What is product categorization?
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Product categorization, sometimes referred to as product classification, is a field of study within natural language processing (NLP). It is also one of the biggest challenges for ecommerce companies. With the advancement of AI technology, researchers have been applying machine learning to product categorization problems. In this article, we will discuss what product categorization is, why it is important, and how it relates to ecommerce sites. Lastly, we will briefly discuss companies that provide product categorization outsourcing and their position in the market.
Product categorization is the placement and organization of products into their respective categories. In that sense, it sounds simple: choose the correct department for a product. However, this process is complicated by the sheer volume of products on many ecommerce platforms. Furthermore, there are many products that could belong to multiple categories.
For example, the item “Mens Basketball Sneakers” could use the following path and categories:
Clothing, Shoes & Accessories > Men’s Shoes > Men’s Athletic Shoes
However, it could also follow this path:
Sporting Goods > Team Sports > Basketball Equipment > Shoes > Men’s Basketball Shoes
The correct path, or whether you employ multiple paths to the same item, depends on how you expect customers to search for that product. It also depends on which departments you have available. In addition to choosing the correct department, product categorization also deals with the intricate organization of those departments.
Why is product categorization important?
There are many reasons why product categorization is important for ecommerce and marketing. Through the accurate classification of your products, you can increase conversion rates, strengthen your search engine, and improve your site’s Google ranking.
1. Strengthen the user experience
On average, 99% of users will not make a purchase on their first visit to your site. One reason for this is that people want to shop around and find the best price. Eventually, they will finish shopping and be ready to make a purchase. At that point, users will choose the site that offered the best price and the smoothest shopping experience.
A well-built product taxonomy allows customers to find what they are looking for quickly and easily. Making your site easy to navigate is one of the most important elements of your UX and will lead to higher conversion rates.
2. Improve search relevance
Correctly categorizing products allows your search engine to fetch products quicker. As a result, you create a quicker and more accurate search engine. Since the search engine is often the first element users will interact with on ecommerce sites, a strong search engine is pinnacle to the user experience of your site.
3. Help customers find your site
Most people won’t access your site directly. In fact, 35% of people searching for products start on Google. Once you have a strong product taxonomy in place, this will allow you to create the relevant landing pages for your products. In turn, Google and other search engines will be able to index your site and your products more easily. In the end, this allows your products to rank higher on search engines, increasing the chance that customers find your site.
E-commerce product categorization
We’ve explained how accurate and intuitive product taxonomies can improve the user experience and lead to higher sales. The problem is numerous sites contain incorrect product classifications. This is because these sites often require merchants to input their product info and select categories manually. Furthermore, sometimes multiple merchants select different categories for the same product. To solve this problem, ecommerce sites often employ automated product classification.
For example, Amazon hosts around 350 million products on their platform. To help merchants choose the correct category, Amazon and other ecommerce companies have automated product categorization tools available. After simply inputting the title or a few words about the product, the system can automatically choose the correct category for you.
When there are hundreds of millions of products in the catalogue, even a 1% increase in accuracy can lead to millions of additional accurate classifications. As a result, many ecommerce companies heavily invest toward improving their automatic product classification systems.
How do you improve product categorization models?
The remaining question is, how do you improve automatic product categorization models? The overall process is quite simple:
- You create a list of desired categories and guidelines
- Annotators are given those categories, as well as products to be categorized
- The resulting training data is fed to the classifier
- The classifier is then tested on the training data and a test dataset
In theory, once the model has been trained it should be able to place new products into the correct categories, based on your current product taxonomy. In the past, choosing the category path was as simple as reading the product’s title. Using the title, annotators would discern the correct department. With this method, the task is simply a text classification task. However, numerous novel machine-learning based approaches have been created which not only analyze the title, but also the description, image(s) of the product, and other metadata as well.
Product categorization services
Training data creation is where data annotation companies like TELUS International come in. TELUS International provides product categorization services by utilizing vetted crowdworkers, a proprietary data annotation platform and expert analysts.
- Product Image Classification – If you require product image categorization datasets, we can utilize our large crowd of annotators to categorize your product images at scale.
- Product Title & Description Categorization – For text-based product classifiers, we have multilingual staff ready to categorize your products based on their titles and item descriptions. Our services can help you build a high-quality dataset, ensuring your product categorization model has a solid ground truth.