February 5, 2021
Online retail stores have irrevocably disrupted the shopping world. The backstage operations of design, merchandising, production, and other touchpoints in the consumer journey have been completely transformed. Retailers have more variety to satisfy quickly evolving consumer demand through their product catalog.
Although the coronavirus pandemic has caused a decline in shopping worldwide, the fashion e-commerce market is expected to recover. According to ReportLinker, the market will reach $672.71 billion in 2023, growing at a CAGR (Compound Annual Growth Rate) of 11.48% during the forecast period (2020-2023).
As most shopping is going online, retailers are expected to handle large numbers of orders and items at a high level. This is a challenging task, especially for stores that offer products from multiple brands.
One of the solutions to this concern is product tagging. It improves the entire organization of the product catalog. Moreover, since they have the right keywords as their tags, it makes products more relevant.
In 2021, we expect to see new technologies taking over retail processes, including catalog management and product tagging. This article will talk about product tagging in 2021 and AI-driven product tagging as a better alternative.
Product tags, known as object metadata, are a collection of product attributes. Each e-commerce store item consists of several tags set to identify its features, characteristics, and the category that it is part of. There are different tags for every product since every product is unique.
To expand your base-level information, they add a comprehensive description and knowledge that help better understand the purpose and the features of the product. These tags include color, height, style, brand, use, price, and many other attributes. The process of adding these tags is known as product tagging.
You boost search features, product ratings, and product reviews for the consumer by applying attribute metadata to your items. You also streamline the inventory management process on the company side.
The “traditional” way of product tagging is manual. This means that the e-commerce store manager or an employee spends weeks, sometimes even months, manually adding product tags to the images in the product catalog. This is a very inefficient way of doing things, especially when the store has a large number of products. What is more, manual product tagging can lead to lousy catalog management, resulting in several problems:
To prevent these problems, the catalog data must be clean and structured, with accurate product information. If you perform the retail tagging process manually, achieving this will be almost impossible.
However, there’s an alternative. The rise of AI (Artificial Intelligence) has introduced new ways of automating retail processes, including product tagging. AI-based tagging, known as automatic tagging, is replacing manual tagging to efficiently organize the product catalog, saving a lot of time and energy. Let’s dive deeper.
In the product catalog, automatic tagging organizes and tags photos based on their features, leveraging advanced AI algorithms. These algorithms speed up the tagging process, making it automatic and eliminating the need for human interaction, thanks to Deep Learning. This is a method that creates catalog asset metadata. It operates in a way that examines the image and identifies characteristics linked to specific keywords.
To recognize a clothing piece and determine whether it's a skirt or a dress, we only need a glimpse of a photo. This is not an easy job for machines, and that is why people perform the traditional process of catalog tagging. However, automatic tagging can completely replace the manual tagging process. Automatic tagging is a qualified AI system that can recognize clothes in pictures like we humans do.
Scientists developed Neural Networks that replicate the human brain thanks to Computer Vision and Deep Learning advances. They can be taught to identify what is in a picture just as we humans do. These neural networks will take an image, process it, and send us semantic knowledge in the form of text.
AI tagging systems need to learn from a lot of photographs of how clothes and clothing look and what characteristics can be used to identify them to know when a fashion object is in an image. And this is precisely what automatic fashion tagging is—a qualified AI system that has been trained with thousands of fashion photos.
Any photo can get multiple attribute labels added with automatic tagging. These attribute tags are far more than ordinary tags, offering profound, detailed product observations. After a while, tags gather information about their use, who uses them in particular, and their links to other tags, apart from only collecting catalog data.
Here are some of the most important benefits of automatic tagging:
Automatic tagging reduces the time it takes for items to be labeled, increases the accuracy of tags and search results on the website, and plays a significant role in lowering operating costs. In order to eliminate human effort, brands should use automatic tagging and thereby decrease the chances of making an error in the tagging process. In addition, it will reduce the time-to-market of new products, automating the whole process of digitization.
To find out more about product tagging, check out our complete guide for automatic tagging in e-commerce.
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