2 Truths And A Lie: The AI eCommerce Product Tagging Edition

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In today’s economy, businesses are constantly looking for ways to cut costs and increase efficiency. eCommerce retailers are no exception, as they strive to manage their operations more effectively while maintaining a competitive edge. This is where AI can really help make a difference.

Let’s play a quick game of ‘2 truths and 1 lie’, shall we?

Out of the following 3 statements, 2 statements are correct while one of them is a lie. 

Statement 1 – AI-based product tagging is a lot more efficient and standardized as compared to manual tagging

Statement 2 – AI can help save a lot of time and cost that goes into product tagging.

Statement 3 – AI replaces human involvement completely. 

So, which statement do you think is the lie? 

If you guessed statement 3, you are right.

Why? Because humans can still be in the loop for QA and adding more robust feedback to the AI systems.

For eCommerce product tagging, AI can generate tags on its own and these tags will be fairly detailed and accurate. But where the AI is not too sure about the tags it has generated, it will assign a poor confidence score there. It can be helpful to have humans checking those and giving feedback to the system so the AI can learn and tag other products better.

So instead of people manually sitting and tagging every product, all they have to do now is review the tags generated by AI and approve them or give feedback.

Let’s delve a little deeper into the 2 true statements:  

Statement 1 – AI-based product tagging is a lot more efficient and standardized as compared to manual tagging.

This statement is true because the Computer Vision and Natural Language Processing (NLP) capabilities of AI allow it to extract data from both images and text. The AI is able to predict tags from the images using computer vision and extract product data from unstructured text with NLP. It has the ability to do this much faster and in bulk, producing a consistent output that helps improve the overall quality of product data available. 

Statement 2 – AI can help save a lot of time and cost that goes into product tagging.

This statement is true because even today, product tagging is largely a manual process involving teams of people. The problem with manual tagging is the inherent differences in how people term certain attributes, any errors/discrepancies that might come up, and the amount of time it takes to tag these products. The costs incurred from employing large teams end up including direct and lost opportunity costs as well as any delays/errors that result in a slower go-to-market process. In today’s economy, time is money and such delays affect the company’s bottom line directly.  

Alternatively, AI is an efficient and effective resource that can aid these teams by taking up the grunt of the work thereby freeing their time to work on other projects. This help businesses automate the tagging processes and avoid the costs listed above.

How does AI product tagging work?

AI-powered product tagging is revolutionizing the retail industry, enabling eCommerce retailers to be more efficient. Vue.ai’s automated product tagging solution is leading the charge in helping retailers build high-quality product data that is effective for any downstream use cases including better shopping experiences with personalization, improved search, better navigation, etc. 

Let’s understand how it does this – from input to output and the processes involved.

The Input

The AI ingests two levels of input: the first level consists of metadata and images provided by the retailer, typically in a CSV file format, and the second level includes other information that can be obtained from external market sources and databases.

The Process

The tool processes the two levels of input data in different ways. The AI extracts the input it receives from the retailer, which includes metadata and images in a CSV file, using a combination of Image Recognition and OCR for images, and Natural Language Processing (NLP) for text and metadata.

Additionally, it obtains the second level of input data by scraping external sources such as market databases and performing EAN lookups.

The Output

After the tool processes and extracts the information from both levels of input data, it puts them together, analyzes it, and then uses this information to create more comprehensive tags, titles, and descriptions that are relevant to every individual product.

Impact of AI product tagging

Time Saved

The amount of time saved is one of the most significant advantages of AI-powered product tagging. Traditional manual tagging is time-consuming and labor-intensive. By automating the product tagging process, AI can save retailers a significant amount of time and resources.

With faster and more accurate tagging, online retailers can reduce the time-to-market for their products, making them available to customers faster and potentially improving their sales.

Increased scalability

Scaling the creation of product tags can be challenging when dealing with a vast inventory, like in the cases of marketplaces. The situation only becomes more complex when retailers rely on manual tagging, leading to duplicated tags and murky insight into inventory.

On the other hand, AI-powered product tagging enables online retailers to scale the process of creating product tags effortlessly, even with a vast inventory. The AI is able to tag 1000s of images in a matter of minutes. This allows retailers to spend more time on improving customer experiences, knowing that their product data is standardized, detailed, and prepped to improve online visibility.

Learn how ​​Diesel uses Vue.ai’s AI-Powered Automated Product Tagging solution to enhance product data and improve discovery on-site

Long story short, AI-powered product tagging allows retailers to enrich their product data in a fast, accurate, and cost-effective manner, revolutionizing the way they manage their business. 

Click here to learn more about how Vue.ai’s product tagging solution can benefit you

Read more related articles here:

Creating and Enhancing E-Commerce Product Data with A.I.

Managing Product Data: The Need For Product Information Management tools

Metadata: How Discoverable Is Your Product