Product Tree Tagging With Visual AI

How to Create a Product Tree with Visual AI

Tagging items and catalogs in e-commerce stores is an essential task for ensuring that products are easily searchable and discoverable by customers. However, it can be a time-consuming and labor-intensive process, especially for large stores with thousands of products.

Manual Tagging vs Visual AI Tagging

Problems with manual tagging

• Manual tagging can be a very time-consuming process, especially for large stores with thousands of products.
• Manual tagging is prone to errors, especially when it is done quickly or by inexperienced workers.
• Manual tagging can be inconsistent, as taggers may use different criteria to tag products.
• Manual tagging is difficult to scale, as it is difficult to find and retain enough taggers to keep up with the demands of a large e-commerce store.

Benefits of visual AI tagging

Visual AI can help to automate and streamline the product tagging process, making it faster and more efficient. Visual AI algorithms can be trained to identify and classify products in images with high accuracy, even in complex and cluttered backgrounds. This can help to eliminate the need for manual tagging, which can save e-commerce stores a significant amount of time and money.

Integrating this data into the supply chain and inventory management systems empowers the retailer to make more strategic, accurate decisions on which products and styles to offer and how much inventory to maintain.

Compared to standard inventory management systems, visual AI provides a much more nuanced view of what types of products, styles, and trends are gaining traction with shoppers. This empowers the retailer to boost profits by stocking the inventory with items that data indicates will sell most.

Here are some of the key benefits of using visual AI for product tagging:

• Speed: Visual AI can tag products much faster than humans, tagging hundreds or even thousands of products in record time.
• Accuracy: Visual AI is more accurate than humans at tagging products, as it can identify and classify products accurately, even in complex and cluttered backgrounds.
• Consistency: Visual AI tagging is more consistent than manual tagging, as it uses the same algorithms to tag all products.
• Scalability: Visual AI tagging is scalable for e-commerce stores of all sizes.

How Selectika tags products and creates the most precise product tree

Selectika’s visual AI system utilizes computer vision deep learning to automatically abstract product descriptions, and tag each item with more than 20 data points. It tags complete catalogs in record time. The benefits of the system cross into all the aspects of the store and brand. 

• Saves time and creates consistent and precise data. 
• The recommendation engine becomes increasingly accurate with superior results across the board. 
• The system provides real-time insights and behavioral data to empower retailers 
• Selectika makes internet cookies obsolete.

Overall, visual AI has the potential to revolutionize the way that products are tagged and cataloged in e-commerce stores. By automating the process and making it more efficient, visual AI can help e-commerce stores to save time and money, improve the shopping experience for customers, and increase sales.