The Technology of Interpreting Desire and the New Rules of Marketing

Most of the services that customers encounter today, including e-commerce, content platforms, OTT, and search advertising, operate primarily around personalized recommendations. Recommendations are no longer just a simple feature; they have become central to the customer experience. What is particularly interesting is that products or content recommended by AI tend to result in significantly higher purchase or click-through rates. For example, about 35% of Amazon’s total sales are generated through AI-based recommendations, and 75% of Netflix users watch content that has been recommended to them.¹⁾ This is not simply because recommendation algorithms have become more sophisticated, but because AI is able to read signals of desire from customer behavior. Even if customers do not explicitly express what they want, their interests and needs are naturally revealed in their behavioral data. In this article, we will examine how AI interprets the behavioral data left by customers and how companies are strategically utilizing this information. We will also explore how the emerging technology of generative search is capturing customer intent and transforming marketing approaches.

✅Customers Speak Through Their Actions!

When marketers try to understand their customers, the first method that often comes to mind is conducting surveys. However, today’s customers rarely articulate their true intentions in words. Instead, they reveal themselves through countless actions—such as clicks, scrolling, time spent on a page, and adding items to the cart.

According to Harvard professor Gerald Zaltman’s book, How Customers Think: Essential Insights into the Mind of the Market (2003), and various related studies, consumer purchase decisions are heavily influenced by habits, unconscious motivations, emotions, and environmental stimuli. In fact, it is revealed that as much as 95% of choices are made unconsciously. This means that long before a customer ever says, “I want to buy this,” many clues are already embedded in their behavior. In other words, actions come before words.

✅AI That Reads Patterns and Predicts the Next Move

A vast amount of customer data, left unconsciously through various actions, can be intricately interpreted by AI. AI analyzes behavioral data as a kind of “intent vector.” For example, it predicts future actions by synthesizing dozens of variables—such as the time of day a customer is most active, the device they use, and the price range of products they search for.

Let’s say Customer A repeatedly searches for a specific brand’s home appliances every Tuesday evening using a mobile device. AI learns this pattern and can automatically recommend relevant products to that customer at a similar time the following Tuesday. These predictions are especially powerful when made in real time, as they can significantly increase conversion rates.

Our AI marketing solution, groobee, helps improve business performance through real-time, behavior-based predictive technology. Features such as interest-based recommendations for each visitor, exit-intent pop-ups, and cart history-based remarketing enable marketers to interpret customer actions more precisely and respond instantly and effectively.

💡Recommended Reading> Real-Time Customer Flow Insights Every Marketer Should Know

✅A New Dimension of Understanding: Generative Search

Recently, the paradigm of recommendation technology itself has been changing. Instead of simply focusing on what a customer has clicked, there is now an effort to understand in real time what the customer actually wants at the moment. At the center of this change is generative search technology.

Whereas traditional search algorithms operated mainly around keywords, generative search understands the entire context of the natural language input provided by the customer, grasping not only the intent but also the emotions, situation, and needs contained within the sentence. For example, if a customer searches for “Recommend a low-calorie drink I can enjoy on my way home from work,” conventional search would simply list low-calorie drinks, but generative search takes into account various factors such as the time of day, immediate consumption, health-consciousness, and ease of purchase to select and suggest the most suitable content and products.

PLATEER’s LLM-based generative search engine, genser, makes these intent-driven recommendations a reality. By extracting key words, understanding sentence intent, and generating product lists, this advanced search engine provides main features such as summarizing search results, explaining the reasons behind recommendations, and offering related keywords, thereby driving real conversions throughout the user journey. In fact, after implementing genser, the number of search users increased by 2.2 times, and the click-through rate on search results rose by as much as 86%.

💡Discover how generative AI search can move the hearts of your customers

✅Not "Without AI," but "With AI"

The countless traces left by customers are not just simple logs. Within them lie clues about where customer desires are headed, under what conditions they respond, and what triggers their sensitivity. AI discovers patterns in these traces, creates rules, and calculates the optimal timing for action. Now, marketers are tasked with designing brand messages and customer experiences on the data-driven frameworks built by AI.

Planning is the domain of humans, but execution belongs to AI. When the strategy of marketers—equipped with both emotion and logic—is combined with the agility of AI reading real-time data, the customer experience will evolve even more dramatically.

¹⁾ References: MDM.com, McKinsey & Company, Netflix Media Center

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