March 26, 2020
E-commerce is an ever-changing market.
New trends pop up every year that smart retailers use to outperform their rivals, and this year 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 catalogue is clear: for a product to be discoverable, it needs to be properly tagged and organised.
And when you’re a multi-brand online store, second hand marketplace or a shopping aggregator with a catalogue of hundreds of thousands and even millions of products, this can be quite a challenge.
Automated product tagging is one of the trends that has emerged as a response to this concern.
Product tagging is like the bridge between your products and the online world.
It's a big deal, whether you're a big retailer or just starting out.
It's all about how you organise, show, and promote your products online, especially as everyone looks for cost-effective ways to grow.
These digital labels, known as product tags, meticulously describe the attributes of products enabling easy navigation and discovery for customers.
They help them easily find what they're looking for and boost your online store's traffic, sales, and overall success.
The retail industry's advancement hinges on effective product tagging to attract tech-savvy consumers.
In 2023, global e-commerce sales are projected to reach a [staggering $6.5 trillion], constituting 22% of retail sales worldwide.
According to Statista, globally, retail e-commerce sales reached around $5.2 trillion in 2021.
Predictions indicate a 56% surge in this value, with an estimated 8.1 trillion dollars expected by 2026.
With this growth comes the challenge of managing extensive inventories that can easily overwhelm and frustrate shoppers, leading to cart abandonment and missed opportunities.
In the digital age, data is really important, and product details are the foundation for successful online stores.
They include lots of info like images, names, prices, and more, helping stores manage products well and make it easy for customers to shop, even when there are tons of things to choose from.
This guide shows how excited we are about using AI to improve online shopping and our strong dedication to making it easy for everyone to find products they love.
In the upcoming chapters, we'll teach you all about product tagging and how AI is changing it fast.
By the end, you'll have the know-how to use product tagging effectively, creating an outstanding shopping experience that keeps customers coming back and boosts your business.
Now, you might be wondering, "Why are product tags so crucial in the e-commerce cosmos?" Let’s dive right in.
Improving Discoverability and Navigation
Tags are crucial for guiding your customers through your online store.
They make it easy for shoppers to browse your products by allowing them to filter and navigate through different categories.
This ultimately results in a better and more convenient shopping experience.
Elevating Customer Experience
In online shopping, providing customers with a flawless experience is essential.
Keeping product tags accurate and updated helps customers easily find what they're looking for, whether they're using the search bar, navigating through menus, or applying filters.
A smooth shopping experience leads to happy customers.
Personalised Product Recommendations
Thorough product tagging reveals important details about what attracts your customers.
With this information, you can provide personalised product suggestions, making the customer experience even better and increasing sales.
Proper product tagging goes beyond helping customers. It also improves the way things work behind the scenes.
It ensures accurate reporting of sales, costs, and demand and supply data, making it easier to manage your inventory efficiently.
Visibility and Marketing
Product tags are not just about navigation; they're also a potent marketing tool.
They enable potential customers to discover your products efficiently through search engines, advertisements, and social media.
Effective tagging can boost your brand's online visibility, streamline sales tracking, and unlock new avenues for cross-selling and upselling.
Now, let's take a journey through the exciting evolution of product tagging in the world of e-commerce. It's like tracing how your favourite app or gadget has evolved over the years – always improving, getting smarter, and making our lives easier.
The Old School Days
In the past, product tagging was comparable to tidying up a messy closet.
It involved a somewhat manual process where the merchandising team had the important task of creating, assigning, and managing labels for products. It was akin to using sticky notes on everything just to keep track of your belongings.
The Birth of Digital Tags
Then came the [digital revolution](https://www.worldatlas.com/articles/what-was-the-digital-revolution.html#:~:text=The Digital Revolution Defined&text=During this time%2C digital computers,way to the Information Age.).
Product tagging took a leap into the digital realm. Instead of actual sticky notes, we had digital tags.
Brands started attaching metadata to products, and these digital labels contained everything from brand names, colours, sizes, and types. It was like finally moving from paper to a computer – things got way more organised and searchable.
The Game-Changer: E-commerce Boom
With the rise of e-commerce, product tagging became the superstar. Online shopping went through the roof, especially after the pandemic hit.
People started buying everything online, from groceries to gadgets. And guess what? Product tagging played a pivotal role.
A Personalised Touch
As online shopping gained popularity, people desired a more personalised experience.
They wanted to locate items swiftly and effortlessly.
This is where comprehensive product tagging played a crucial role. It was akin to transitioning from a disorganised library to a meticulously catalogued one – shoppers could easily find what they needed.
Smart Tagging for Smarter Shopping
Today, we're in the era of smart tagging.
It's like having a personal shopper who knows your tastes inside out.
These tags not only help you find products but also give retailers insights into what you might like. It's like the app store recommending apps you didn't even know you needed.
2010: It all began with Facebook
The first “sights” of automated 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.
Realising 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.
2015: Google launches Google Photos and becomes “disturbingly good at data-mining your photos”
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 automated face tagging feature as well.
2017: Google & Facebook make advancements, Pinterest marches ahead
In 2017, Facebook rolled out an update of their automated 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 automated sorting options. Now you could sort images by date, place, and visual features, like “red”.
These advancements paved the way for the use of automated tagging in e-commerce.
In 2017, Pinterest was one of the first to introduce automated 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.
The rest is history.
The Future Looks Bright
So, what's next for product tagging? Well, the future looks incredibly exciting.
We're talking about more efficient searches, better recommendations, and even cooler ways to organise your virtual shelves. It's like going from a smartphone to a full-fledged smart home – convenience and innovation all the way.
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 catalogue have to be well organised 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 need to combine visual features with product descriptions and function requirements.
Enter automated image tagging - one of the most important AI-driven products used by fashion marketplaces in the 2020s.
In the world of e-commerce, the process of product tagging is like the navigation system of a ship. It directs products to their intended destinations, ensuring they reach the right customers.
But just as ancient mariners had to rely on the stars to guide their way, e-commerce companies have had their own set of challenges when it comes to product tagging.
Imagine manually tagging thousands of products in an online store.
It's a bit like trying to count all the grains of sand on a beach – an overwhelming and practically impossible task. Manual product tagging can be a daunting challenge for several reasons:
Human Error: Humans are, well, human. We make mistakes, and in the world of product tagging, even small errors can lead to big problems. A simple typo or oversight can result in products not showing up in relevant searches, causing frustration for both customers and retailers.
Time-Consuming: Manual tagging is incredibly time-consuming. It diverts valuable human resources away from more strategic tasks, such as optimising product listings, analysing customer data, or devising marketing strategies.
Scalability Issues: As your online store grows, so does the number of products. Manual tagging becomes increasingly unwieldy as your inventory expands. It's like trying to juggle more and more balls – eventually, you'll drop some.
Inconsistency: Different employees may tag products differently, leading to inconsistent product listings. This inconsistency can confuse customers and make your store appear unprofessional.
Now, let's navigate away from the challenges and set our course toward a more efficient solution.
As the name suggests, automated product tagging is an automated alternative to manual product tagging.
Automated product tagging is a method that uses AI algorithms to organise 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 automated product tagging process automatically generates metadata for catalogue 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 catalogue 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 catalogues.
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 automated 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 automated tagging process, the Deep Learning algorithm processes images or videos, extracts their characteristics, and discovers relevant objects.
By using automated tagging technology, retailers can rest assured their tags and labels are accurate, rich and using a consistent taxonomy.
Then, the first sellers started using AI technology for auto tagging.
Automated 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 a 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.
Enter automated product tagging solutions – the North Star for e-commerce businesses navigating the tagging seas.
These AI-powered solutions bring a breath of fresh air to the often turbulent waters of product tagging.
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, automated tagging helps in four main ways.
First, rich tags help you get deep insights for shoppers. This data ultimately powers personalised 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
Automated product tagging makes the e-commerce website more personalised.
Every click on a tag makes the system aware of the different expectations of shoppers, which, paired with the data-driven historical behaviour, 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.
1.2 Serve more relevant recommendations
Netflix saves around $1 billion on an annual basis through customer retention, thanks to personalised 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 labelling.
Through a greater number of informative apparel logos, you can learn more about what customers want and develop more customised 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 personalised 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 catalogue much more easily and find your products much faster.
2.2 Improved catalogue management
Accurate visual attributes of products enable streamlined and well-organised backend product processes.
Product catalogue 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 organise products based on different criteria e.g. brand, design, style, colour, and other criteria.
More product tags can be added from time to time so that the product catalogue 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)
Automated tagging doesn’t only improve catalogue 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, automated product tagging reorganises 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 automated 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 automated 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 automated fashion product tagging model can increase catalogue 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, creating a smoother buyer journey, and better supporting the commercial team without having to hire new people to do all these tasks.
Augmented tags represent the next phase of evolution in product tagging, integrating AI-generated metadata with human-generated tags to create a more comprehensive and accurate description of each product.
By combining the power of AI algorithms with human understanding, augmented tags can significantly enhance the depth and richness of product data, enabling a more nuanced and detailed understanding of each item within a catalogue.
AI-generated tags are created using advanced computer programs that examine different aspects of a product, like its colour, style, and material.
These tags are generated automatically by the system, using complex algorithms trained on extensive datasets to identify patterns and features within images.
The AI can identify subtle details that a human might miss, resulting in a more accurate and detailed collection of tags.
On the flip side, human-generated tags offer a deeper understanding and interpretation of the product's features.
They bring in a human touch, adding qualitative insights that enrich the tags, capturing subtle details that might not be obvious from just looking at an image.
Human-generated tags also consider cultural and contextual knowledge, which is vital in meeting the varied preferences and expectations of diverse customers.
When combined, these augmented tags form a strong and detailed information framework that enhances the product data.
This mix of AI-generated and human-generated tags provides a more detailed and accurate view of each product, leading to better search results, personalised suggestions, and improved experiences for customers.
Implementing augmented tags leads to a noticeable improvement in how customers find products, resulting in more engagement and higher conversion rates.
By combining the power of AI-generated and human-generated tags, e-commerce platforms can offer various benefits and advantages. Look at the real examples that prove these advantages.
More Accurate Search Results
Example: Suppose a customer is searching for "casual women's shoes" on an e-commerce platform.
With the implementation of augmented tags, the search results will display a comprehensive range of options, including various styles, colours, and materials, catering to the diverse preferences and needs of customers seeking casual footwear.
This ensures that the search results are more accurate and relevant, leading to a better user experience.
Example: A customer frequently browses and purchases athletic wear from an e-commerce platform.
By leveraging augmented tags, the system can analyse the customer's past purchases, preferences, and browsing history.
This data, combined with AI-generated and human-generated tags, enables the platform to offer tailored recommendations, such as suggesting matching accessories, complementary workout gear, or new arrivals from the customer's favourite brands, enhancing the customer's shopping experience.
Effective Cross-Selling and Upselling
Example: A customer adds a smartphone to their cart on an e-commerce platform.
Leveraging augmented tags, the platform can identify complementary products, such as phone cases, screen protectors, or wireless chargers, that align with the customer's preferences and the specifications of the chosen smartphone.
By displaying these related products during the checkout process, the platform effectively implements cross-selling and upselling strategies, leading to increased sales and customer satisfaction.
Improved Visual Merchandising
Example: An e-commerce platform specialises in home decor and furnishings.
With augmented tags, the platform can curate visually cohesive product displays that align with various interior design styles, such as modern, minimalist, or bohemian.
By leveraging the combined power of AI-generated and human-generated tags, the platform can showcase products in visually appealing settings, providing customers with inspirational and contextually relevant displays that resonate with their preferences and help them envision the products in their own homes.
In the dynamic world of online shopping, the accuracy of product tagging is crucial for engaging customers and ensuring a smooth shopping experience.
When tags are accurate, customers can easily find the products they want, leading to happier customers and more successful sales.
On the other hand, if tags are inconsistent or incorrect, customers can become frustrated, which can hurt brand loyalty and reduce the likelihood of customers returning.
Additionally, the accuracy of product tagging has a direct impact on search engine optimization (SEO) strategies.
When tags are precise and relevant, they help improve the visibility of products on search engine results pages (SERPs).
This, in turn, helps e-commerce platforms expand their online presence and reach more potential customers.
Conversely, if tags are inaccurate or misleading, it can negatively affect SEO performance, leading to lower organic traffic and reduced brand visibility.
Human validation and verification of tags help to catch any mistakes or inconsistencies, improving the overall quality and trustworthiness of the product information.
Manual QA involves careful examination and confirmation of product tags by experienced professionals to make sure that each tag correctly represents the relevant product details.
By utilising manual QA, e-commerce platforms can maintain high standards of tagging accuracy, reducing the risk of incorrect or confusing tags that might negatively impact how customers interact with the platform.
Manual validation allows for the identification and correction of any errors or discrepancies, leading to the improvement and optimization of product tags to better match customer expectations and search preferences.
Human-validated attributes are crucial for making sure that product tags are consistent and dependable, creating a unified tagging system that improves the overall customer experience.
By carefully checking each tag, e-commerce platforms can maintain a reliable and uniform tagging framework, making it easier for customers to navigate and find what they're looking for.
Furthermore, they help create a complete and trustworthy product classification system that matches what customers expect and how they search.
By using human-validated attributes, e-commerce platforms can ensure that each product tag accurately represents the relevant product details, helping customers find the right products quickly and easily.
By highlighting the importance of human validation and manual QA processes, e-commerce platforms can maintain high standards of tagging accuracy, creating a strong and dependable product data structure that improves customer interaction and satisfaction.
Human-validated attributes are essential for building a customer-focused and user-friendly e-commerce platform, enhancing the overall shopping experience and promoting long-term customer loyalty.
Different sectors in e-commerce have their own specific challenges and needs for product tagging.
Understanding these unique requirements is key to creating tailored tagging strategies that cater to the preferences of customers in each industry.
Fashion and Apparel: The fashion and apparel industry demands a detailed approach to product tagging, considering the various aspects of clothing and accessories.
Using specific fashion tags like fabric type, style, pattern, and fit can greatly improve product discoverability in this sector.
Electronics and Gadgets: The electronics and gadgets sector requires a precise tagging strategy that includes technical specifications and features.
Using a comprehensive set of tags that highlight key technical attributes, such as device specifications, compatibility, and performance metrics, can help customers make informed purchase decisions.
Home and Living: The home and living sector needs a comprehensive tagging approach that focuses on design aesthetics and functional attributes.
Using diverse tags that cover interior design styles, material compositions, and product functionalities can help customers find relevant home and living products that match their preferences.
Beauty and Cosmetics: The beauty and cosmetics industry requires a detailed and descriptive tagging approach that includes product attributes like ingredients, formulations, and application techniques.
Using a variety of beauty-specific tags, such as skin type suitability, product benefits, and application methods, can improve product discovery and personalised recommendations in this industry.
Today, effective product tagging plays a crucial role in boosting sales and improving product visibility.
This guide has provided a comprehensive understanding of product tagging, from its historical evolution to the latest AI-driven solutions.
By highlighting the benefits of automated tagging and the importance of manual quality assurance, businesses can ensure accurate and reliable product data.
Additionally, the guide has emphasised the significance of industry-specific tagging strategies, underscoring the need for tailored approaches in various sectors.
As businesses strive to stay ahead in the dynamic world of online retail, mastering the art of product tagging remains a key to success.