The Classifier processes every product in your catalog and works out what it actually is. It reads your product data, analyses images, and maps each item to the right place in the Knowledge Graph, giving Elevate the structured information it needs to match products accurately against what your customers search for.
How it works
Reading your product feed
The first thing the Classifier does is read the structured fields in your product feed. If you've sent a brand name in the brand field, Elevate trusts that value and uses it directly. Attributes you provide explicitly are treated as reliable and given priority over anything the Classifier might infer from other sources.
Analysing product images
If you provide images, the Classifier identifies which image best represents the product's colors, then runs a pixel-level analysis to determine the primary and secondary colors. This works well for clean, front-facing images against a neutral background. Products with complex lighting, many shades, or patterned backgrounds can produce less accurate results. If the automatic color detection doesn't look right for a product, you can review and manually override it in Elevate.
Extracting attributes from text
Not every attribute needs to be in your feed for the Classifier to find it. Using advanced language models, it reads your product titles and descriptions to infer things like material, style, or fit, even when those aren't sent as separate fields. A detailed, well-written product description gives the Classifier more to work with and generally produces better results.
Assigning concept precision
Once it has gathered everything it can, the Classifier maps the product to concepts in the Knowledge Graph and assigns a quality score to each match. A product correctly identified as a "tank top" gets a stronger classification than one only identified as "clothing." That precision directly affects how well the product surfaces in relevant searches as more precise matches rank more strongly.
Because the Classifier is data-driven, the quality of what comes out is closely tied to the quality of what goes in. Incorrect or inconsistent product data is the most common cause of misclassification.
How to improve classification accuracy
Send clean, consistent data
Send brand values in a dedicated brand field, and make sure they're accurate and consistent across your catalog. If brand names are embedded in descriptions or vary in spelling, the Classifier is more likely to miss or misread them.
Write clear, focused product titles and descriptions. Descriptions that reference other products such as "pairs perfectly with our linen shorts", can confuse the Classifier, which may pick up the wrong product type from those references. Describe the product itself, not what it goes with.
Use good product images
Use high-quality, front-facing images with a neutral background. The cleaner the image, the more reliable the color analysis. Flat product shots with even lighting give the best results.
Investigate the feed first if something looks wrong
If products are appearing in unexpected search results, reviewing your product feed is the right starting point. Inconsistent data such as mismatched brand names, descriptions that mix product types, or low-quality images, is usually the cause. If you believe a product type or classification model itself needs adjustment, contact your Customer Manager. Classification models are continuously improved, and specific feedback from customers helps drive that process.
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