August 13, 2021
The shopping experience can sometimes be quite complex. Let’s say that your friend’s wedding is approaching and you want to buy something to wear. So, you walk into a fashion store to look for a dress or a suit. Or, maybe your brother’s birthday is coming next week and you go to the mall to search for a gift. In both cases, you have thought about some options but didn’t really decide on anything specific.
Now, imagine the same thing, but online. A user comes across your e-commerce fashion store with the intention to buy a gift. However, they don’t have anything specific in mind so your store has to offer various choices for them to decide. What is more important, these choices have to be relevant and adjusted to the user’s preferences.
75 percent of customers' search inquiries are brand new each month, according to Salesforce and Publicis.Sapient, demonstrating how quickly consumers are finding new product kinds, brands, and features. But, finding a product online, especially when you’re not too specific, can be tricky. In a physical store, you can approach a sales assistant and ask for advice and recommendations. Online, you need to boil down your thoughts into searchable terms in order to get to the result. If you do this successfully, you’ll find a convenient product.
As the fashion e-commerce market becomes increasingly competitive, concentrating on seamless product discovery may help you stand out. Product discovery experiences, when done effectively, should minimize barriers between searching and purchase, as well as fully understand each customer's thoughts and wants. This customized method of organizing products, called product tagging, promotes consumer loyalty as well as sales.
Consumers who use a website or app that has poor product discovery capabilities spend more time just trying to find anything they want to buy. In fact, According to PwC, one out of every three clients will forsake a beloved brand after a single unfavorable encounter, and 92% would totally abandon a brand after two or three negative interactions.
To avoid this problem, we suggest product tagging. Object metadata, often known as product tags, is a collection of product attributes. Each item in an e-commerce site has many tags attached to it that define its features, attributes, and category. Because each product is unique, there are distinct tags for each one.
These product attributes provide a detailed explanation and expertise to your base-level information to assist you better grasp the product's purpose and characteristics. Color, material, pattern, style, occasion, and a variety of other characteristics are all included in these tags. Product tagging is the process of adding tags to the products in the product catalog.
By adding attribute metadata to your products, you may improve search features, product ratings, and product reviews for your customers. On the business side, you also streamline the inventory management process.
Manual tagging is the "classic" method of product tagging. This implies that the e-commerce store manager or employee spends weeks, if not months, manually adding product tags to the product catalog pictures. This is a highly inefficient method of doing things, especially when there are a lot of items in the store. Furthermore, manual product tagging can lead to poor catalog management, which can result in a number of issues:
To avoid these issues, catalog data must be well-organized and contain correct product information. It will be very hard to do this if you execute the retail tagging procedure manually.
There is, however, another option. With the growth of AI (Artificial Intelligence), new techniques of automating retail activities, such as product tagging, have emerged. To properly arrange the product catalog, AI-based tagging, also known as automatic tagging, is replacing human tagging, saving a lot of time and work. Let's take a closer look.
Automatic tagging classifies and tags pictures in the product catalog depending on their characteristics, using sophisticated AI algorithms. Thanks to Deep Learning, these algorithms speed up the tagging process, making it automated and removing the need for human intervention. This is a technique for generating metadata for catalog assets. It works by examining an image and identifying features associated with particular keywords.
We simply need a glance at a snapshot to recognize a clothing item and determine whether it's a skirt or a dress. Because this is a difficult task for machines, individuals still undertake the conventional process of catalog tagging. Automatic tagging, on the other hand, can totally replace the manual tagging procedure. Automatic tagging is a trained AI system that can detect clothing in photographs in the same way that people can.
Thanks to breakthroughs in Computer Vision and Deep Learning, scientists were able to create Neural Networks that mimicked the human brain. They can be trained to recognize what is in an image in the same way that we humans can. These neural networks will take a picture, analyze it, and then return us words with semantic understanding.
To recognize when a fashion piece is in an image, AI tagging systems must learn from a large number of images of how garments and apparel appear and what features may be utilized to identify them. Automatic fashion tagging is just that—a skilled AI system that has been trained with thousands of fashion pictures.
Automatic tagging can add many attribute labels to every photo. These attribute tags are far more than conventional tags; they provide in-depth product observations. After a while, in addition to collecting catalog data, tags gather information on their own usage, who uses them, and their connections to other tags.
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Consumer expectations are always changing, along with the progress of new technologies. It's critical for businesses to not only satisfy but also surpass the expectations of their target customers in order to create and nurture an outstanding shopping experience.
Regardless of the number of channels you're using, it'll always come down to the value you give to customers. Connecting customers to the items they're looking for in a simple, personable, and seamless way will make them think of your brand as the first association to online shopping.
Automatic tagging minimizes the time it takes to tag objects, improves the accuracy of tags and search results on the website, and contributes significantly to reducing operational expenses. Brands should utilize automatic tagging to remove the human effort and, as a result, reduce the possibilities of making a tagging error. It will also shorten the time to market for new products by automating the entire digitization process.
Pixyle.ai's automatic product tagging engine can help you acquire a clean catalog, increase your SEO rating, and maximize your sales volumes by searching the whole product catalog in only a few seconds. To see how it works, try our demo now.