Automate product categorization with full accuracy
Classify every item into the right category using machine learning that understands titles, attributes, and images
What is product categorization and how does it work?
Product categorization uses machine learning to assign the right category to each item in your catalog. It analyzes titles, descriptions, specs, and even images to understand the product’s purpose and match it to your taxonomy, no changes to live listings required.
Product Categorization Results
Average category-assignment precision
Faster time-to-site for newly onboarded SKUs
Broken filter incidents reported after re-indexing
Why product categorization powers better catalog performance
Proper classification is the backbone of ecommerce navigation. Automated categorization ensures every product sits in the right place, improving search, filtering, product discovery, and customer satisfaction at scale.
Precision search visibility
Correct categories place every item in the exact SERP and on-site search lane
Unbreakable navigation paths
Consistent taxonomy eliminates dead ends and keeps shoppers flowing through filters
Reduced return rates
Customers buy the right item first time thanks to clear category context
Unified marketplace mapping
One classification pass aligns Amazon, Google, and long-tail vertical schemas
Real-time integrity alerts
Ongoing monitoring flags mis-sorted or orphaned SKUs before they impact UX
Growth-ready taxonomy scaling
Handles millions of SKUs and evolving category trees without re-engineering
Ready to eliminate wrong categories and broken filters?
Request a free category mapping sample and see how your products should be sorted
Ecommerce automations by use cases
Product categorization uses machine learning to assign the right category to each item in your catalog. It analyzes titles, descriptions, specs, and even images to understand the product’s purpose and match it to your taxonomy, no changes to live listings required.
Product Categorization
How do you handle “borderline” products that could fit more than one category?
We run a two-stage decision workflow. First, the core classifier assigns up to three probable categories, each with a probability score. If the top-two scores fall within a narrow delta (typically 5 percentage points), we trigger a disambiguation routine that weighs secondary attributes, use-case keywords, material, compatible devices, even customer-review phrases, to see which category delivers the best facet coverage and search-intent match. When scores remain razor-close, you can set a tie-breaker: push to the more specific leaf, default to the parent, or route the SKU into a “Needs Review” queue where merchandisers make the final call. This ensures dress-shoes don’t leak into “Sneakers” or 2-in-1 laptops end up in “Tablets” unless that’s your deliberate strategy.
Can we inject our own business rules or seasonal categories into the model?
Yes. Alongside the main taxonomy, you can provide rule layers, e.g., “If Brand = X and Collection = Summer, route to ‘Beachwear’ even if the description contains ‘loungewear.’” Rules are compiled into a real-time evaluator that runs before the statistical model, guaranteeing hard requirements always win. When you add a temporary “Black Friday Deals” branch or retire “Home Office” after a season, you simply edit the JSON rule sheet; the next incremental run re-categorises affected SKUs without a full re-train, keeping maintenance overhead low.
What role do product images play, and what if some SKUs lack photos?
Vision models provide crucial tie-breakers for visually distinctive items, think footwear heel shape or tool connector type. If an image is available, we extract feature vectors and blend them with text-based signals, lifting overall precision by 2-4 points on average. When images are missing or low-resolution, the system re-weights text and attribute evidence so accuracy remains stable. SKUs flagged “image-poor” are listed in the post-run report so you can prioritise photography where it matters most.
How does the solution integrate with our PIM/ERP and keep data in sync?
A lightweight connector polls your PIM (Akeneo, Salsify, Pimcore, SAP CX, etc.) for new or modified SKUs, runs them through the categorisation API, and writes back the category ID plus a confidence score. Updates can be batched nightly, run on-demand, or triggered by webhooks the moment a buyer adds a SKU. Each record carries a “categorised_at” timestamp, so downstream systems can skip re-processing unchanged items, preserving API quotas and compute credits.
What safeguards stop misclassifications from slipping through after launch?
A live-monitor module listens to click-stream and search-log signals: if users frequently refine out of a category, bounce quickly, or apply contradictory filters (e.g., selecting “Men’s Jackets” after landing on “Women’s Coats”), the SKU is flagged for re-evaluation. Weekly drift reports cluster these anomalies so you can fix dozens at once rather than chasing single errors. You can also set automated rollback rules, any item with user-behaviour outliers reverts to its previous category until the model’s next tune-up.
How do we measure the ROI of automated categorisation?
The post-implementation dashboard tracks three KPIs: reduction in manual tagging hours (captured via time-sheet or project-management logs), improvement in in-category search conversion (baseline vs. post-go-live), and decrease in customer-service tickets tied to “item not found” or “filter broken.” We translate those gains into cost savings and incremental revenue, then compare them against your subscription and compute fees. Typical clients recoup deployment costs in 6–10 weeks and free analysts to focus on strategic merchandising instead of fire-fighting taxonomy errors.
Product categorization
Pricing is based on the volume and complexity of your operations. Get a personalized quote tailored to your product catalog size, automation needs, and platform requirements