As a product catalog manager or content specialist, the last thing you want to be doing is spending your time on data entry and excel lookups. You’d rather spend your time copywriting, optimizing product listings, and other enrichment tasks that require the domain expertise you have. Wouldn't it be nice if you could do that while all the little mundane tasks that take up so much time and effort just ran by themselves?
One such task is categorizing products accurately to your taxonomy. Using modern AI and machine learning, it can indeed run quietly in the background while you focus on more critical tasks. In this article, find out how Pumice.ai automates ecommerce product categorization.
In both business-to-customer (B2C) and business-to-business (B2B) ecommerce, correct product categorization is critical for your customers and your business:
Wrong or inexact categories can create additional business, financial, and legal issues in almost every modern ecommerce channel as explained below.
The Google product taxonomy has about 5,600 unique categories organized as hierarchical categories that get more granular the further you go down the tree. It enables your products to show up in Google Shopping, as shown above, and in Google search, Google Maps, YouTube, and other Google results. It's also used for targeted advertising campaigns in Google Ads.
Miscategorization can lead to losing potential leads and reduced conversions as shown in the above illustration. As mentioned before, these external channels require your categorization to be correct, with some categorizing for you, and some requiring you to categorize your products.
Even if you’re not selling products through Google Shopping, this taxonomy is a great one to use for your site or marketplace, as it covers a very large range of products, and is granular where it’s needed.
Amazon's product taxonomy is visible to sellers when uploading products and to buyers on the Amazon search page as shown above. Popular products in a specific category are found on the category pages.
Setting the correct category is important for sellers because your products can be removed and your account flagged if categories are incorrect. The right category lets you correctly fill in all required product fields. While some products might seem like a candidate for multiple categories, it's important to choose the correct one.
The Shopify product taxonomy is a list of categories that is publicly available. It's important because it:
On Meta properties like Facebook, Instagram, and WhatsApp, setting the right category is important for the following reasons:
For global B2B ecommerce, standardized taxonomies like the United Nations Standard Products and Services Code (UNSPSC) and GS1 enable streamlining procurement, regulatory compliance, taxation, and more.
Many ecommerce companies use a combination of rules-based automation, data entry employees, and knowledge process outsourcing to categorize products.
The rules-based automation suffers from several drawbacks:
All the manual data entry and review workflows come with their own problems:
Perhaps you're considering using general chatbots like ChatGPT or large language models (LLMs) like O1 or Gemini to solve such product categorization bottlenecks. Will a prompt like the one below work?
If you're categorizing just a few products, this will work with decent accuracy and efficiency.
However, they have the following drawbacks for heavier or more nuanced tasks:
Pumice's AI models are highly specialized and tuned just for product categorization. Not only do they overcome all the problems and costs of manual workflows but they also overcome all the above drawbacks of general-purpose chatbots and LLMs since they are explicitly trained for product categorization.
We explain the different types of models we use, their training philosophy, and their benefits below.
Our baseline models are generic categorization models. Their training includes a large number of products of different types categorized under large generic taxonomies like Google's.
This allows our baseline models to generalize quite well to any product and taxonomy as long as there aren't subtle nuances between products or categories.
You can even specify your company custom taxonomies and get accurate results. For example, if your online shop specializes in hardware products, you can bring your custom categories extracted from your inventory. Our baseline models will identify the best fit category for your products with high accuracy.
Our model looks at text data such as title and description, as well as the image. T
But what if you have half a million products and 5000 categories? Then, even high accuracy means thousands of miscategorized products that need manual reviews. Do you have the time, manpower, and costs for that?
For such scenarios, you need models with extremely high accuracies whose error rates are far lower. You need our fine-tuned models!
Fine-tuning means training a model to pay close attention to subtle nuances between different product details as well as different categories. These models learn the exact relationships between your product data and the taxonomy.
Unlike our baseline models that were trained on generic products and taxonomies, the training data for fine-tuning consists of very carefully selected products and categories whose nuances can confuse even human workers.
By deliberately training our models on such difficult datasets, we force a fine-tuned model to pay attention to all product and category nuances for deciding the best possible category. If a model miscategorizes a product, we optimize it so that it learns to categorize that product more carefully. Fine-tuning is also great for multi-language use cases. Many categorization services struggle with different languages or use multiple languages. We use fine-tuning in these instances to help the model completely understand language differences and still achieve a high accuracy.
Such stringent training enables our fine-tuned models to achieve far higher accuracies compared to our baseline models. Our fine-tuned models guarantee at least 90% accuracy; if it's less than 90%, you don't have to pay!
Our fine-tuned and enterprise plans provide carefully fine-tuned models for the following standard taxonomies:
These models can accurately categorize your products even if the categories are confusingly similar with subtle cultural nuances or domain-specific differences like the ones below.
We even fine-tune models for your specific ecommerce site products and categories!
Perhaps you run a specialized ecommerce B2C business that sells only tea products from all over the world.
Perhaps it's a B2B business that sells all kinds of electronic components or hardware industrial supplies with a business specific taxonomy of website categories.
For such use cases, you'll likely have a specialized and deep taxonomy. For example, Google's taxonomy has just one category for all kinds of screws while a specialized supplier like McMaster-Carr has 17-20 different categories just for screws. AI needs to understand such product and category nuances.
To facilitate this, our data scientists work with your experts to carefully design stringent training datasets consisting of your specific products and categories. The dataset is designed to confuse even human experts unless they pay very close attention to all product details. We then rigorously fine-tune our models on such data to achieve extremely high accuracies. We guarantee at least 90% categorization accuracy from our fine-tuned models even on your custom taxonomies; if it's less than 90%, you don't have to pay for usage.
Our enterprise plan supports any number of such fine-tuned categorization models.
In this section, you'll learn to use Pumice.ai to automate ecommerce product categorization.
Pumice needs only 2 product details:
As mentioned above, images can be provided as well, although they are optional.
These two details are almost always more than enough for Pumice to accurately categorize all your products.
Other details like fabric type, ingredients, or MPN codes are completely optional. However, don't hesitate to include them if:
Including more details may even bump up the accuracy slightly for some products with lots of variants with minor differences.
Export these product details from your existing product information management (PIM) or ecommerce service in comma-separated values (CSV) files.
For bulk categorization, you can either upload a CSV file using the "Upload" button or import directly from your Shopify shop as shown below.
Make sure your CSV file has a header row with "title" and "description" columns. Product titles and descriptions are mandatory. All other details are optional but it doesn't hurt to include them.
Alternatively, if you have a dev team taking care of your ecommerce properties or PIM, you can ask them to upload product CSV files using Pumice's application programming interface (API). They can use the CSV upload endpoint and get back an upload ID for each file.
Pumice can categorize products either using one of the built-in taxonomies or using one of your custom taxonomies. The first approach is called static categorization and the second is dynamic categorization.
The built-in static taxonomies include pre-defined taxonomy models for Google product category taxonomy and Shopify product taxonomy.
For dynamic taxonomies, upload a text file containing all your custom categories using one of the options shown below. If you're using the API, upload your custom taxonomy using the upload tree endpoint and get a tree ID.
A typical taxonomy text file looks something like this:
Pumice supports different ways to do the categorization.
If you're a non-technical user, you can do either bulk categorization ("batch run") or item-by-item categorization ("single run") from Pumice's dashboard.
Alternatively, your dev team can integrate the categorization into your existing business workflow using the Pumice API.
Let's look at all these ways below.
For a batch run, upload your product details CSV file and select a built-in or custom taxonomy. Click "Upload & Generate" to start the categorization. The results of each run are available in the "Generated Entries" section.
If you have just one or a few products, enter the product title and description in the "Single Run" section. Select a built-in or custom taxonomy and click "Generate." The product's details and category are displayed.
If you have a dev team, they can use the API in different ways from your applications or ecommerce plugins.
The batch categorization endpoint can either do dynamic categorization using your custom taxonomy or static categorization using a built-in taxonomy.
For batch dynamic categorization using your custom taxonomy, call the endpoint as follows:
For batch static categorization using a built-in taxonomy, call the endpoint as follows:
In either case, the endpoint returns a task ID to track the categorization. Use the get results endpoint to get back the categorization results.
For item-by-item categorization, use the single categorization endpoint. Like the batch endpoint, it can either dynamically categorize against a custom taxonomy or statically against a built-in taxonomy. Instead of a CSV, supply a single product title and description in a "data" object as shown above. Everything else is similar to the batch endpoint.
Our product and image similarity features can evaluate the similarity of two products based on their details or images. They allow you to judge the best categories for new products by comparing them with existing categorized products in your catalog.
They're available on the dashboard and via the similarity API.
You can upload the identified product categories back to your PIM or ecommerce service manually or using their APIs through custom integrations. Our most popular integrations include:
As you saw, Pumice.ai is a PIM enhancement service to streamline product information tasks at scale.
Contact us today to learn how you can get started automating your product categorization and improve your customer experience.
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