Elevate your e-commerce performance with MagnetLABS comprehensive product catalog scoring framework. We track over 35 metrics across nine fundamental performance dimensions to evaluate you catalog's effectivement in supporting generative search optimization (GEO), search engine optimizaton (SEO), and internal site search and product discovery.
Our scoring framework empowers you to boost rankings, elevate customer engagement, and improve conversion rates. By optimizing your product data, you gain a competitive edge, simplifying the path for customers to find and purchase your products, resulting in increased sales and stronger customer loyalty.
Coverage
Ensures the core product content fields exist and have content.
Accuracy
Ensures the data accurately represents the product attributes and specifications.
Completeness
Measures whether all required attributes and information are present.
Consistency
Checks if the product data is uniform across fields and adheres to predefined rules and formats.
Relevance
Evaluates how well product data aligns with user intent to ensure meaningful results across search platforms.
Natural Language
Assesses how well product content matches conversational, question-based queries used in AI-powered search.
Structured Data
Evaluates the presence and correctness of schema markup and other structured data formats.
Content Quality
Evaluates how effectively descriptions and related content provide valuable, clear, and engaging information.
Mobile Optimized
Quantifies how well product titles and descriptions are optimized for mobile devices.
"Poor quality product/catalog data is one of the topmost factors behind low search-engine rankings and product discoverability."
"Poor product data can lead to unmet expectations, where the product received does not match what was advertised. This discrepancy can result in returns, negative reviews, and a reluctance to make future purchases from the same brand or retailer."
"Poor-quality data can lead to irrelevant or incorrect recommendations, diminishing the effectiveness of recommendation engines."