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MAR 04
2026
On modern EC sites, it is not uncommon to find hundreds of thousands or even millions of products. When users search, candidate products are fully displayed, and recommendation functions are highly sophisticated. Even so, many EC operators face the problems that “products are not being seen, so they cannot be sold” and “CVR is not increasing.”
The background to this issue is not necessarily a lack of measures, but rather a mismatch between how users choose products and the structure of the EC site itself. Users are not confused because “there are too few options,” but because they “do not know how to choose.” As the number of products increases and information becomes more complex, this mismatch becomes even more apparent.
In this article, we will first structurally organize the reasons why users feel lost. Then, we will discuss the role of tag design as one solution, and explain how AI-driven tag generation, which has attracted attention in recent years, can transform both the purchasing experience and the operational efficiency of EC.
Users are not confused by having "few options." In fact, the more information and options presented, the greater the confusion about "how to choose." When this problem is analyzed structurally, three key flaws become apparent:
If we organize this problem structurally, three key flaws become apparent. EC sites are typically designed around product categories such as “One Piece,” “L Size,” and “Black.” On the other hand, what users are actually looking for are options based on purpose and situation, such as “clothes you want to wear on a special day” or “items that are just right for this season.”
The misalignment between this “product axis” and the “intention and purpose axis” is the primary factor that causes hesitation
There are many sensory triggers for purchasing, such as “somehow spring-like clothes,” a “calm atmosphere,” or something that “suits my current mood.” However, in conventional search and filtering functions, there is no mechanism to verbalize this “somehow” in the user’s mind and connect it to specific products.
As a result, the feeling of “it’s not bad, but it lacks a decisive factor” continues, and the decision is postponed.
When hundreds of products are lined up in the search results, users become exhausted. They don’t know which one to look at or which one to choose. They get tired just from scrolling, and their motivation to buy decreases. Without an intuitive experience of “browsing and choosing,” hesitation only deepens.
This structure also overlaps with the deadlock on the EC management side. The number of products continues to increase, and although major measures such as advertising, email newsletters, and coupons have been implemented, results are struggling to grow and the next breakthrough is not in sight. This is because the fundamental condition—that “users are able to actively choose”—is not properly in place.
The more measures that are added, the stronger the feeling users have of “being targeted” or “being pushed.” As a result, even if there are short-term numerical improvements, comparative fatigue and user drop-off increase, making it difficult to achieve medium- to long-term growth. What EC operators should reconsider is not the “next measure,” but the “structure” itself.
One way to solve the user’s “hesitation about how to choose” is through tags. Tags that visualize the user’s “intention” and “sensibility” function as a mechanism to classify products across categories and support searching and browsing.
In the past, organizing product attributes such as color, size, and material was the main focus. However, in recent years, the importance of tags that are closer to users’ intentions and sensibilities—such as “use scenes,” “purpose,” and “mood”—has increased.
Based on product information, descriptions, and behavioral data, tags can visualize contexts such as “for what purpose this item is selected” and “what kind of person it suits.” As a result, the search experience shifts from “the task of guessing the correct keyword” to “the experience of choosing while confirming your own feelings.”
By introducing tags, the relationship between users and EC management itself changes. Users are freed from the burden of thinking about search conditions, and instead can explore products by following tags based on sensibilities such as “I want to wear this on a special day” or “I want something that feels calm.” Rather than getting lost in too many choices, they can feel confident that they are choosing according to their own intentions. As a result, the purchasing experience becomes more natural and positive.
Improving this kind of “ease of choice” also brings significant benefits to the EC management side. By visualizing users’ interests and goals through tags, decisions about measures and product planning can be made based on data rather than relying solely on experience or intuition. This makes it possible to improve accuracy while reducing operational load.
In addition, tags not only make it easier for users to choose products, but also tend to increase the average number of purchases. Because tags clearly express “the reason for choosing this product” in words, users can confidently browse related products one after another according to their interests. By simply following the tags that catch their attention—without the hassle of starting a new search—they can discover “what they want” that they may not have even been aware of.
In other words, instead of forcefully recommending products, purchase opportunities naturally increase as users enjoy exploring the site. This improvement in “ease of choice” ultimately leads to higher sales. Tags are not just a UI enhancement; they represent a structural reform that shifts the axis of EC decision-making from “product” to “user intent,” creating a mechanism that benefits both users and operators.
There are multiple approaches to tag generation, and AI is one of them. The main options are as follows:
Manual tagging
This is a method in which the person in charge sets tags for each product individually. It is relatively easy to introduce at the beginning, but as the number of products increases, the operational load also grows. In addition, tag quality may vary depending on the person responsible.
Using generative AI APIs
This method incorporates generative AI (LLM) APIs into the system and generates tags from product descriptions and related data. While it enables flexible tag creation, it also involves ongoing development and maintenance costs.
Using existing EC platform functions
Basic tag management is possible with standard EC platform features. However, there are limitations when it comes to capturing deeper contexts such as user intentions and emotions.
Each of these methods can be effective for a certain product volume or a limited category range. However, to continuously overcome the challenge of minimizing operational load while maintaining accuracy—and to precisely capture users’ ambiguous intentions—an AI approach optimized specifically for EC applications can be a highly powerful option.
What EC sites need is not more new measures, but a structure that allows users to choose products according to their own intentions. In catalog-type EC, which presents products unilaterally, it is impossible to address users’ hesitation. What is required now is to consider users’ situations and interests and naturally show them a path by conveying, “You can choose from this.”
AI-powered tag generation is one of the key factors that supports this transformation. By visualizing contexts such as the user’s purpose, mood, and usage scene as tags, EC can evolve from a mere product list into a system that actively supports selection.
Verada AI, an AI service developed by Fanplayer, implements the idea of “from catalog to concierge” as a comprehensive EC experience, rather than just a partial function. Verada AI is an AI platform that analyzes product data and user behavior in real time, building the best experience by understanding the user’s intentions and interests. Leveraging advanced analysis technology—also used in financial systems—it offers the advantage of making judgments with both accuracy and stability.
One of the features provided by Verada AI is the AI tag. Three types of tags are automatically generated based on product information and user behavior data. These tags are displayed on the product details page, allowing users to browse related products simply by clicking on the words that catch their interest.
Introducing this mechanism does not require recreating product data. Existing product catalogs can be used, so implementation does not require a major renovation. Tags can be displayed without coding and optimized for each device, reducing the operational load for the team.
Before adding more measures, it is essential to prepare a “base” that allows users to choose without hesitation. This is a highly practical approach, even considering ease of implementation. Verada AI transforms the EC site from a catalog that merely arranges products into a concierge aligned with the user’s intentions. Optimizing sales efficiency with this AI foundation is an essential step to support continuous sales growth.
The three types of tags are monotag, which provides information about the product itself; human tag, which captures people’s emotions, intentions, and lifestyle; and misetag, which supports convenience and strategy on the store side. In misetag, it is also possible to generate tags by combining buzzwords and tags of brands that the store wants to promote.
For press inquiries please contact pr@fanplayr.com
