Have you ever gone to your favorite supplier or retailer, tried to filter by product specs, and still couldn’t find what you were looking for - even though you knew exactly what you wanted? You’re not alone.
Even today, it’s a common frustration - especially in B2B ecommerce. While major retailers have invested heavily in advanced site search and structured product data, many distributors are still playing catch-up. For them, digital transformation hasn’t just been about going online - it’s been about wrangling decades of supplier data chaos into something buyers can actually search, filter, and trust.
And the data backs this up.
Source: Ventana Research, "Building High-Quality Complete Product Information"
A study by Ventana Research found that:
It’s no wonder that only 16% of organizations fully trust their product information processes - and you get a poor customer experience.
For B2B distributors, this disconnect shows up everywhere - from broken filters and duplicate SKUs to inconsistent category structures that make good products invisible. In the sections that follow, we’ll look at three of the biggest underlying causes of poor search performance - category mapping, attribute normalization, and duplicate detection - and how modern AI-driven tools like CatalogIQ are solving them at scale.
Most distributors know the pain of trying to merge supplier data into their own catalog structure. Every new feed arrives in a slightly different shape, with categories that don’t match your taxonomy. One supplier calls it Fasteners, another Screws & Bolts, another Hardware Components. Multiply that inconsistency across hundreds of vendors and you end up with a catalog full of overlap, confusion, and products that buyers can’t easily find.
Category mapping is more than a clerical task - it’s a search problem. When supplier categories don’t align to your internal taxonomy, your site search and filters can’t work properly. Products may appear under multiple headings, disappear from expected ones, or simply fail to surface in results at all. Instead of a clean, logical experience, buyers get friction and frustration.
One large industrial distributor we reviewed faced this challenge across more than 50 main categories and several hundred subcategories. Mapping supplier data manually had become both time-consuming and error-prone. New products often didn’t fit neatly into existing categories - forcing teams either to spend hours remapping or to create redundant new ones. Both options slowed onboarding and cluttered the taxonomy over time.
AI is now changing that process entirely. Instead of relying on manual tagging, AI can analyze product titles, descriptions, and attributes to recommend the correct category within your existing taxonomy - or flag when a new one might be needed. For example, when new products are added that share similar materials, dimensions, or use cases with an existing category, the AI automatically maps them to the right section of your catalog - no manual tagging required.
CatalogIQ takes this one step further, learning from each correction or approval. Over time, it becomes a self-improving model tuned to your specific catalog logic - reducing manual work, speeding up onboarding, and ensuring that every new product lands exactly where it belongs.
Once products are mapped to the right categories, the next challenge begins: cleaning up the attributes that make search and filtering work. For distributors managing supplier data, this is where things often go sideways.
Two vendors might sell the exact same item - but one calls the color Navy, another Dark Blue, and another Midnight Sky. To a human, those may be similar. To a search engine, they’re completely different. Multiply that inconsistency across every product attribute - color, size, material, unit of measure - and suddenly your filters explode into hundreds of redundant options that confuse buyers and break search relevance.
These problems persist because most distributors still depend on manual cleanup or legacy tools that treat supplier input as truth. According to Ventana Research, over half of organizations struggle with incompatible data quality tools, and more than 75% still rely on spreadsheets for managing product information - a recipe for inconsistency and error.
In one industrial dataset we analyzed, a single attribute field allowed multiple comma-separated values such as “A,” “B,” “A,B,” and “A,B,C.” Instead of producing distinct search filters, the system generated combined ones - turning what should have been three simple options into a messy tangle of duplicates. The result? Poor filtering, inconsistent results, and frustrated customers who can’t refine their search efficiently.
CatalogIQ solves this use case by flagging when values appear combined and parsing them into proper discrete attributes for filtering.
AI-powered normalization changes that. By analyzing attribute patterns across supplier feeds, AI can detect synonyms, harmonize values, and even convert units automatically. “Dark Blue,” “Navy,” and “Midnight Sky” can be unified into a single standardized value like Navy Blue, while “12 mm” becomes “0.47 in.”
CatalogIQ adds a layer of governance on top - providing validation rules, completeness checks, and approval workflows so every change remains transparent and reversible. The result is a cleaner, more consistent catalog where attributes behave as they should, and buyers finally get the simple, accurate filtering experience they expect.
Even with clean categories and normalized attributes, another silent killer remains: duplicates. In distributor catalogs, the same product often arrives from multiple suppliers - sometimes with different SKUs, slightly different titles, or conflicting attribute sets. Left unchecked, duplicates split clicks and reviews, confuse buyers, bloat search results, and create downstream issues in pricing, inventory, and reporting.
Why do duplicates happen so often? Because identifiers aren’t consistent. One feed includes a GTIN/UPC, another uses an internal part number, and a third supplies only a model reference. Add in minor copy differences (“3/8” vs “0.375 in”), variant collisions (color/size packs), and kit or bundle overlaps, and you get multiple records that are functionally the same product - treated as different SKUs. When that happens, the impact ripples across the entire buyer experience: search results become noisy and repetitive, competing product pages dilute SEO performance, pricing variations erode trust, and inventory and analytics data drift out of sync.
Solving duplication reliably requires entity resolution - a process that determines when two or more records actually represent the same real-world product, then merges them into a single, trusted record (often called the “golden record”). This isn’t just string matching; it blends identifiers and context to reach a confident decision.
CatalogIQ applies this approach through AI-powered matching logic that compares normalized titles, attributes, and identifiers to detect near-duplicates with high confidence. True duplicates are merged automatically into a single, trusted record, while low-confidence matches are flagged for quick human review. The result is a unified catalog where every SKU represents a unique, validated product and every search result displays only what buyers need to see - no clutter, no confusion.
When distributor catalogs are mapped, normalized, and deduplicated end to end, the improvements are obvious. Search pages feel quieter and more relevant because near-identical items collapse into a single, authoritative product detail page. Facets shrink to the values buyers actually use, which increases filter interaction and narrows results more predictably. New supplier feeds move from intake to publish faster because category decisions and attribute rules are applied consistently. On the back end, teams spend less time reconciling conflicts and more time on merchandising and pricing strategy. Even basic reporting gets easier: one product equals one record, so inventory, returns, and revenue no longer fragment across duplicates.
Just as important, the system becomes self-improving. Each approval teaches the model how your taxonomy works, which values are acceptable, and when a new category or attribute should be created. The result is a catalog that keeps pace with your assortment instead of drifting into chaos as the SKU count grows.
Clean, consistent product data isn’t just a back-office exercise - it’s the foundation of modern B2B search and discovery. When every SKU maps cleanly to your taxonomy, attributes are normalized across suppliers, and duplicates are eliminated, buyers get the simple, accurate search experience they expect. Behind the scenes, your teams save hours of manual cleanup and your catalog becomes a single source of truth that’s easier to scale, audit, and optimize.
CatalogIQ™ automates this entire process - mapping new SKUs to the right categories, harmonizing attributes across supplier feeds, and detecting duplicates before they reach your storefront. The result is a structured, search-ready catalog that drives faster onboarding, cleaner filters, and higher conversion across every digital channel.
If you are beginning this journey, track a few simple indicators to prove progress. Time to publish for new SKUs should decline as mapping automates. Average number of facets per category should stabilize as synonyms roll up into approved values. Duplicate rate should trend down as more suppliers are normalized. On the demand side, look for higher search CTR, lower bounce on filtered result pages, and more traffic consolidating to your canonical PDPs. These are practical signals that cleaner data is translating into a better buyer experience and more efficient operations.
What’s your CatalogIQ?
Ready to clean, classify, and unify your catalog data? Let’s talk.
Contact Kevin Jackson at kevin.jackson@magnetlabs.ai to schedule a demo of CatalogIQ™ - MagnetLABS’ AI-powered solution for building, normalizing, and enriching distributor catalogs at scale.