It's no secret that eCommerce businesses need to automate their product categorization to keep up with the competition. Not only is it a time-consuming process, but it's also one that requires precision and accuracy.
However, product categorization offers the benefit of reducing customer frustration by ensuring that search and browsing are streamlined. Plus, it helps with SEO with the help of natural language processing, (NLP).
Thankfully, advanced AI technology has tackled the hassle of manual product categorization by facilitating the entire process. Now, with the help of powerful AI such as Pumice.ai, you can accurately categorize your products with ease and get a leg up on the competition.
In this blog post, we'll explain what product categorization automation is, why eCommerce businesses need it, and how our platform can help you optimize your product catalog for better performance.
Simply put, product categorization is the organization of products according to specific categories and utilizes a specific product hierarchical framework. This may seem like a simple task, however, it becomes daunting when your business boasts a vast amount of products on a wide range of sales channels. What's more, one product can have multiple categories. With that said, you have to ensure that each product is accurately categorized, and remains up-to-date without using a ton of effort and time.
To help you overcome this challenge, product categorization automation comes into play.
Now that we know what product categorization is, let's discuss why eCommerce businesses need it. After all, as an eCommerce business owner or manager, your ultimate goal is to make sure customers can find the products they're looking for as quickly and easily as possible.
Product categorization plays a pivotal role in customer satisfaction and experience. Research has shown that around 99% of customers will leave an eCommerce website and not make a purchase on their first visit. These people are just browsing. However, if they can't find what they're looking for, chances are they'll never come back.
This is where product categorization comes in. By automating your product categorization, you can ensure that products are accurately categorized and easy to find. As a result, customers will be able to find what they're looking for quickly and easily, without getting frustrated.
What's more, product categorization can help you upsell and cross-sell products. For instance, let's say a customer is looking for a pair of shoes. If the product they're interested in is categorized under "men's shoes", they may also be interested in other products in that category such as socks, laces, and insoles.
By automating your product categorization, you can ensure that customers are presented with relevant products that they may be interested in, leading to more sales and a better customer experience.
Manually categorizing products is a time-consuming and tedious task. Not to mention, it's prone to human error. One small mistake can lead to big problems down the road.
For instance, let's say you have a product that's categorized under "men's shoes" but it's actually for women. A customer may purchase the product not realizing it's not meant for them, leading to returns, refunds, and a loss in sales.
Product categorization automation can help you overcome these challenges by accurately categorizing your products, and eliminating data entry errors.
Pumice.ai offers an ai based model that allows you to automatically categorize your products to your ecommerce product taxonomy. This allows you to completely remove this manual process from your PIM workflow and get back to doing more important product tasks.
You can even fine-tune the model to your specific store and taxonomy. As a result, the model is better able to categorize products accurately.
We follow the Google Product Taxonomy format for how to structure your taxonomy for categorization. This is a very simple file structure that has each “level” of the taxonomy defined as its own row. This means each level of the taxonomy hierarchy has all above information included.
Once you’ve added your product data through our API or CSV upload, and designed your taxonomy to fit with your store, you can run a new product through the Pumice.ai dynamic categorization model. The model will then generate an output based on the example input such as choosing a season and categorizing it accordingly. Our categorization model doesn’t rely on keyword matching or semantic matching to categorize to product categories. The models have learned a deep understanding of how to best match products to the categories sequence, relative to all the available options and varying tree depth levels.
Once you've decided to automate the enhancement of your product categorization, there are a few best practices you should follow to ensure accuracy and consistency.
Here are a few best practices to follow:
Pumice.ai allows you to upload your product data with key fields such as title, description, brand, GTIN, price and more. Different fields in your dataset will correlate better or worse to your categories based on the trained relationship between your data and the categories. It’s best to use fields that are filled often and are not filled with nulls, and do a great job of describing your product.
You can make this process incredibly accurate by creating a product taxonomy that best describes the full range of your product catalog. This includes discrete categories, facets as sub-categories, and balanced categories.
Each time you get back a result with categorized product data you get a confidence value for each product <> category pair. This is how confident the model is in the result based on its training and understanding of the two sides. This can be extremely useful to understand a few things:
1. How accurate your model generally is.
2. How good your model is for each category and how much that skews. Oftentimes the training data is unbalanced towards a few classes which means the other classes don’t have the same level of strength in terms of accuracy and data variance coverage.
3. What is the average confidence value of a correct result? This can help you process correct vs incorrect results at runtime.
Make sure to have a good data governance system in place to ensure that all data is properly managed and maintained. This includes having an established data entry process, validating data entry, and reviewing and updating the product data regularly. This will help ensure that your product data is accurate and up-to-date, leading to better categorization results. There are plenty of tools you can use to clean product data. Some of the most popular ones include:
• Data Enrichment Tools – These help to fill in missing values in your product data.
• Data Quality Tools – These help to identify and fix errors in your product data.
• Data Visualization Tools – These help to visualize the product data and identify inconsistencies.
• Text Analysis Tools – These help to analyze the text fields in your product data and detect anomalies.
Pumice.ai offers custom fine-tuning of the dynamic categorization model that allows you to train the model specifically on your product data and taxonomy. This greatly improves the accuracy of the baseline dynamic categorization model. We strongly recommend this process for users that have large or unique taxonomies.
Product categorization is a vital part of any eCommerce business. It helps customers find what they're looking for, and it can lead to more sales.
Pumice.ai automates your product categorization, so you can focus on other areas of your business. With our powerful AI technology, you can categorize your products with ease and accuracy.
If you're looking to automate your product categorization, look no further than Pumice.ai. We can help you take your business to the next level.
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