Automated ecommerce product tagging

Tired of manual product tagging and endless vendor coordination? Our AI-powered tagging solution automates the entire process, making product data management effortless and accurate.

With advanced NLP, OCR, and image recognition technology, our system extracts, enriches, and organizes your product data at scale. Whether you're dealing with structured or unstructured data, our solution ensures comprehensive product attribution across fashion, grocery, electronics, home and furniture, and beauty categories.

Transform your product data with AI

• Save time and resources: Reduce manual tagging effort by 85%, saving up to 27 hours per person per week.
• Enhance product discovery: Boost conversion rates by 40% with accurate, comprehensive product attributes.
• Accelerate time-to-market: Launch products 30% faster with automated data extraction and enrichment.
• Improve search accuracy: Leverage industry-specific taxonomies with thousands of pre-set tags for precise product findability.

AI-powered features:

• Intelligent data extraction Our system processes both images and text to capture complete product information, using advanced AI to predict missing attributes.
• Market-aware tagging Automatically enriches product data using EAN databases and market sources, ensuring comprehensive attribution.
• Customizable taxonomy Adapt the system to your specific business needs with custom tags and flexible integration options.
• Continuous learning Our AI engine evolves with your business, continuously improving accuracy through QA feedback.

What is automated product tagging?

Automated product tagging is an AI workflow for ecommerce stores where an AI or machine learning system automatically assigns descriptive tags or labels to product description content in an online ecommerce catalog description. These tags categorize products based on attributes like color, size, material, style, and function, making it easier for shoppers to find items via search or filters.

Automated tagging relies on computer vision and natural language processing (NLP). Here’s a breakdown of how it typically works:

1. Image recognition: AI analyzes product images to identify visual characteristics. For example, a system might recognize a "blue cotton shirt" by detecting the color and material.
2. Text analysis: NLP tools scan product descriptions or titles to extract keywords and important attributes that help classify the product.
3. Tag assignment: Based on its analysis, the system generates tags and assigns them to each product. Tags might include categories (e.g., "casual shoes," "office wear"), specific features (e.g., "waterproof," "vegan"), and other relevant details.
4. Accuracy and training: Over time, the system improves as it's trained with new data and fine-tuned based on user feedback, making tagging more accurate and customized to a brand's catalog.

Automated product tagging saves time by reducing the need for manual tagging, which can be especially helpful for large inventories. It also boosts discoverability and enhances search and filter functionality on ecommerce sites, improving the user experience and potentially increasing conversions.

Why is automated product tagging important for ecommerce stores?

Automated product tagging is crucial for ecommerce stores because it enhances searchability, personalization, and inventory management, leading to a better shopping experience and higher conversions. Here’s why it matters:

Improves product discoverability

Tags make it easier for shoppers to find products through search filters and recommendations. For example, if a customer searches for "black leather boots," automated tags ensure those boots appear in the search results without manual input. This helps potential buyers quickly find exactly what they're looking for, reducing bounce rates and increasing the chances of a sale.

Enhances site navigation and filtering

With accurate tags, customers can filter by categories like color, size, material, or style, creating a smoother and more intuitive browsing experience. Effective tagging enables more precise filters, making it easier for shoppers to locate products that match their preferences and needs.

Supports personalization and recommendations

Automated tags provide data for personalization engines, allowing stores to make tailored recommendations. By tagging products with specific attributes, AI can suggest items based on a user’s browsing and purchase history, leading to higher engagement and conversion rates.

Saves time and reduces manual work

Manually tagging hundreds or thousands of products is time-consuming and prone to errors. Automated product tagging speeds up the process, freeing up teams to focus on other tasks while ensuring consistency and accuracy in product categorization.

Enables better inventory management

Tags provide insights into popular product attributes, which can inform purchasing and stocking decisions. For example, if certain tags (like "sustainable" or "eco-friendly") see high demand, the store can adjust inventory to meet that demand, improving overall sales and customer satisfaction.

Boosts SEO and search engine visibility

Well-tagged products improve on-site SEO by allowing search engines to understand the product catalog more effectively. This can lead to higher rankings in search results, driving more organic traffic to the store.

Improves scalability for large inventories

As ecommerce businesses grow, the number of products can become overwhelming to tag manually. Automated tagging enables seamless scaling, ensuring that even large inventories are consistently categorized and searchable.

Automated product tagging empowers ecommerce stores to create a more efficient, enjoyable, and customer-centered shopping experience. By streamlining product discovery and supporting personalized shopping, automated tagging can lead to higher conversions, increased loyalty, and improved operational efficiency.

How to use Strategically AI to supercharge your ecommerce product tags

To optimize product tags using Strategically AI, you can leverage its product attribute extraction and automated product tagging features. Here’s how it works:

1. Extract product attributes with AI: Strategically AI scans each product, identifying specific details that customers might search for, like color, material, dimensions, and functionality. For example, for a plant, it may extract attributes like planting month, sunlight requirements, and soil type. For a fashion item, it could recognize lace type, style, and size options.
2. Convert attributes into tags: Once the attributes are extracted, they’re converted into relevant tags. These tags make it easier for customers to find products using search and filters, both on your site and in search engines. They also enrich the data Google uses for your product feed, improving visibility.
3. Improve search and collection pages: Tags allow products to automatically appear on relevant collection pages and show up more accurately in search results. For example, tags like “anti-chafing” or “special occasion” help narrow down searches for specific product qualities, enhancing user experience.
4. Enhance SEO and drive traffic: By aligning tags with popular search keywords, Strategically AI ensures that product pages are optimized for both on-site search and Google’s algorithm, helping increase organic reach and conversion rates.

With Strategically AI, you can ensure your tags accurately reflect customer language, making it easier for users to find products and boosting both discoverability and SEO performance across your store.

Frequently asked questions

What is product tagging?

Product tagging is the process of assigning descriptive labels (or "tags") to products. These tags are keywords or attributes that help categorize and identify the product's features, like color, material, size, or use case. In ecommerce, product tags make it easier for customers to find items through search and filters, enhancing product discoverability and the overall shopping experience.

What is AI auto tagging?

AI auto tagging is the use of artificial intelligence to automatically assign tags to products, content, or media based on their attributes. AI algorithms analyze visual, textual, or metadata aspects of items to generate accurate tags, reducing the need for manual tagging. For eCommerce, AI auto tagging streamlines product categorization and helps create a more efficient and consistent tagging system, enabling faster scaling and improved search functionality.

What is automatic tagging in NLP?

Automatic tagging in NLP (Natural Language Processing) is the process of using algorithms to analyze text and assign tags based on detected patterns, keywords, or entities. In eCommerce, NLP-based tagging systems can extract product features or customer intent from text descriptions, helping categorize products, improve search results, and enhance recommendation systems.

What does product tag mean on a marketplace?

On a marketplace, a product tag is a keyword or label assigned to a product to categorize it and improve searchability within the platform. Tags can include product attributes like “organic,” “vintage,” or “handmade,” and are typically used to help buyers find relevant items by narrowing search results based on specific qualities or themes. Tags make it easier for customers to discover products that align with their preferences and needs.