AI Catalog Tagging: Optimizing Product Information for SEO

Customer preferences have substantially changed over time, especially during and after the pandemic. Customers now want a wider range of products and use multiple channels to find their favorite clothes or products, such as voice or visual searches. 

To meet the ever-increasing needs, online retailers are releasing new products regularly and using innovative solutions such as AI catalog tagging to boost productivity. Using AI catalog tagging, companies can automate product tagging and make it quicker and more accurate. The best part is that this helps make the product and the website more discoverable, which translates into more conversion and, thus, revenue.  

In this blog, we’ll dive deeper into AI catalog tagging, the technology behind it, the benefits it brings, and how it boosts the SEO game for retailers. 

Streamlining Product Catalog Management with AI

Traditional product tagging involves manual elements. Either the e-commerce manager or employees spend weeks or even months manually tagging products to images in the catalog. This process not only takes a lot of time but is prone to errors. 

Moreover, this leads to poor catalog management, causing multiple problems, including: 

• Entangled supply chains. 
• Inaccurate search results leading to poor customer experience. 
• Lengthy digitization process for new products. 

However, businesses can deal with this through AI automation. Using AI algorithms, companies can automatically tag all their products based on different attributes. This way, businesses can reduce the error margin, save time, and enhance the shopping experience for their customers. 

AI and Machine Learning: Powering Automated Catalog Tagging 

Complex AI and machine learning algorithms and techniques are behind making automated catalog tagging possible: 

• Image Recognition: Al algorithms use image recognition to analyze product images and extract important attributes or data. This helps businesses automatically tag items based on visual features, including colors, shapes, patterns, etc.
• Natural Language Processing (NLP): NLP helps AI understand text-based information related to fashion products, such as social media comments, reviews, product descriptions, etc. Using this information AI can tag products more accurately based on their preferences and attributes.  
• ML Models: ML models are trained using large data sets of already labeled products. This helps the algorithms identify patterns and relations between attributes, leading to more accurate results. 

Continuous Learning and Improvement: AI-based catalog tagging systems learn and improve continuously by adapting to new trends, customer preferences, and styles. This way, with time, systems become more accurate.

Benefits of AI-Powered Catalog Tagging 

Here’s how automated catalog tagging benefits businesses:  

• Reduced Reliance on Manual Elements: Using AI-powered catalog tagging, businesses can automate the entire process, making it more accurate and faster. Plus, employees can save time and work on more non-redundant tasks.
• Enhanced Catalog Management: Businesses can keep track of all the SKUs without fail and tag products automatically, making catalog management more convenient. 
• Valuable Data: Businesses can leverage descriptive tags to learn more about the customers’ intent and improve their offerings.  
• Revenue Boost: Customers can easily find the products when everything is tagged perfectly. This will help improve the customer experience and generate more revenue.

SEO Optimization and Enhanced User Experience 

Sometimes due to human error, products are incorrectly tagged using the wrong keywords. Because of this, when shoppers search for products, the expected products don’t show up leading to a loss of potential revenue. 

On the other hand, by using AI catalog tagging, businesses can ensure products are accurately tagged and the right keywords are used. This way, when shoppers search for products, Google will know which website to push to the top, improving the SEO ranking. And when websites rank higher, the chances of making sales also increase. 

Additionally, when shoppers can easily find the product they want to buy, their overall shopping experience will improve. And this will further help improve sales.

Implementing Effective AI Catalog Tagging Strategies 

Here are some actionable steps businesses can follow for effective AI catalog tagging: 

• Define Tagging Goals and Attributes 
• Data Collection and Preparation 
• AI Training and Model Customization 
• Implement AI-Generated Tags 
• User-Centric Optimization 
• Schema Markup for Structured Data 
• Ongoing Monitoring and Refinement 
• Performance Analysis and Iteration 

Following the above steps is a cumbersome process. Moreover, businesses might need dedicated experts to craft an AI-based tagging solution, which is costly and time-consuming. But here’s a better idea: find a reliable AI catalog tagging solution. 

Businesses can start tagging their products instantly by integrating an AI catalog tagging solution such as ViSenze. ViSenze allows you to enhance and enrich your product listing with hundreds of attributes, improving discoverability and making your catalog more shoppable.   

Moreover, as AI works in the backend, you can drastically reduce operational costs, reduce human error and populate your catalog seamlessly.

Voice Search and Visual Search Optimization 

AI-tagged catalogs can enhance both visual and voice search results. Here’s how: 

• Voice Search: Using Natural Language queries, AI can accurately analyze and understand voice search queries. Whenever someone uses voice search, AI deciphers the intent, context, and keywords and delivers accurate results. Let’s say someone searches for “a red dress for a baby shower.” In this case, AI will identify attributes like occasion, style, color, and more and bring the right product in front.  

• Visual Search: Users can upload pictures of clothes for visual search. Once the user searches, AI uses computer vision (AI search) and image recognition to identify the style, brand, object, and other attributes. AI will then find products in the catalog and present them to the user. However, this will only happen if the products are tagged using the correct keywords, which again is made possible by AI.

Future Trends and Developments in AI Catalog Tagging

As technology advances further, applications of AI in general and AI catalog tagging in specific will only increase. It’s believed in the years to come, AI technology will become more accessible, enabling small businesses to leverage it to boost productivity and SEO. 

Moreover, NLP and machine learning capabilities are expected to advance further. This will make AI systems even more efficient in interpreting human language and enhancing their tagging capabilities. 

Case Studies: Successful SEO Outcomes with AI Catalog Tagging 

There are multiple examples of companies using AI catalog tagging or AI in general.  

Take VVIC for example. VVIC is a leading B2B platform in China that connects fashion wholesalers, retailers, and suppliers. However, VVIC was facing issues related to user experience, such as suppliers posting low-quality images with watermarks lacking crucial information.

VVIC then opted for ViSenze’s automated catalog tagging solution to detect poor-quality images after evaluating image search technology and computer vision. Thanks to the precise solution that helped VVIC achieve a massive 97% accuracy in image quality control.

Conclusion 

AI catalog tagging can revolutionize the way businesses manage their products. Using AI catalog tagging solutions, businesses can become more accurate, save time, and, most importantly, improve their SEO rankings, leading to user engagement and, thus, more revenue.  If you’re a business that relies on traditional catalog tagging, it’s time to find a reliable AI tagging solution and step up the SEO game. Get in touch with us to learn more about how AI-powered Visual Search can boost engagement and conversion rates!