Describing clothing items is difficult. Sure, you can mention the color and maybe some distinct features like sleeve length. After that, talking about the pattern and overall style is challenging. Unless you are a fashion expert, browsing for fashion products using conventional text searches is impossible. Here is where deep tagging for e-commerce comes in.
The reason behind this is because fashion is visual. People can’t put their favorite style to words. But, they can recognize something they like as soon as they see it. The solution to this is using an image search tool. Let your customers tell you what they want by providing image content! Still, this is impossible without using deep tagging for e-commerce.
What Is Deep Tagging in e-Commerce?
Deep tagging uses image recognition and computer vision. It extracts product features from the image content. It’s useful for fashion purposes. Let’s check out how AI image tagging works on the example below. First, try to describe her top as detailed as you can. Sure, it’s white with short sleeves. However, other features are difficult to pinpoint for most users. You don’t want to ask for any excessive hassle from your customers. The process of online shopping needs to be smooth and easy. An image search is a simple solution. Customers are often inspired by public figures and their wardrobe. They can use their social media photos as search queries.
Even a plain t-shirt can have so many characteristics. Imagine more complex items! AI auto-tagging can describe each image on similar images much better than your or customers. Visual recommendations work using deep tagging. First, all items in your inventory need to go through AI image tagging. Now you have a database of well-described products. When a user uploads image content to your search engine, the algorithm will auto-tag it the same way.
Finally, the image search tool will compare the tags from the user-uploaded photo to items in your inventory. The last step is to present the results to your customer. If all tags match a product in your catalog, that’s probably what your customer wants! If not, the AI image tagging algorithm should find similar items where the correlation is not 100%.
Why Catalog Optimization Is Important
A deep-tagged catalog has many advantages under the surface. It’s so rewarding that it makes the road to it worthy. The steps consist of designing an AI auto-tagging algorithm and providing the best visual recommendations to customers. Here are the most notable advantages of catalog optimization:
Easy, user-friendly image search tools
We’ve already mentioned visual recommendations as something that can’t exist without a deep-tagged catalog. It’s a win-win situation that satisfies both parties. Customers can use image search queries for their favorite products. Retailers now have an optimized inventory for other purposes on this list!
Credits: Forever 21 Adds AI-Powered Search, Navigation – Consumer Goods Technology
SEO and Google Product Searches
Not everyone is short on words when it comes to specific product searches. Some people know how to describe their favorite product, and they use Google to search for it. Unfortunately, the search often doesn’t return anything similar to their desired item. The fault, of course, is with the retailer.
Their products are not well described in their online catalog. For example, the short-sleeved white top from above is probably described just like that in the catalog. There are no specific details that make it unique for customers who know what they want.
With deep tagging, you can take SEO for e-commerce to the next level.
After you perform AI image tagging on your product catalog, all of your items can be described automatically with many textual details. All you need to do is to improve SEO for e-commerce by including these details in your web store. It will pop up in specific Google searches, and drive traffic to your website.
The search query on the image above returns satisfying results, but they are mostly from major retailers like Amazon and AliExpress. They excel at image tagging and using AI. Imagine your products ranking next to retail leaders!
Get to Know Your Customers
Before deep tagging, your insight into your customers’ preferences was limited. Today, they might buy a white shirt, and tomorrow they will buy a black jacket. It doesn’t provide enough information to customize their preferences in the right way.
An image search can tell you more. It contains information about your customer’s style. Sure, they might be looking for a single item from the picture, but they might see the same bundle in your store. Before you know it, they’re buying everything from the photo because they like how it looks on someone else.
It provides a unique insight that can be used to display relevant product recommendations. The key is not getting customers to buy a product. You want them to browse your store long enough, buy multiple items, and come back tomorrow!
How Does Deep Tagging Work?
Deep tagging for e-commerce, under the surface, is an AI auto-tagging algorithm. This algorithm can be based on neural networks or machine learning for image tagging. Either way, this algorithm has seen plenty of manually tagged fashion photos. In simple words, after seeing a million properly tagged images, it can autonomously analyze the one million first.
The process goes like this:
- Data scientists gather data, fashion images in this case.
- Images are manually tagged to show the AI algorithms how it’s done.
- AI trains using this data + complex math, algebra, and code.
- It works! Sort of… but it can always improve.
- Once the accuracy is satisfying, AI can tag images on its own.
The level of automation with deep tagging is high. Even if you ignore all other benefits, someone has to tag your catalog. If you are about to open a huge online store with thousands of products, someone has to go through this process. It’s boring, repetitive and prone to mistakes. AI can’t get bored, and it doesn’t make mistakes due to the lack of sleep!
An image search beats text each time. Not everyone is familiar with the concept because retailers still haven’t adopted it as a standard. AI image tagging is a revolutionary concept heaven-made for e-commerce. An algorithm is auto-tagging your online inventory. Users upload visual content to your image search engine, containing products of interest. Image recognition and computer vision work to auto-tag this image as well. The goal is to find similar items in your store and present them to customers.
The concept is easy, but image tagging and computer vision are complex tasks. Once you set them up, benefits to your store will stack one on another. It includes better insight into your customers’ spending habits, which is gold in the retailers’ world. It also improves SEO for e-commerce.
Image search is a new trend, and the right time to implement image tagging is now. Keeping up with the latest e-commerce trends is how to keep your business stable. Today, AI auto-tagging is trending. Tomorrow will hold new technologies for the bravest of the bunch. One thing is certain: those who managed to keep up with technology are leading in every industry, including retail!