Automated product tagging is a system, usually based on machine learning model, that allows for the automatic addition of tags to products.

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If you are interested more about automated product tagging, please read on.

Product tagging is an effective tool for marketers and eCommerce businesses, as it helps them to create and maintain a consistent brand identity across all channels. By using automated product tagging solution, marketers, online stores owners and others can also save time on their daily tasks and focus on more important things.

Automated product tagging solution from is designed to be easy-to-use, which means that users can easily implement it in their daily workflow. The system works by automatically adding tags to products based on the name of products. This makes sure that you never have to worry about missing any tags again!

Product tagging from images

Product tagging from images is a relatively new technology that can be used in a variety of ways. It is especially useful for online stores who want to keep up with the competition, but have trouble finding time or resources to do it themselves.

One of the most obvious uses for product tagging from images is in retail stores. If you have an established brand name and need to make sure that customers are able to find your products when they search for them on social media or other sites, then this solution may be perfect for you!

Another way that product tagging from images can be useful is in digital marketing. If you’re trying to reach out to more people than just those who are already familiar with your business, then this could help you reach new customers that might not know about your products otherwise!

Machine learning models for tagging -> multi label classification

Multi-label model is a complex machine learning model that can be used for various tasks. It can be used to predict multiple classes or labels at the same time. Multi-label models are often used in text classification, where each document has multiple tags, e.g. in predicting tags for given product name on the online store.

Multi-labelled models are different from binary classification problems because they have more than two possible outcomes. In general, when we say we are doing multi-label classification, we mean that the outcome variable is a vector of k values representing the k different possible classes.

In contrast to binary classification, where there is only one correct answer per example, multi-label classification problems have many possible answers per example (i.e., documents). For example, if we have a document about a car and want to classify it as either “car” or “not car,” then our outcome variable will take on one of two values: 1 or 0 (yes or no). However, if we want to classify the same document into multiple categories such as “car,” “vehicle,” and “automotive,” then there are many possible outcomes for each outcome.

Here is e.g. a set of tags products generated by the multi label classification model from, for the word “sewing”:

22 %
12 %
9 %
8 %
7 %
7 %
7 %
6 %
5 %
5 %
5 %
5 %
4 %
4 %
4 %
3 %
cross stitch
3 %
3 %
2 %
2 %

Benefits of automated product tagging

Our automated product tagging solution is the perfect solution for businesses that want to keep up with a growing number of products and don’t have the time or resources to manually tag them all. System from will automatically tag your products, saving you time and money while helping you stay organized and presentable.

Automate your business processes

Our system is designed to automate your business processes, so it won’t take up any additional time from your employees. Instead, it will help them focus on more important tasks like marketing and sales.

Save time and money

Using an automated tagging system means you don’t have to spend hours tagging each product individually—instead, you can set up a single rule that applies to all products in your inventory.

Why is product tagging important?

Product tagging is important because it allows you to gain insight into your products, their customer base, and the way they are being used.

By adding tags to your products, you can view aggregate information about how many times each tag has been used, which tags are most popular, and what outlets are most popular among users. You can also see which tags are most frequently associated with specific products or product types, or view a breakdown of who is using the product and where they’re located. This data can be used to make decisions about whether or not to keep selling certain products, as well as what new products might be needed based on market demand.

This information can also help you target advertising campaigns more effectively. For example, if users frequently associate one tag with a specific product type (like “wooden magnetic bottle opener”), then you might decide to advertise wooden magnetic bottle opener during those months when that tag is most commonly used by customers.