Benchmarks / Electronics

Electronics agentic commerce benchmark

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Across 22 electronics stores Ottom measured at every stage in the 90 days to 19 October 2026, the median agentic readiness score was 97 out of 100. 91% of them let an AI shopping agent reach the payment step.

n = 22 of 27 stores · 21 July 2026 to 19 October 2026 · v1.0.1 · bench-v5 · methodology

Scores come from the 22 stores Ottom measured at every stage. A further 5 were scanned but stopped before checkout, usually at bot protection or a robots.txt rule, so they have no comparable score. All 27 are still counted in the stalls below, because what was found on a store does not depend on how it was scored.

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 electronics as a whole. The methodology sets out what that excludes.

Readiness score

The median of the 22 electronics 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 96 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 22 electronics stores Ottom measured at every stage, 77% scored 80 or better at the compare stage, against 95% at the discover stage.

Each stage of the journey, and the share of electronics stores whose score at that stage was 80 or better.

Reaching the payment step

91% of the 22 electronics 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 electronics stores is products have no GTIN or MPN to match against, found on 63% of the 27 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 27 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.

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.

9 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.

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, Electronics agentic commerce benchmark v1.0.1, 21 July 2026 to 19 October 2026, methodology bench-v5. https://ottom.io/benchmarks/electronics/v1.0.1. 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.

• v1.0.0

  Issued 13 October 2026.

Read next

• What stops an agent in electronics

  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.

See where your electronics store sits