Say, a shopper searches for “ankle-height distressed jeans” and ends up scrolling through irrelevant options—plain jeans, capris, or even denim jackets. It’s a frustrating experience, and it’s alarmingly common. Studies reveal that over 30% of ecommerce searches fail to deliver relevant results. Why? Because most product catalogs lack the depth and detail to match specific search intents. Missing or vague product attributes mean that even the most precise queries often lead nowhere. And here’s the kicker: Shoppers with such specific, long-tail queries tend to have the highest purchase intent.

So, how do we fix this? The answer lies in catalog enrichment and attribute extraction—bringing granular, meaningful product data into the catalog to ensure precise searches connect seamlessly with the right items.

The Shift from Traditional to AI-Powered Enrichment

Traditionally, catalog enrichment relied on manual processes or rule-based methods, where human curators tagged products based on visible attributes. While effective in some cases, this method was slow, prone to errors, and couldn’t scale with large inventories or complex product lines. Missing details led to poor search relevance, high exit rates, and zero-result pages that frustrated shoppers.

Today, AI-powered enrichment transforms this process by extracting meaningful, detailed attributes from product images and text, aligning them with search functionality at scale. Netcore Unbxd's AI-driven approach ensures speed, consistency, and accuracy. Here’s how it works:

1. Identifying Missing Fields & Catalog Rating: AI scans the catalog to detect incomplete data and assigns a quality rating to measure how well the catalog is structured.
2. Exhaustive Category-to-Feature Field Mappings: AI automatically maps relevant attributes to each product category, ensuring consistency and completeness.
3. Using Intelligent, Trained Data to Enrich Missing Fields: Machine learning predicts and fills in missing attributes, improving search accuracy.
4. Reviewing the Catalog and Starting Indexing: The enriched catalog undergoes validation before being indexed, ensuring search engines can interpret and retrieve the right products.

For example, instead of simply tagging a product as "jeans," enrichment ensures the catalog reflects nuances such as "mid-rise distressed skinny jeans with ankle cut."

How AI Knows Your Product Features

We’ve developed a robust, two-step modeenrichmentibute extraction that balances accuracy with scalability.

1. Object Detection

The first step is identifying the object of interest within an image. For example, in a photo featuring both a dress and boots, the model isolates these items as separate entities. Using pre-trained deep learning models customized for specific verticals—like apparel, furniture, or jewelry—we achieve high accuracy in object segmentation.

2. Attribute Prediction

Once the object is identified, a second model analyzes it to predict its attributes. These attributes are divided into:

• Generic Attributes: Common across products, such as color, material, or size.
• Specific Attributes: Tailored to the product type, such as sleeve type for T-shirts, distress style for jeans, or closure type for jackets.

The outputs are validated and integrated into the catalog, often enhanced through a knowledge graph to map synonyms and related terms.

Why Enrichment Matters for Search

Traditional search engines, without enriched attributes, struggle to deliver relevance. A user searching for "suede fringe ankle boots" might receive an overwhelming mix of results—ranging from generic boots to irrelevant items like high heels. Worse still, many searches return zero results, increasing exit rates and reducing conversions.

Industry studies show that over 30% of ecommerce searches fail to meet user expectations due to vague or incomplete catalog data. This inefficiency isn’t just frustrating; it’s costly. Enriched attributes fundamentally transform search experiences by addressing this gap. Here’s how:

1. Improved Search Relevance Traditional catalogs often lack granular details. With enrichment, customers searching for “peplum hemline” or “suede ankle boots” are more likely to find relevant results. This precision enhances both customer satisfaction and conversion rates.

2. Enhanced Recall for Tail Queries Not all users search with popular terms. Tail queries like “fringe blazer” or “drawstring pajamas” often go unanswered due to missing data. By enriching catalogs, these niche searches can retrieve the right products, addressing a critical gap.

3. Faceted Navigation and Filtering Enrichment allows for more dynamic filtering options on ecommerce platforms. Customers can now filter by unique attributes like “heel type,” “hemline,” or “closure type,” improving browsing experiences.

4. Better Personalization and Recommendations Rich attributes feed into recommendation engines, enabling hyper-personalized suggestions. For example, a user browsing “ankle-length distressed jeans” might receive recommendations for similar styles with different fits or washes.

The Future of Search with Enrichment

As ecommerce grows increasingly competitive, attribute and catalog enrichment isn’t just a nice-to-have—it’s a necessity. Without enriched attributes, search engines struggle—over 30% of searches result in zero results or irrelevant listings, leading to increased exit rates. AI-driven enrichment ensures that platforms can interpret and surface the right products, reducing frustration and improving conversions. It empowers businesses to:

• Stand out with highly relevant search experiences.
• Reduce catalog inaccuracies and improve inventory alignment.
• Cater to niche searches that drive long-tail conversions.

Incorporating enriched attributes into search systems isn’t just about staying competitive. It’s about creating a future where every search feels intuitive, personal, and satisfying—where users don’t have to settle for "close enough," but find exactly what they’re looking for.

If you’re looking to harness the power of attribute and catalog enrichment, now is the time. Let’s build smarter, sharper search experiences—together.