Benchmarks / Home and furniture
Home and furniture agentic commerce benchmark
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Across 30 home and furniture stores Ottom measured at every stage in the 90 days to 19 October 2026, the median agentic readiness score was 97 out of 100. 93% of them let an AI shopping agent reach the payment step.
n = 30 of 30 stores · 21 July 2026 to 19 October 2026 · v2.0.3 · bench-v5 · methodology
A store enters Ottom’s survey only if a catalogue of at least ten products can be read from it in one request, which in practice means Shopify and WooCommerce storefronts. These figures therefore describe the storefronts Ottom can read in bulk rather than home and furniture as a whole. The methodology sets out what that excludes.
Readiness score
The median of the 30 home and furniture stores Ottom measured at every stage in the 90 days to 19 October 2026 scored 97 out of 100, and a quarter of them scored 95 or below.
The spread of overall readiness scores across the category. We publish quartiles rather than a best and worst store, because the best and the worst are each one identifiable merchant.
Where agents stall
Of the 30 home and furniture stores Ottom measured at every stage, 90% scored 80 or better at the discover stage, against 93% at the evaluate stage.
Each stage of the journey, and the share of home and furniture stores whose score at that stage was 80 or better.
| Stage | Median score | Stores passing | Stores measured |
|---|---|---|---|
| Discover | 100 | 90% | 30 |
| Evaluate | 88 | 93% | 30 |
| Compare | 100 | 90% | 30 |
| Buy | 100 | 93% | 30 |
Reaching the payment step
93% of the 30 home and furniture stores Ottom measured let an AI shopping agent reach the payment step. Ottom stops there and never submits payment.
What stops them
The most common stall on home and furniture stores is products have no GTIN or MPN to match against, found on 67% of the 30 stores Ottom scanned in the category in the 90 days to 19 October 2026.
The most common stalls in this category, as a share of all 30 stores scanned. A stall found on fewer than 5 stores is withheld, because at that count it points at a merchant rather than at a category.
None common
- No product identifier67%
None common
None common
| Stall | Stage | Severity | Share of stores |
|---|---|---|---|
| Products have no GTIN or MPN to match againstEvaluate | Evaluate | Medium |
What to change first
- No product identifier
Populate the barcode field (GTIN, EAN or UPC) on each variant, which most platforms already have a field for.
7 further stalls were found in this category but withheld: too few stores showed them to report a share without pointing at an individual store.
How it has moved
Each point is one night’s figure, counted over the 90 days to that night under methodology bench-v5. Neighbouring points share almost all of their stores, so these lines move slowly, and they move when stores enter or leave the window as well as when a store changes. A store Ottom surveyed is measured again at most once every 60 days, so a change it makes shows up at its next reading, not the night it is made.
Median readiness score
Line: the median store. Shaded: the middle half of stores.
Stores passing each stage
Each chart runs from 0% at the bottom to 100% at the top.
Discover
Evaluate
Compare
Buy
Stores where an agent reached the payment step
Stores counted
Solid: stores measured at every stage, which give the scores. Dashed: every store scanned, which the stall shares are counted over.
Citing this benchmark
These figures are published under CC BY 4.0. Quote them freely, with this attribution:
Ottom, Home and furniture agentic commerce benchmark v2.0.3, 21 July 2026 to 19 October 2026, methodology bench-v5. https://ottom.io/benchmarks/home/v2.0.3. Licensed CC BY 4.0.
The same figures as JSON and as Markdown.
Previous versions
A new version is issued when a nightly refresh changes a figure on this page. Earlier versions stay here for 90 days after they were issued.
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Read next
- What stops an agent in home and furniture
The mechanisms behind the figures: which stalls bite harder in this category, and why.
- How these benchmarks are measured
What counts as a store, what counts as a completed checkout, and why a category with too few stores publishes nothing at all.
- All category benchmarks
Every category Ottom measures, and which of them have enough data to publish.
- Why AI agents abandon carts
The failure modes behind the buy stage, and how they differ from human abandonment.
- AI commerce ROI calculator
Put a revenue figure on the stage where agents stall on your store.