Selling the same SKUs as everyone: differentiate on data, not price

Run the same manufacturer part number through three distributor sites and you'll usually get three different pages selling the exact same physical product. Same GTIN, same specs, same box. What's different is the description, the attributes, and whether a buyer — human or AI — can actually tell what the thing does. When the SKU itself can't be the differentiator, product data becomes the only lever left besides price. And price is a race to zero.

The commoditization math hasn't changed, but who sees it has

Multi-line distribution has always meant carrying overlapping catalogs. What's changed is how fast a buyer can compare you against the distributor next door. Buyers now check competitor pricing mid-quote, on a phone, before they've hung up. When a customer asks a distributor to match a price that's 8% lower, matching it can wipe out most of the margin on that account — Bizowie's analysis of the distributor's pricing dilemma lays out exactly how thin that math gets once price transparency turns every negotiation into a commodity negotiation. BlueLinx, a public building-products distributor, reported gross margin down 190 basis points year over year in Q1 2025 — a real, filed number, not a hypothetical, showing what commoditization pressure looks like on an income statement.

If price is the only variable a buyer can evaluate, price is what they'll decide on. The fix isn't a better pricing algorithm. It's giving buyers something else to evaluate — which means the data has to actually say something.

What "the same SKU" looks like on two different pages

Here's a fastener line item as it typically arrives from a supplier feed, next to what it looks like after the missing structure gets filled in from the spec sheet and datasheet that already existed, just not in the feed.

Raw feed description: SS 1/4-20 X 1 HEX HD CAP SCR

Enriched attribute table:

One of these rows is searchable, filterable, and answerable. The other is a string a human has to squint at. Same physical part, same SKU cost, wildly different chance of surfacing when a buyer's search — human or machine — narrows by thread size and material.

Ask an answer engine, not just a search box

A growing share of B2B research now starts in a chat window, not a search bar. Forrester's most recent buyers' journey research found buyers are now nearly twice as likely to name generative AI or conversational search as their most meaningful research source, ahead of vendor websites, product pages, and sales reps (Forrester: From Keywords to Context), and 95% of B2B buyers say they plan to use generative AI somewhere in a future purchase.

Try the buyer's actual query: "which distributors carry a passivated 1/4-20 x 1 stainless hex cap screw compliant with ASME B18.2.1." An answer engine pulling from structured, attribute-complete pages can name your SKU with confidence. Pulling from a bare description string, it can't extract enough to cite you at all — it just skips the page, because ambiguous data creates hallucination risk the model would rather avoid than resolve. Distributors watching AI-referred traffic show up in their analytics for the first time are running into this exact filter, whether they've diagnosed it yet or not.

That filtering isn't cosmetic. Research on AI-driven B2B discovery has found brands that appear in an AI system's cited sources convert at meaningfully higher rates than the same content ranking organically — being the answer beats being a link. Complete attribute data is the entry ticket to being citable in the first place.

Margin follows differentiation, not the other way around

None of this requires ripping out how a distributor already manages product data. It requires treating the description field as a liability when it's the only thing separating your SKU from a competitor's identical listing. A few things worth checking against your own catalog:

Fixing these at scale is genuinely tedious work — manual enrichment of a single SKU, done properly against source documents, tends to run 30-45 minutes when a person does it by hand, which is why most catalogs stay half-done. It's also not a multi-year systems project; it's closer to weeks once someone commits to running it as an ongoing process instead of a one-time cleanup.

Where Anglera fits

Your PIM or your flat file already holds the raw material — the spec sheets, the supplier feeds, the half-finished descriptions. Anglera plugs into whatever you're already running, or starts from nothing more than an export, and does the unglamorous work of scoring every SKU for completeness, extracting the missing attributes from the documents that already contain them, and keeping that data current as suppliers push updates. It doesn't replace your system of record; it makes the data inside it the thing buyers, and the AI systems increasingly standing in for buyers, can actually act on. When the SKU is identical to three competitors', the catalog that reads clearest wins the query — and the margin that comes with it.