which include the method of using the phone, hours of activity, speed of interaction with content, and recurring patterns in daily behavior.
According to a report issued by McKinsey Management Consulting, 71% of consumers expect to receive personalized interactions from brands, while 76% of consumers feel frustrated when they receive non-personalized or generic experiences.
How do algorithms monitor your mood?
Many consumers believe that the data collected by digital platforms is limited to what they write, buy, or search for, but the reality is more complex, as every click, every second a consumer spends in front of certain content, and every hesitation before pressing the purchase button, may turn into an analyzable signal.
Artificial intelligence systems make use of a wide range of data, including browsing and search history, purchase history, geographic locations, active and idle times, duration of interaction with content, type of devices used, digital consumption patterns, and interaction with messages and notifications.
This data may seem meaningless and disconnected, but when combined with other signals, it turns into an accurate behavioral map that artificial intelligence treats as parts of a single picture, allowing the construction of models capable of detecting patterns that humans do not notice.
Increased nighttime browsing time, decreased social interaction, and increased consumption of entertainment content indicate a state of fatigue or boredom, and from a marketing perspective, this means a higher likelihood of responding to offers for travel, entertainment, or products related to relaxation.
When algorithms build your digital copy
In traditional marketing, age groups, income levels, and geographic locations were the primary targeting criteria.
While “ultra personalization” focuses on the emotional state through “emotional AI” that attempts to infer emotions through various indicators, such as tone of voice, facial expressions, or even writing styles.
Companies, such as Realeyes, use device cameras to record viewers’ emotional responses in real time as they browse ads, providing advertisers with emotional data that is more accurate than anything a consumer consciously expresses.
In addition, companies are seeking to build a “behavioral digital twin,” a virtual model that simulates consumer behavior, preferences, and future decisions based on available data.
The role of the “behavioral digital twin” is not limited to describing what the consumer does, but extends to simulating what he might do later in different circumstances, allowing potential responses to be tested before marketing campaigns are actually implemented.
Amazon is building a predictive model that simulates consumer behavior and life with its logic and contradictions, based on data collected from the e-commerce platform, the “Alexa” assistant, the “Prime Video” platform, and “Amazon Go” stores.
While Spotify feeds its predictive model with daily listening habits and their changes in order to determine the state that the consumer is going through, while providing content and advertising products that suit that state and are behaviorally related to it.
Persado has a huge database of words and phrases classified according to their emotional impact on the consumer. The company analyzes billions of interactions in the financial services sector to identify the words and phrases that prompt the consumer to click, buy, or stop, targeting each consumer with the phrase appropriate to his or her psychological makeup.
While Dynamic Yield, which was acquired by MasterCard, relies on an adaptive engine that allows different offers and content to be presented to two users sitting in the same room, based on behavioral indicators that neither of them sees.
All of this coincides with the emergence of the concept of “predictive shopping,” which relies on anticipating consumer needs before they are expressed explicitly.
Amazon is one of the first companies to explore this trend through a patent for proactive shipping, an idea based on sending products to distribution centers close to the consumer based on algorithms’ expectations of the likelihood of them purchasing them.
Although the full commercial application of this idea is still limited, it reflects the direction the industry is headed, where demand forecasting becomes part of the sales process itself, rather than just a tool to support it.
Choice architecture targets moments of weakness
“Hyper-personalization” differs from previous digital marketing tactics in timing and precision, as impulsive purchasing decisions rise significantly at pivotal moments, such as feelings of loneliness in the middle of the night, fatigue after a stressful day, boredom while waiting, and anxiety over major life events.
A large portion of e-commerce spending is due to impulsive purchasing that occurs in the absence of prior planning, and the consumer later regrets a large portion of these purchases, known as digital regret.
E-commerce platforms rely on “choice architecture,” which helps reduce the space for conscious thought and stimulate impulsive response through elements such as time-limited offer counters, sold-out notifications, as well as personalized recommendations generated in the moment.
Algorithms that analyze browsing patterns can detect indicators of mild depression, social anxiety, and low self-confidence in some cases before the user himself makes the connection between these behavioral patterns and his psychological state, while targeting him with consumer content specifically designed to make him feel that the purchase is beneficial to his condition.
The Salesforce Einstein platform combines consumer accounts with machine learning to produce individualized forecasts and content recommendations across email channels, websites and apps in real time.
The Persado system tests millions of different wordings to determine which word suits each user. In cases documented by the company, major financial institutions recorded an increase in conversion rates after using the tool.
According to an article published by the Harvard School of Professional Studies, such tools have become a pillar in the arsenal of major telecommunications, banking, and retail companies.
Carefully designed choice environments seek to narrow the margin of conscious thought in favor of immediate response, where the “choice architecture” changes people’s decisions without them realizing it.
The platforms began to customize a choice environment for each individual in real time, using the digital behavioral twin, as purchasing decisions stem in large part from brain areas associated with feelings, and not rational analysis.
These algorithms target those areas by injecting the right stimuli at the right moment before the mind has a chance to review and evaluate.
As a result, the consumer feels that he is choosing freely, while the algorithm has paved the way for him that takes him to the destination he chose in a phenomenon called “restrained will”, meaning that the person is still choosing, but he is choosing from a list prepared for him without his knowledge.
Between consumer rights and absent legislation
This reality has prompted lawmakers and regulatory bodies to act, as European law prohibits the use of emotional AI systems in work and educational environments, with specific exceptions related to security, health and some legally regulated cases. The law also requires that individuals be notified of exposure to these systems before employing them.
However, this legislation faces major challenges in keeping pace with rapid development, as these systems in the commercial and marketing context fall into a gray area that has not yet been resolved, and many companies argue that what they collect is behavioral data and not emotional data, a distinction that is difficult to prove legally false with the tools currently available.
Despite concerns, “hyper-personalization” provides practical benefits to the consumer, as it helps reduce irrelevant advertising, improves their ability to discover products and services that match their needs, and reduces the time they spend searching and comparing available options.
For companies, this approach contributes to improving customer experience and raising levels of satisfaction and loyalty, which explains the continued intense investment in this field despite the mounting controversy over its ethical and legal limits.
In conclusion, in the era of “ultra-personalization,” the algorithm has become able to shape your consumption behavior with real and documented effectiveness, but this knowledge, despite its accuracy, is not real in the deep sense, nor is it capable of encompassing your human contradictions and psychological depth.
In a world where desires are made before they are born, and emotions are engineered before they are felt, you should stop for a moment before every click and ask yourself: Is this what I really want, or is this what I am meant to want?
