If you are running a retail business or manage product inventories, you probably already know that keeping your product catalog in order is no simple task.

But what you may not realize yet is just how much the way you categorize products can impact your business. A key subtask of catalog management, product categorization is the process of classifying items into consistent, logical groups — for example, “Clothing → Women → Shoes” or “Electronics → Smartphones → Accessories.” Done well, it creates a structure that makes products easier to find, compare, and purchase, both for your internal systems and custom-facing online stores.

In an increasingly competitive marketplace where consumers expect fast, relevant, and personalized shopping experiences, even small errors in the categories of your products can have outsized consequences. As we’ll examine in this article, misplacing products in the wrong categories can hurt search performance, frustrate customers, lead to missed sales, and ultimately push customers toward better-organized competitors.

Ensuring your product categories are optimal is therefore not just a tedious admin task but a critical component of running a successful retail business.

At Neuralk, our mission is to help retailers solve this challenge and get product categorization right from the start. In this article, we’ll walk through the key elements of product categorization that are crucial for retailers to understand:

• What’s product categorization and why it matters
• The core challenges retailers face when categorizing products
• The business cost of getting product categorization wrong
• How to automate product categorization effectively
• Why traditional machine learning isn’t enough
• Why Tabular AI is the best solution

By the end, you’ll understand how smarter categorization solutions powered by Tabular AI are essential for any retailer looking to stay ahead of the competition, and how Neuralk AI is uniquely positioned to help you achieve just that.

What’s Product Categorization and why it matters?

At its core, product categorization involves assigning each item to one or more categories within a defined taxonomy. In other words, it means placing products into a structured system used to organize them, usually starting with a broad parent category followed by increasingly specific subcategories. This might seem straightforward at first, but when your catalog has thousands or millions of products, each with complex attributes, it quickly becomes a challenge.

For example, take this product from a major online retailer:

• Product Name: Becokan Beach Bags for Women [..]
• Category Path: Clothing, Shoes & Jewelry › Luggage & Travel Gear › Travel Totes

At first glance, the category path might look straightforward, but even this simple example reveals several layers of complexity:

• Taxonomies are multi-level: Retailers often classify products across multiple category levels, from broad groups like “Clothing, Shoes & Jewelry” to highly specific ones like “Travel Totes.” Each level needs to be accurate to ensure the product shows up in relevant searches and filters.‍
  ‍
• Deciding on categories can be ambiguous: Even for humans, deciding where a product truly belongs is not always straightforward. In the example, the beach tote bag is listed under “Luggage & Travel Gear” which itself falls under the parent category “Clothing, Shoes & Jewelry”. Other retailers might have chosen to place a similar product under “Sports & Outdoors,” “Accessories,” or simply “Bags.” These different choices highlight just how subjective product categorization can be, making it difficult for retailers to define a standardized and consistent category system across their catalog. ‍
  ‍
• Product descriptions are rich & messy: Take a closer look at a typical product title like the one in the example, and you’ll often find a mix of attributes (e.g., waterproof, sandproof, and large size), all packed into a single line. These overlapping features can each suggest different potential subcategories, making it even harder to determine the most appropriate placement. ‍
  ‍
• Product categories are constantly evolving: Every season brings new products with unique titles and descriptions that don’t necessarily fit neatly into existing categories or taxonomies, forcing retailers to make challenging decisions about if, how and when to revise their category structures.

The core challenge behind Product Categorization: no universal “product language”

Behind all these challenges lies the root cause: the absence of a common “dictionary” or universal reference system that everyone agrees on to categorize products. Since every retailer, brand, or marketplace has unique needs, customer expectations, and internal processes, they each end up developing their own custom categorization methods. This lack of standardization creates serious bottlenecks that can significantly slow down your operations. For example:

• Your teams must continuously revise their categorization systems as product lines grow, customer expectations evolve and performance signals indicate the need for change.
  ‍
• Sharing or scaling these processes across departments, platforms, or partners, each holding their own categorization systems, can quickly become a major headache, creating confusion and extra manual effort.
  ‍
• Your competitors might organize the same products in very different ways, making it difficult to benchmark or align your offerings with market expectations.

The business cost of getting Product Categorization wrong

A fragmented and inconsistent categorization system doesn't just create operational headaches for your teams but singnifcantly increases the risk of placing products in the wrong categories. Take our beach bag example: instead of being placed under “Bags” or “Accessories”, it might mistakenly end up in “Outdoor Gear.” At first glance, this may seem like a small error, but in reality it can trigger major consequences for your business, including:

• Lower conversion rates: When customers struggle to find relevant products or navigate confusing categories, they are less likely to complete a purchase, directly impacting your sales and overall conversion rates.
  ‍
• Reduced visibility of your products on search engines: Products buried in the wrong categories are harder to find, not only on your website, but also through external search engines like Google. This can lower organic traffic, hurt your SEO and give competitors more opportunities to reach your potential customers.
  ‍
• Increased frustration by your customers: Products placed in the wrong subcategories often create mismatched expectations, resulting in more returns and negatively impacting the overall customer experience and satisfaction.
  ‍
• Slower time-to-market: Poorly organized taxonomies make it harder and slower to add new or update existing product listings for your teams, delaying launches and reducing your ability to respond quickly to market trends and demands.

Ultimately, all the above issues can quickly add up: even a 5% misclassification rate can translate into millions of monthly lost revenue.

Now that we know the risks, what options do we have to address them effectively? Manual categorization quickly becomes impractical and error-prone, while rule-based systems often break down as new products are added and taxonomies evolve. Clearly, the solution lies in smarter, automated approaches that can keep up with the pace of modern retail and evolving market demands.

Automating Product Categorization: why traditional Machine Learning isn’t enough

For retailers managing large product catalogs, many data science teams have turned to ML algorithms (like XGBoost or CatBoost) to automate their categorization process. On clean, well-structured datasets, these models can deliver solid accuracy. But in real-world retail, where data is messy, inconsistent, and constantly evolving, traditional ML approaches run into serious limitations, such as:

• Requiring extensive manual work to clean & preprocess the input data and extract meaningful features for training.
• Needing frequent retraining as product catalogs and taxonomies evolve.
• Taking weeks or even months to build and maintain, consuming valuable team resources and delaying time-to-market.

If you want to dive deeper into these challenges you can check out our full article on the limitations of legacy ML for enterprise data here.

What Retailers Need to Automate Product Categorization Effectively

To successfully overcome these challenges, retailers need intelligent and scalable solutions beyond traditional ML that can:

• Generalize efficiently across new products: Handle newly launched items, seasonal products, or niche SKUs without needing manual mapping or model retraining.
  ‍
• Make the most of every product detail: Use all available information in your catalog such as titles, descriptions, attributes, and tags to accurately infer the correct category even when keywords are unclear or inconsistent.
  ‍
• Capture structured context beyond semantics: Automatically understand rich information hidden in your catalog's structure, such as category hierarchies, feature correlations and cross-category dependencies that LLMs alone can’t detect (we invite you to read our article on why LLMs aren’t enough for enterprise data ‍here).
  ‍

• Adapt quickly to taxonomy changes: Instantly incorporate reclassifications, merged categories, or new category levels without manual intervention.
  ‍‍
• Operate at high volume and speed: Be able to process large volumes of SKUs daily without the need to maintain complex pipelines or constantly retrain models.
  ‍

The solution: how Neuralk AI solves product categorization with Tabular AI

Despite its obvious challenges, product categorization doesn’t have to be slow, messy, or prone to errors anymore.

Via our proprietary Tabular AI technology, we are completely transforming the way retailers manage this critical task, placing products to the right categories though a seamless API access faster and more accurately than ever before.

Our solution directly addresses the core pain points retailers might face: powered by our pretrained Tabular Foundation Model, it generalizes automatically to new or unseen products without the need for complex ML pipelines or costly model retraining. Its state-of-the-art performance (see our benchmark here) lies in the fact that it is able to fully exploit all product information—from titles and descriptions to attributes and metadata—while all also being able to capture hidden information in your catalog's tabular format that traditional ML and LLMs alone are unable to detect.

Thanks to our expert AI modules integrated into our API, the entire categorization pipeline is handled end-to-end, so you don’t have to maintain or update it yourself.

‍

Conclusion

Effective product categorization is no longer a tedious back-office task: when automated properly, it can be a strategic driver of sales, customer satisfaction, and streamlining of operations. With Neuralk’s Tabular AI technology, retailers can now achieve this smarter, faster, and more accurately than ever before, boosting their business's results with near-zero effort.

Are you curious to know how Neuralk AI can simplify your product categorization? Let’s dive in together! Secure your free expert evaluation today by filling out the form here.

‍

‍