March 26, 2020
New trends pop up every year that smart retailers use to outperform their rivals, and 2022 is no different.
As we see more and more people choosing to shop online, retailers are in a situation where they need to manage an increasing number of orders and products.
The problem that accompanies a growing product catalog is clear: for a product to be discoverable, it needs to be properly tagged and organized. And when you’re a multi-brand online store, second hand marketplace or a shopping aggregator with a catalog of hundreds of thousands and even millions of products, this can be quite a challenge.
Automatic product tagging is one of the trends that has emerged as a response to this concern.
In this guide, we’ll cover:
What is a tag? A tag is a piece of information that categories a product within the seller’s product catalog. A tag tells you everything you need to know about the product—color, size, type, brand, use, sale, etc.
Tags are what makes search possible - it’s through the use the use of tags that sellers enable shoppers to navigate around the site and filter products based on the categories they want to explore.
By having consistent, correct, relevant product tags stores are able to improve their filters, product discoverability and overall customer experience.
However, the process of manual fashion product tagging can be very inaccurate. It’s even more complicated for stores with large inventory management, where fashion product tagging can last forever.
There are two ways of tagging products: manually or using automation technology. Read on to learn the difference.
Manual product tagging is the traditional way of tagging products.
This suggests that the e-commerce shop manager or an employee physically applies product tags to the photos in the product catalog.
Like with all things manual, human error can easily happen when manually tagging products. From overlooked characteristics, misspelled words, completely wrong tags - manual tagging can result in mixed supply chains, a long digitization process of new products, and irrelevant search results that lead to poor customer experience.
To avoid these problems, the catalog information must be clean and organized, with accurate product details. If you manually conduct the retail tagging process, it will be almost impossible to achieve this.
Thankfully, there’s a much less time-consuming alternative.
As the name suggests, automatic product tagging is an automated alternative to manual product tagging.
Automatic product tagging is a method that uses AI algorithms to organize and tag photos of the product based on their details. The advanced image recognition algorithms that leverage deep learning make the tagging process automated and performed efficiently without the need for human intervention.
The automatic product tagging process automatically generates metadata for catalog assets. It scans the image and identifies patterns within that are linked to particular keywords.
As their use gets more frequent, tags created this way can collect data not only about the catalog data, but also about how and where they’re being used, who is searching for them, and how they correlate to other tags.
A human needs to catch a glimpse of an image of a clothing item to be able to decide what it is — a dress, a blouse or jeans. For computers this isn’t as easy, and that’s why a large part of inventory management is done manually, by humans.
The problem with manual inventory management is that it’s extremely time consuming, especially for retailers that have large catalogs.
Thankfully, with the advancements of Computer Vision and Deep Learning, scientists created Neural Networks that mimic the human brain. These Neural Networks can be trained to process an image the same way a human does. These advancements have made it possible for computers to “read” an image and produce semantic information in a textual form.
Put simply, if a computer scans an image of a red, long dress, it will be able to recognise it is:
As you can notice, products don’t have only one tag - they have many different fashion tags. For example, an image of a blue shirt with flowers can have several fashion tags attached by machine learning technology—”blue shirt”, “floral shirt”, “slim-fit”, “formal”, “long-sleeve”, “buttoned-shirt”, etc.
This is a huge breakthrough and can make the operations of retail businesses much easier, simpler, and less time consuming.
But how do computers learn how to read an image and understand what’s in it?
In order for AI systems and computer vision-based processes to recognize if a fashion item is in an image, they need to learn from a lot of images on how clothing and apparel look and what attributes can be used to describe them.
An automatic product tagging is simply a trained AI system that has seen thousands of previously tagged fashion images, and has learned from them. Now, it can do the same - perform fashion tagging using various categories and attributes.
During the automatic tagging process, the Deep Learning algorithm processes images or videos, extracts their characteristics, and discovers relevant objects.
By using automatic tagging technology, retailers can rest assured their tags and labels are accurate, rich and using a consistent taxonomy.
The first “sights” of automatic image tagging date from the end of 2010, when Facebook introduced its facial recognition feature.
This service was supposed to eliminate manual image tagging - the system was able to immediately recognize who’s on an image and suggest who should be tagged when people uploaded photos of their friends.
Many similar solutions were brought to the market after that. However, the full potential of this technology was yet to be discovered.
Realizing that images are full of untapped data, companies wanted to find a way to process this data and leverage it to grow and expand their operations.
In 2015, Google was probably further than anyone else when it came to processing image data. They launched Google Photos and enabled advanced photo sorting based on their advanced image recognition technology. The modern Google Photos app includes an automatic face tagging feature as well.
In 2017, Facebook rolled out an update of their automatic tagging features which could now find photos where users appeared and help them find out when others were publishing an image they’re on.
The next upgrade of the technology came from Google that same year, when they started offering various auto tagging and automatic sorting options. Now you could sort images by date, place, and visual features, like “red”.
These advancements paved the way for the use of automatic tagging in e-commerce.
In 2017, Pinterest was one of the first to introduce automatic image recognition, after the launch of their visual search engine in 2015.
This new feature skyrocketed the platform’s popularity, especially because people were using the app mostly for its visual nature, which was now upgraded.
Then, the first sellers started using AI technology for auto tagging.
Automatic tagging of products in ecommerce has slowly, but surely, become the preferred way to do things.
Even social media platforms like Facebook and Instagram have started using it - pictures now have product tags with information on the items that users can buy from a storefront.
Now, it’s fashion retailers’ move. The trends and data shows that more and more fashion retailers adopt this technology to improve operations and customer experience.
But this is all just the start.
As deep learning technology evolves, we expect to see many more features that will transform digital selling forever.
Let’s imagine a situation where a shopper is looking for a black silk dress in your e-commerce store. However, when they enter the search term, they get an orange skirt. They would be disappointed, right?
This is probably the worst nightmare for any ecommerce store. If website visitors encounter a situation like this, they will get very frustrated and abandon your site.
In fact, Janrain research discovered that 74% of online customers get annoyed when they come across content that is irrelevant. If a company's search feature falls short of expectations, the consumer would be disappointed.
To prevent this, all products in the product catalog have to be well organized and tagged with the correct terms and keywords. However, manual product tagging is unrealistic and impossible to implement given the volume of products offered online today, the growing number of online retailers, the vast magnitude of different categories, and the the need to combine visual features with product descriptions and function requirements.
Enter automatic image tagging - one of the most important AI-driven products used by fashion marketplaces in the 2020s.
Online retailers get tons of benefits by using automated product tagging. These benefits are a result of the richness and consistency of the product tags. It is ultimately product data that powers the rest of the systems in an ecommerce store.
To put it simply, automatic tagging helps in four main ways.
First, rich tags help you get deep insights for shoppers. This data ultimately powers personalized shopping experiences and more relevant recommendations.
Second, rich tags help you improve product discoverability.
By combining these two, you’ll make it easier for shoppers to find what they want and you’ll remove friction from the buying experience, which leads to increased sales.
Finally, by automating manual tasks, employees are able to make a better use of their time, by focusing on high value tasks vs. data entry.
#1.1 Better understand your shoppers
Automatic product tagging makes the e-commerce website more personalized. Every click on a tag makes the system aware of the different expectations of shoppers, which, paired with the data-driven historical behavior, gives the right answer to the question: “What does the shopper really want to see?” It allows the customization engine to be much more efficient and precise when reading the purpose of the shoppers.
#2.1 Serve more relevant recommendations
Netflix saves around $1 billion on an annual basis through customer retention, thanks to personalized content. High-performing e-commerce relies heavily on data. People's buying preferences in fashion, for instance, are primarily based on what they find visually appealing, as well as what they believe enhances their unique traits and fits well with other clothing pieces.
Deep Learning algorithms enable you to detect and comprehend your products' output as well as extract multidimensional attributes. You can learn a lot about your consumers' attitudes, tastes, and demographic and geographic characteristics by using automated product labeling. Through a greater number of informative apparel logos, you can learn more about what customers want and develop more customized deals. This enables you to track and evaluate patterns, allowing you to prepare your e-commerce store for potential customers.
But to make this all possible, you need to have rich tags powering your personalized recommendations.
Since product auto-tagging technology attaches richer content to each product, giving the personalization system more information to work with and understand what kind of products the users like. This info comes from the combination of the shopper’s search history and the tags.
This is valuable data that apart from giving your customers exactly what they want, ultimately enables you to make better business decisions.
#2.1 Richer product data & comprehensive navigation
Everything you see on the front-end of an ecommerce store such as navigation and product categories is powered by product information and tags in the back-end. Hence, having richer and more accurate tags makes it easier for ecommerce store managers to support instant product exploration and make site navigation more intuitive and comprehensive.
With automated product tagging, you should be able to enrich your data through a multi-level fashion product tagging structure, with hundreds of product tags at each level. You can also add mutually-exclusive product tags that refer to a particular attribute of a certain product. For example, you can have “accessory” as a category, and “necklace” as a subcategory. These subcategories, on the other hand, can have more attribute tags that enrich product data, like “large”, “golden”, “asymmetric”, etc.
All this allows customers to make sense of your catalog much more easily and find your products much faster.
#2.2 Improved catalog management
Accurate visual attributes of products enable streamlined and well-organized backend product processes. Product catalog pictures with precise product tags allow retail stores to track sales, discover the most searched products, find out and eliminate products that aren’t popular, and always have stocks under control.
Automated product tagging can organize products based on different criteria e.g. brand, design, style, color, and other criteria.
More product tags can be added from time to time so that the product catalog will always remain updated with fashion trends. These tags can seriously impact buying decisions and therefore bring deeper insights into attribute-level sales analytics.
Incorporating a fashion tagging engine in the e-commerce store can improve the decisions managers make, as it allows them to have a better overview of the products from their online retail store.
#2.3. Better search engine ranking (SEO optimization)
Automatic tagging doesn’t only improve catalog management—it can also be a serious SEO booster. Being one of the few low-cost marketing strategies still available, SEO is an important strategic goal for ecommerce companies.
How does fashion tagging help?
Providing keyword-friendly tags to other content-rich areas of the online retail platform, making tags accessible and, most importantly, clickable for visitors will improve the website's SEO and position on the Search Engine Results Page (SERP).
The more valuable and accurate a product image tag is, the greater its ability is to appear in search results. Since automated tags automatically annotate images that have particular tag categories and reference marks, they can be a great SEO booster.
Additionally, automatic product tagging reorganizes the internal hierarchical linking system of the website, placing connections on strategically better positions around the web.
This is especially useful because Google’s AI-based algorithm now understands search context and considers it when ranking results.
#3.1 Reduce shopping cart abandonment rate
With automatic product tagging, customers can get to the products they want or need more easily.
Sometimes, the product they want can be unavailable, so they leave the store thinking that there’s no other alternative. Shoppers abandon their shopping carts for many reasons and products being out of stock is one of the most common reasons why this happens, leading to an average cart abandonment of 83.6% for some industries.
Product tags can reduce these losses by encouraging the consumer to choose from a wide range of related products that can be an alternative to the unavailable product. This encourages shoppers to spend more time browsing and reduces bounce rates.
#3.2 Increase Average Order Value (AOV)
Every online retail store has the goal to get customers to stay as much as possible on their websites and end their visits with a purchase. As automatic tagging connects products with e-commerce product tags, it allows store owners to recommend customers more products during their checkout process.
So, customers will find even more related products they like, adding them to their shopping carts and buying more products than initially planned.
For example, if the shopper is looking for a summer dress, they will be looking at dresses with the “summer dress” tag. However, if they are getting ready for the summer, they could probably also use some sandals or t-shirts the system can offer with the tag “summer”.
They’ve probably only come to your store for the dress, but the auto tagging system is here to remind them to buy other summer items as well. Accurate product tags can enable suggesting other relevant summer items, eventually increasing sales figures.
As a bonus point, clean and correct tags can reduce returns because buyers will get more information about the product, so they won’t be surprised when the fashion item is delivered to their doorsteps.
#4.1 More time for important tasks
Advanced image algorithms allow the entire product image tagging process to be performed in a single day, replacing weeks of human effort. An automatic fashion product tagging model can increase catalog processing time by up to 90%.
This means that product & data managers and fashion marketplace employees have more time left for other more important tasks. They can focus on improving customer experience, create a smoother buyer journey, and better supporting the commercial team thout having to hire new people to do all these tasks.
Product tagging is the backbone of every ecommerce store. A catalog of descriptive, rich, accurate tags improves product indexing and resuls in more accurate search queries, more relevant recommendations and a truly personalized user experience. This in turn leads to lower bounce rates, more products added to carts and ultimately increased sales.
An automatic tagging tool like Pixyle.ai will reduce the time it takes you to tag products, and most importantly - will improve the accuracy and richness of the fashion tags that power better product discoverability.
See it for yourself:
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