Cutting wrong-part returns in building materials with better product data

A contractor orders an LVL beam from a product page that lists a size and a price and not much else. The beam that shows up doesn't match the engineered plan, the job stalls, and the part comes back. Multiply that across every SKU in a building-materials catalog missing a grade, span rating, or treatment spec, and you have a returns problem that looks like logistics but is actually a data problem.

The return isn't really about the beam

Industry analysis of construction product data puts the scale of this in plain terms: incomplete or unclear product information is behind up to 35% of product returns in sectors where precision matters, and building materials is exactly that kind of sector. A 2x10 is not a 2x10 once you factor in species, grade stamp, moisture content, and treatment. An LVL beam is not interchangeable with another LVL beam just because the depth matches.

The same research describes a familiar failure mode: a buyer gets one spec from a sales rep, a different spec on the website, and a third version on the technical data sheet. None of the three is wrong, exactly — they were entered at different times, by different people, from different source documents. In electrical distribution, a parallel problem has been estimated to cost the channel $5 billion a year in lost sales, returns, and manual rework. Building materials distributors run the same vendor feeds and the same understaffed catalog teams — the exposure is structurally similar even where the dollar figure hasn't been separately measured.

Buyers also increasingly don't want to call and ask. Gartner research cited in that same piece found 67% of B2B buyers prefer a rep-free experience. If the product page can't answer the question, the buyer guesses, orders the wrong part, or calls your counter staff anyway — defeating the point of a self-service catalog.

What a building materials buyer actually needs to know

Before a contractor, estimator, or engineer will click "add to cart" on a structural or exterior product, they're checking for answers to a short, specific list of questions. For an engineered wood product like an LVL beam, that list looks like:

• What's the grade and design value (Fb bending strength, E modulus of elasticity — e.g. 1.9E-2850Fb, 2.0E)?
• What depths and widths is it available in, and how many plies does my span require?
• What's the maximum span at this depth, for this load and deflection limit (L/360 live load is standard for floors)?
• Is it rated for the application — floor, roof header, or column?
• What's the required bearing length at end and interior supports?
• Is it treated, and if so, is the treatment compatible with the fasteners and connectors I'm already speccing?
• Does it ship with an ICC-ES or equivalent code report I can hand to an inspector?

Miss any one of these and the buyer abandons the page or orders based on a guess. A missing ply count or an unclear span rating doesn't just cost a sale — it costs a delivery, a restocking fee, and a support call when the crew opens the crate on site.

Before and after: the same LVL beam, two different product pages

Here's what a typical supplier feed hands a distributor, next to what a buyer actually needs to see.

Raw feed description (typical): "LVL Beam 1.75x11.875 - engineered wood - 24ft - premium grade"

Enriched attribute table:

The raw description tells a buyer the product exists. The attribute table tells them whether it's the right one for their span, their load, and their connector — the actual decision they're making.

Ask an answer engine

This is also how buyers are starting to search before they ever land on a distributor's site. A contractor typing into an AI answer engine isn't asking "LVL beam price" — they're asking something like:

"What LVL beam depth do I need to span 22 feet on a residential floor with L/360 deflection, and does it need to be treated for a covered but unconditioned porch?"

If your product data only has a size and a price, there's nothing for the answer engine to pull from your catalog — it will cite a manufacturer's span chart instead, and the buyer clicks through to a competitor's page when they're finally ready to buy.

A checklist for building-materials distributors

• Audit high-return SKU categories (engineered wood, fasteners, treated lumber, roofing) for missing grade, span, and treatment attributes first — that's where wrong-part returns concentrate.
• Standardize units and naming across sales rep sheets, the website, and technical data sheets so one product doesn't show three different spec sets.
• Pull design values and code reports from actual supplier documentation, not a generic category template.
• Flag SKUs where span or load data is genuinely mill-dependent, rather than guessing a number.
• Score every product page against the buyer-question list above before it goes live, not after return volume shows up.
• Re-check enrichment whenever a supplier updates a data sheet — grade stamps and code reports change more than catalogs get updated.

Fixing this at scale isn't a matter of hiring more catalog staff to retype spec sheets faster. Your PIM stores the data — Akeneo, Salsify, Syndigo, or a flat file, it doesn't matter which. Anglera plugs into that layer and does the work: scoring every product page against a buyer-question checklist like the one above, gap-filling attributes from actual supplier documentation rather than guessing, and keeping them current as specs change — live in weeks, not a multi-year system migration, because the fix is additive to what's already there.