Cutting returns in furniture & home with better product data

Furniture shoppers buy blind, without touching the fabric, sitting on the cushion, or measuring the doorway themselves. Every one of those judgment calls gets made from the product page, and when the page is thin, the shopper guesses, orders anyway, and returns it when the guess is wrong. That pattern is expensive in a category where a single return can cost more than the item's margin.

Why furniture returns are a data problem, not a shipping problem

Furniture and home goods are consistently singled out as the costliest category to process a return in, with per-item processing running $55-90+ once inspection, restocking, and freight are factored in, according to Eightx's 2026 vertical cost breakdown. That's before the retailer eats a restocking fee or writes off a damaged piece that can't be resold as new.

Retailers routinely point to two root causes: the product didn't match its description, and the shopper misjudged whether it would fit. Clickpost's analysis of furniture returns puts it plainly — unlike apparel, where returns are driven by buyer indecision, furniture returns trace back to "product damage, incorrect size, or mismatched expectations." Both of the size and expectation failures are fixable on the page, before the order ever ships.

The questions a sofa page has to answer

A shopper deciding on a sofa is running through the same checklist every time, whether or not the retailer gives them the data to answer it:

• Will it fit through my door and up my stairs?
• Will it fit against my wall, and will there be room to walk around it?
• Is the seat depth right for sitting upright versus lounging?
• What is it actually made of, and how does that fabric wear and clean?
• Can one person carry it in, or does it need two?

Sofa buying guides consistently point out that overall width tells a shopper whether a sofa fits the wall, but seat depth tells them whether it will feel right to sit on — and most listings only answer the first question. Delivery-team access (door width, stair turns, packaged dimensions) is the measurement shoppers are told to check for themselves precisely because retailers so rarely provide it.

Here's what a typical raw feed looks like next to what a shopper actually needs, using a mid-size three-seat sofa as the example:

Nothing in the "enriched" column is exotic. It's the information a shopper would ask a salesperson on a showroom floor, made explicit instead of assumed.

The same gaps are now costing you AI recommendations

Shoppers increasingly skip the browsing step entirely and just ask an AI assistant for a recommendation. Try it yourself: ask an AI shopping assistant to "recommend a sofa that fits an 8-foot-wide wall and works for a small apartment with narrow stairs." The assistant needs assembled dimensions, packaged dimensions, and delivery-clearance data to answer that question with any specific product — and if your catalog doesn't carry that data, it will recommend a competitor's listing that does, or hedge with a generic non-answer that doesn't mention your product at all.

This isn't hypothetical. Reporting on furniture retail heading into 2026 notes that home goods catalogs routinely lack dimensional accuracy at the attribute level — assembled versus unassembled sizing, doorway and stair clearance, and packaged weight for solo-versus-two-person delivery — which are exactly the fields an AI agent needs to match a product to a room. A listing built only for a human scrolling through photos doesn't give an AI agent enough structured signal to recommend it at all.

A checklist for the furniture PDP

Before a sofa, sectional, or case-good listing goes live, it should carry:

• Overall width, depth, and height, plus seat depth, seat height, and arm height
• Packaged/shipping dimensions, separate from assembled dimensions
• Weight, both assembled and packaged, with a note on 1-person versus 2-person carry
• Delivery clearance guidance: minimum doorway width, stair-turn considerations
• Fabric or material composition, plus a durability or cleaning spec (rub count, stain resistance)
• Fill type and firmness description, not just "foam"
• Assembly requirements and estimated time
• Care instructions specific to the material, not a generic disclaimer

Every gap on that list is a shopper decision made on incomplete information — and a return, or a lost AI recommendation, waiting to happen.

Anglera closes these gaps automatically, scoring every furniture SKU against a checklist like this one, gap-filling missing dimensions, materials, and care fields, and keeping the page current as suppliers update specs. It plugs into whatever PIM or commerce platform already holds the catalog — no migration, no rip-and-replace — and does the enrichment work so the page can finally answer the questions shoppers, and now AI agents, are already asking.