Search for conversion rate benchmarks and you will find dozens of pages quoting confident, precise, mutually contradictory numbers. One says the ecommerce average is 1.4%. Another says 2.9%. A third says mobile converts at half of desktop; a fourth shows mobile ahead.
They are not all wrong. They are measuring different populations with different definitions in different years, and almost none of them say so. This page gives you the current figures from primary sources, states what each one actually measured, and explains where the disagreements come from.
Figures verified 3 August 2026. Every number below links to its source.
Why Published Benchmarks Disagree
Four variables account for nearly all the variation, and understanding them is more useful than any single number:
- Platform. A Shopify-only dataset and a mixed-enterprise dataset describe different businesses at different price points.
- Definition. Is the denominator sessions or users? Is the conversion a transaction, a lead, or any goal completion? A "conversion rate" computed on users runs meaningfully higher than one computed on sessions.
- Market and vertical mix. A benchmark weighted toward fashion and one weighted toward B2B software will differ by more than most optimization work ever achieves.
- Date. Several widely cited benchmark pages are still quoting datasets from 2023. Nothing labels them as stale.
When two benchmarks differ by 2x, the explanation is usually in this list rather than in anyone's performance.
Overall Ecommerce Conversion Rate
IRP Commerce publishes monthly market data across its merchant base. For June 2026:
| Metric | Value |
|---|---|
| Overall conversion rate | 2.03% |
| Same month, prior year | 1.85% |
| Year-over-year change | +9.74% |
For a Shopify-specific view, Littledata's benchmark across 2,800 stores puts the average at 1.4%, with the top 20% above 3.2% and the top 10% above 4.7%. Note the date on that one: it reflects 2023 data, and it is one of the most-cited benchmarks on the web.
By Industry
Industry is the single largest source of variation — larger than device, channel, or anything you are likely to change.
From IRP Commerce, June 2026:
| Sector | Conversion rate | YoY change |
|---|---|---|
| Arts and Crafts | 5.53% | +37.13% |
| Kitchen & Home Appliances | 2.84% | −22.83% |
| Pet Care | 2.70% | −2.16% |
| Health and Wellbeing | 2.58% | +13.64% |
| Sports and Recreation | 1.95% | +14.98% |
| Cars and Motorcycling | 1.78% | +45.98% |
| Fashion Clothing & Accessories | 1.70% | +13.07% |
| Toys, Games & Collectables | 1.63% | −33.91% |
| Food & Drink | 1.31% | +15.59% |
| Baby & Child | 0.51% | −33.17% |
Dynamic Yield's benchmarks, drawn from 400+ brands and 200M+ monthly unique users, put Pet Care & Veterinary Services highest at 5.7%, Beauty & Personal Care at a 5.37% twelve-month average, and Luxury & Jewelry lowest at 0.71%.
How to read this: the two sources rank sectors differently and report different absolute levels for the same category. That is expected — different merchant bases, different regions, different session definitions. Use the spread (roughly 0.5% to 5.5%) and your sector's relative position, not the decimal places.
The year-over-year swings are also worth noticing. Several categories moved 20–45% in a year. If your rate moved 15% and you attribute it entirely to your optimization work, you may be taking credit for your category.
By Device
This is where published benchmarks disagree most sharply, and where you should be most skeptical of confident claims.
| Source | Mobile | Desktop | Note |
|---|---|---|---|
| Littledata (Shopify, 2023) | 1.2% | 1.9% | Desktop ahead by ~58% |
| Dynamic Yield (global, current) | 2.75% | 2.47% | Mobile ahead |
Both are real datasets. They point in opposite directions.
The widely repeated claim that "mobile converts at half of desktop and the gap is widening" traces back to older, platform-specific data and does not hold up against current global benchmarks. A persistent mobile gap is real in some verticals and datasets — particularly high-consideration and high-ticket purchases, where research happens on a phone and the transaction happens on a laptop — but it is not a universal law, and treating it as one leads teams to over-invest in a problem they may not have.
What to do instead: measure your own device split, and check whether the gap survives controlling for traffic source. Mobile traffic skews toward social and discovery channels with inherently lower intent. A meaningful share of the apparent device gap is a channel-mix effect wearing a device costume.
By Traffic Source
Channel is the second-largest source of variation, and the newest entrant is the most contested.
Semrush's analysis of 50,000+ websites across 17 industries found that in 2025, AI traffic accounted for less than 0.15% of total visits while growing 66% year over year — against organic search growth of 2.38%. Google's AI Mode reached 0.01% of total traffic.
On how well that traffic converts, two credible studies reach opposite conclusions:
| Study | Scope | Finding |
|---|---|---|
| Visibility Labs | 94 ecommerce brands, GA4, Jan–Dec 2025 | ChatGPT 1.81% vs non-branded organic 1.39% — 31% higher for AI |
| SSRN working paper | 973 sites, $20B revenue, Aug 2024–Jul 2025 | Organic 13% higher than ChatGPT; affiliate 86% higher |
The first excluded homepage and blog traffic to isolate commercial intent; the second compared against all channels including branded organic. Claims that AI traffic converts "4.4x" or "23x" better than organic do not appear in either primary source and should be treated with suspicion.
There is also a structural undercount here. People frequently ask an assistant for a recommendation and then search the brand on Google before buying, so last-click attribution books the conversion as branded organic. Both research teams flagged this.
How to Use Benchmarks Without Being Misled
Use them to size opportunity, never to set targets. Knowing your sector runs 1.5–2.5% tells you whether 1.1% is a problem worth investigating. It does not tell you what your rate should be, because your traffic mix, price point, and business model are not the benchmark's.
Compare against yourself first. Your own trend line, segmented by channel and device, is a better decision input than any external figure. A conversion rate is a ratio of two numbers that both move for reasons unrelated to your site quality.
Check the denominator before comparing anything. Sessions versus users is the most common apples-to-oranges error in benchmark comparisons.
Segment before concluding. An aggregate rate blends channels with wildly different intent. A drop in aggregate conversion often means the traffic mix shifted, not that the site got worse.
Watch for composition effects. Rising bot and agent traffic dilutes conversion rates without any change in customer behavior. If your filtering has not kept pace with the growth in non-human traffic, part of your year-over-year decline is measurement, not performance.
The Bottom Line
The honest summary of 2026 conversion benchmarks: ecommerce sits around 2% overall, industry spread runs from roughly 0.5% to 5.5%, device differences are dataset-dependent rather than universal, and AI referral traffic is still small enough that its conversion advantage — if it has one — is genuinely unsettled.
Anyone quoting these numbers to two decimal places without naming a source and a date is selling certainty that the underlying data does not support. The useful move is to establish your own baseline, segment it properly, and measure change against it.
If you want that baseline maintained continuously rather than assembled by hand each quarter, Surface AI tracks conversion performance by segment and runs experiments against it automatically.