How fasteners buyers search now — and why your catalog isn't the answer
Fastener buyers now ask AI engines before opening your catalog. Thin ERP descriptions can't answer their questions — here's what data has to look like instead.
A buyer who needs a hundred Grade 8 hex cap screws for a machine rebuild doesn't start by typing a part number into your site search anymore. He opens ChatGPT or Perplexity and describes the job: the thread size, the load, the finish he needs for outdoor exposure. If your product data can't answer that question in a form a model can parse, the engine quietly routes the buyer to whichever distributor's data made the match obvious — and you never see the search happen.
The search moved before the catalog did
Recent research puts this shift in numbers. A multi-source analysis found 73% of B2B buyers now use AI tools in their purchase research, and Forrester's 2026 Buyers' Journey Survey reportedly found buyers name generative AI or conversational search as their single most meaningful research source — ahead of vendor websites, product experts, or sales reps. A separate UK study found 66% of B2B decision-makers now use AI tools for supplier research, with 90% saying they trust what the AI recommends, and that just five brands in a given category capture roughly 80% of AI-generated recommendations.
That last stat is the one that should worry a fastener distributor. It's not a level playing field where every catalog gets a fair look. The engine picks a small number of winners per query, and it picks them based on which product data is legible enough to trust.
Fastener buying is a near-perfect test case for this. A single item can be correctly specified or completely wrong for the application depending on thread pitch, grade, material, finish, head style, drive type, and the governing standard — DIN, ISO, ASTM, or SAE. There's no room for "close enough" the way there might be with a generic office supply. An answer engine that gets a fastener recommendation wrong sends a customer a part that doesn't fit or fails under load, so it has every incentive to only recommend distributors whose data actually states the spec instead of implying it.
Why your feed reads as empty even when the SKU exists
The industry's own data problem is well documented: most fastener distributors run on nothing more than a part number and a basic ERP description — no images, structured attributes, spec sheets, or marketing copy. That's fine for a counter clerk who already knows the part cold. It's close to invisible to a model that has no prior context and has to infer the spec from the string alone.
Here's what that gap looks like side by side:
| Field | Raw ERP feed | Enriched attribute data |
|---|---|---|
| Description | HXCS 1/2-13X3 GR8 ZN | Hex Cap Screw, 1/2"-13 UNC thread, 3" length |
| Grade | (implied in string) | Grade 8, SAE J429 |
| Material | (missing) | Medium carbon alloy steel |
| Finish | (implied in string) | Zinc-plated (Fed Spec QQ-Z-325, Type II) |
| Head style / drive | (missing) | Hex head, external hex drive |
| Tensile strength | (missing) | 150,000 psi minimum |
| Standard | (missing) | ASTM A574 / SAE J429 Grade 8 |
| Application notes | (missing) | Rated for high-clamp-load steel-to-steel joints; not recommended for aluminum without washer per corrosion class |
The left column is a string a human who already knows fasteners can decode. The right column is a set of facts a model can match against a question. That's the entire difference between a SKU that shows up in an answer and one that doesn't.
What an actual buyer question looks like
Ask an answer engine this, the way a maintenance buyer or contractor actually would: "I need a Grade 8 hex cap screw, 1/2-13 thread, 3 inches long, zinc plated, that a distributor can ship same day and that's rated for a steel-to-steel structural joint — who has it in stock?"
To answer that, the model needs thread size, grade, length, finish, standard, and application fit stated as discrete values, not folded into a ten-character abbreviation string. It also increasingly needs to see that data at the source — supplier documentation and spec sheets — not just a marketing paragraph, because engines weight verifiable, source-grounded attributes over descriptive copy. A distributor whose flat file or ERP export has never been enriched past the item master has no answer to give. A distributor whose data has been pulled from cut sheets, standards documents, and manufacturer specs, then structured and quality-scored, gets cited.
Fixing this doesn't mean fixing your PIM first
The instinct is to treat this as a PIM project: pick a platform, migrate the item master, wait a year. That's the wrong order of operations for a problem that's costing you visibility right now. Your PIM — Akeneo, Salsify, inriver, Stibo, Syndigo, Pimcore, Informatica, or none at all — stores the data. It was never built to extract thread specs and grade callouts out of a compressed ERP string, gap-fill missing attributes from supplier documentation, and keep that data current as new SKUs come in.
That's a separate, additive layer, and it doesn't require a multi-year systems engagement to start. Anglera plugs into whatever you already run — or starts from a flat file if you don't run a PIM at all — and scores, enriches, and gap-fills the attributes above from real supplier and source documents, live in weeks rather than quarters. Manual attribute enrichment for a catalog this size typically runs 30-45 minutes per SKU when done by hand; the fastener buyer asking an answer engine a spec question doesn't wait for that backlog to clear, and neither does the distributor across the aisle whose data already answers it.