Product tagging is an important part of the product management process. It allows you to keep track of products, understand your customers, and measure the effectiveness of your marketing campaigns.
If you want to use free product tagging API, check it out at:
https://www.producttagging.io/demo_dashboard/
Product tagging helps you:
– Keep track of products by adding tags to them. You can tag a product with any type of information you want, from its name to the category it belongs in, or even a specific store where it can be purchased.
– Understand your customers by using tags that describe what they want. For example, if someone is looking for a new pair of jeans, they might search for “jeans” or “pants.” You can use these tags to see what people are searching for and tailor your offerings accordingly.
– Measure your marketing efforts by tracking how many people visit pages that contain certain tags (such as “jeans”). This will give you information about which tags draw people in and which ones don’t work as well.
How to implement product tagging
Implementing product tagging is a great way to expand your reach and get more customers. It can also help you improve customer engagement and retention, as well as gain valuable insight into what your customers want.
The first step in implementing product tagging is deciding which categories you’ll use to tag your products. You’ll want to make sure that the tags are easy for customers to understand, but also that they’re unique enough that it’s unlikely someone will use the same tag for two highly different products. Once you’ve decided on your categories, label each product with multiple tags that correspond to those categories. For example, if you sell clothing online and have categories of “color” and “style,” label each item with both terms so that customers can find it when searching either category—or both!
This system should work well as long as there aren’t too many items in any one category. If there are thousands of items in one category, then it might be hard for customers to find what they’re looking for even if they know all the right tags!
Methods, types of product tagging
When it comes to tagging products, there are two ways to go: manual or automatic. Manual tagging is when you have a person manually type in the relevant tags for product.
E.g. a person can choose the following tags for jacket:
Automatic tagging is when you have a program automatically generate this information using some kind of machine learning model that was previously trained on sufficient number of examples.
The difference between these methods is that automatic tagging usually provides more accurate data and results in less human error.
To summarize, there are two main types of product tagging:
– Automatic product tagging, which uses machine learning and artificial intelligence to identify elements in a product photo that indicate what the product does, such as its color or shape, and then uses this information to tag the photo with keywords. This type of tagging is often used by large retailers who have large catalogs and want to make it easier for customers to find what they’re looking for.
– Manual product tagging, which gives users the option of applying any number of tags manually when they post photos or videos of their item on social media or other platforms like Pinterest. This type of tagging is typically used by smaller businesses who don’t have access to automatic tools but still want an easy way for their customers to find their products online.
Product tagging API
First, let us clarify – what is API?
API stands for application programming interface. It is a set of routines, protocols, and tools for building software applications. The API specifies how software components should interact. For example, an API for getting data from a social media platform would define how to log into the platform, how to post a status update, and how to get information about other users on the platform.
A good API makes it easy to write software that interacts with another system. It provides all the functions needed by programmers to create applications that work with it. Good APIs are well documented and have consistent naming conventions so that programmers know what they can expect when they call a function or method.
Why use an API?
There are many reasons why businesses choose to use APIs:
* They allow for faster development by letting developers focus on solving problems instead of building new code from scratch. This means faster development times and reduced costs.
* They allow developers to build applications using existing code libraries rather than starting from scratch every time (which would be very time-consuming). This makes it easier for developers to build apps quickly without having to spend too much time learning new tools or frameworks each time they start a new project.
Here is an example output from a product tagging API solution for the product name “flower pearl bracelet set”:
{
"language": "en",
"classification": [
{
"category": "bracelet",
"value": 0.25490230321884155
},
{
"category": "pearl",
"value": 0.23528774082660675
},
{
"category": "jewelry",
"value": 0.0925285816192627
},
{
"category": "pearls",
"value": 0.04833732917904854
},
{
"category": "bracelets",
"value": 0.04579661414027214
},
{
"category": "jewellery",
"value": 0.02759299799799919
},
{
"category": "necklace",
"value": 0.02725454606115818
},
{
"category": "necklaces",
"value": 0.021142233163118362
},
{
"category": "beaded",
"value": 0.01934560015797615
},
{
"category": "gold filled",
"value": 0.01926005631685257
},
{
"category": "fashion jewelry",
"value": 0.018406618386507034
},
{
"category": "gift",
"value": 0.018269013613462448
},
{
"category": "swarovski",
"value": 0.018169162794947624
},
{
"category": "mother of pearl",
"value": 0.017738845199346542
},
{
"category": "nickel free",
"value": 0.017523707821965218
},
{
"category": "gold bracelet",
"value": 0.01752016693353653
},
{
"category": "charm bracelet",
"value": 0.017275892198085785
},
{
"category": "silver plated",
"value": 0.01697913184762001
},
{
"category": "rose gold",
"value": 0.016845911741256714
},
{
"category": "silver bracelet",
"value": 0.016497479751706123
},
{
"category": "bridesmaid_gift",
"value": 0.016496796160936356
},
{
"category": "handcrafted jewelry",
"value": 0.016319844871759415
},
{
"category": "delicate",
"value": 0.016132378950715065
},
{
"category": "gold plated",
"value": 0.015865791589021683
},
{
"category": "necklace set",
"value": 0.015838202089071274
},
{
"category": "handmade",
"value": 0.01575801707804203
},
{
"category": "dainty",
"value": 0.0156490970402956
},
{
"category": "bangle",
"value": 0.015517214313149452
},
{
"category": "dangle",
"value": 0.015409352257847786
},
{
"category": "statement jewelry",
"value": 0.01538075041025877
},
{
"category": "pendant necklace",
"value": 0.01537358295172453
},
{
"category": "handmade jewellery",
"value": 0.015366971492767334
},
{
"category": "pearl necklace",
"value": 0.015303929336369038
},
{
"category": "charm",
"value": 0.015172511339187622
},
{
"category": "gold jewelry",
"value": 0.014680236577987671
},
{
"category": "sterling silver",
"value": 0.014224332757294178
},
{
"category": "handmade jewelry",
"value": 0.014076737686991692
},
{
"category": "earrings",
"value": 0.014061669819056988
},
{
"category": "costume jewelry",
"value": 0.013714371249079704
},
{
"category": "gold necklace",
"value": 0.013697324320673943
},
{
"category": "silver necklace",
"value": 0.01365120429545641
},
{
"category": "boho jewelry",
"value": 0.013374527916312218
},
{
"category": "swarovski crystal",
"value": 0.013291953131556511
},
{
"category": "chain necklace",
"value": 0.013042600825428963
}
]
}
Machine learning models for product tagging API
Multi label classification is a type of supervised learning where we want to identify multiple classes or labels for each data item. So let’s say you’re working with product names and you want to predict what labels it should be tagged with it.
Note that you are not predicting one label, but possibly more. This is why it is called multi label classification.
Compared to e.g. standard text classification where the goal is to predict just one class for given item.
