by Om RathodType "Shopify conversion rate benchmark" into Google and you'll get a number somewhere between 1.4% and 2.5%. That number is technically true and mostly useless.
It's an average across every category Shopify supports, from $25 phone cases to $3,000 sectional sofas. Blending those together tells you almost nothing about whether your store is healthy.
Here's what that looks like in practice. A beauty brand sees a 2% blended benchmark, looks at its own 3.2% conversion rate, and assumes it's crushing it. Actually, 3-4% is just normal for skincare and cosmetics [VERIFY]. Meanwhile, a furniture brand converting at 0.8% panics, when 0.8% might put them ahead of most of their direct competitors [VERIFY]. Same data source, opposite conclusions, both wrong.
This report breaks down conversion rate, AOV, cart abandonment, and repeat purchase rate across six common Shopify verticals: apparel, beauty, home goods, food/beverage/supplements, and electronics. The goal is to give founders a comparison set that actually matches their business, not a generic number pulled from a blog post with no category breakdown.
One honest caveat before we get into it: these figures are directional ranges compiled from public ecommerce benchmark reports [VERIFY exact sources before publish], not Trivas proprietary data. Treat them as a starting point for Shopify brand benchmarks by vertical, not a scorecard to hit. Your own trend over time matters more than any single number in this post, and we'll get to why in the last section.
Fashion and apparel stores tend to run conversion rates around 1-2% [VERIFY], on the lower end of ecommerce overall. That's not a red flag by itself. Apparel shoppers browse across multiple sites, compare sizing, and abandon carts constantly before they buy anywhere.
AOV varies a lot by price point, but the bigger lever in this category is bundling. A single-item cart in apparel is a missed opportunity. Brands that push "complete the outfit" upsells or free-shipping thresholds tend to see meaningfully higher AOV than ones that don't, because the marginal cost of adding a second item to a fashion cart is low and the customer is already in a buying mindset.
The metric that quietly wrecks apparel margins isn't conversion rate. It's returns. Online-only fashion often sees return rates in the 20-30% range [VERIFY], driven mostly by sizing guesswork. A brand converting at 2.5% with a 30% return rate can be in worse shape than one converting at 1.5% with a 12% return rate. Return rate needs to sit right next to conversion rate on any apparel benchmark dashboard, not buried in a separate report nobody checks weekly.
Seasonality is the other wildcard. Fashion sees some of the widest month-to-month swings in traffic and AOV of any vertical, holiday and back-to-school spikes especially. Benchmarking against last month is often misleading. Benchmarking against the same month last year is usually more honest.
Beauty and skincare brands typically post the strongest repeat purchase and subscription attach rates on Shopify. Makes sense: people run out of moisturizer and reorder it, they don't run out of a jacket.
AOV in this category usually sits lower than most, often in the $30-60 range [VERIFY]. On its own, that number looks unimpressive next to a home goods brand doing $400 AOV. But single-order AOV is the wrong lens here. A customer buying a $45 serum four times a year is worth more over 12 months than a lot of higher-AOV, one-and-done categories.
This changes what "good" CAC actually looks like. A beauty brand can often sustain a higher CAC relative to AOV than a furniture or electronics brand, because repeat purchases pay back that acquisition cost over time. If you're benchmarking a skincare brand's CAC against its first-order AOV alone, you'll conclude it's overspending on ads when it's actually fine, sometimes great, on a 6- or 12-month LTV basis.
This is exactly the kind of thing that gets missed when founders are stitching together Shopify order data and ad spend in separate tabs. BI reporting built to track LTV and repeat rate alongside CAC, instead of just first-order economics, catches this before you cut a channel that's actually working.
Furniture and home goods stores run some of the lowest conversion rates in ecommerce, often under 1% [VERIFY]. Nobody buys a couch on the first visit. People measure their living room, compare five brands, ask a partner, and come back three weeks later. That's not a broken funnel, that's just how the category works.
The offset is AOV. A $1,200 order size covers a lot of sins in conversion rate. The mistake founders make here is benchmarking CAC against a flat dollar target, like "get CAC under $80," borrowed from a category where AOV is a tenth of theirs. CAC only means something in this vertical when it's evaluated as a ratio against AOV, not as a standalone number.
Cart abandonment also runs higher in home goods, and that's fine. In a low-consideration category like snacks or phone cases, a spike in abandoned carts is a real problem. In furniture, high abandonment often just means people are pricing-shopping across tabs before committing, which is normal behavior for a purchase this size.
Consumables live and die on reorder rate. Subscription and reorder attach rates tend to run high in this vertical, and that's the number worth tracking closely, not first-order conversion rate or first-order AOV on their own.
Shipping cost plays a bigger role in margin here than almost anywhere else. A $6 shipping cost on a $30 skincare order is a rounding error. That same $6 on a $22 bag of coffee is a real chunk of margin. "Profitable AOV" in food and beverage has to account for shipping cost as a percentage of order value, not just the order value itself.
The common mistake: comparing a supplements brand's first-order conversion rate directly against a one-time-purchase apparel or electronics brand. Subscription brands frequently discount that first order to get people onto a plan, which depresses first-order revenue but sets up a much better long-term number. If you're only looking at conversion rate and first AOV, a subscription brand can look weaker than it actually is.
Electronics and other higher-ticket goods see lower conversion rates and longer session-to-purchase windows than almost any other category on this list. Nobody drops $600 on a first visit from a cold Facebook ad. They research, read reviews, compare specs, leave, come back.
What actually moves the needle here is trust. Review volume and quality, a visible warranty, a clear return policy, and financing options at checkout all do heavier lifting for conversion in this vertical than they do for a $25 impulse purchase. Strip those out and conversion rate drops noticeably, even with identical traffic quality.
This also means ROAS benchmarks in electronics tend to look artificially bad if you're only looking at last-click attribution. A customer might see a TikTok ad, click a retargeting ad two weeks later, then convert from a branded search three days after that. Attribute all the credit to the last click and the earlier channels look like they're wasting spend, when they're actually driving the research phase that leads to the sale. This is one of the more common blind spots we see in Shopify accounts pulling ad data straight from platform dashboards without reconciling it against actual purchase paths.
Public benchmark reports, including this one, are a starting point. They're not a target. The real question isn't "am I above or below the industry average," it's "is my own trendline improving quarter over quarter."
A proper benchmark dashboard should show, side by side: conversion rate, AOV, CAC, repeat purchase rate, and margin, segmented by channel and product category. Not five separate spreadsheets that someone reconciles manually once a quarter and half-trusts by the time it's done.
Most brands can't actually get this view without stitching together Shopify order data, ad platform spend, and GA4 session data by hand. That's a real time cost, and it's usually why benchmarking becomes a once-a-quarter fire drill instead of something teams check weekly. A unified reporting layer that pulls all three sources into one place replaces that spreadsheet work entirely, and it's the difference between reacting to last quarter's numbers and catching a problem while it's still small. If you're setting this up for the first time, the Shopify integration guide walks through what data actually needs to connect.
Benchmark against your vertical, not the blended Shopify average, and treat your own trend over time as the real signal. A furniture brand chasing a 2% conversion rate borrowed from a skincare blog post is optimizing for the wrong number entirely.
Trivas pulls Shopify, ad platform, and GA4 data into one dashboard, so founders and growth leads can see their real conversion rate, AOV, and repeat purchase trends without rebuilding this report from scratch every quarter. If you're the one currently stitching these numbers together in a spreadsheet at 11pm before a board meeting, that part of the job doesn't need to exist anymore.
Start a free trial and see what your vertical's actual numbers look like on your own store.
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