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by Trivas.aiSomebody asks "what's a good ROAS" and gets back a single number, like 4x, as if a supplement brand and a furniture retailer run the same business. They don't. Margin structures, purchase cycles, and return rates differ so much by category that a blended "ecommerce average" is close to meaningless the moment you apply it to your own store.
That's the problem with most of the benchmark roundups floating around. They cite a number with no sample size, no date, and no category breakdown, often pulled from a vendor's own customer base and presented as gospel. A benchmark built from last year's iOS 14.5 chaos doesn't tell you much about today's auction dynamics either.
This piece on industry benchmarks by sector skips the single magic number. Instead, you get ranges by category, the context to read them correctly, and a method for benchmarking your own brand against itself first. That's the part most roundups skip entirely, and it's the part that actually changes decisions.
Five metrics come up constantly when brands compare themselves to peers: customer acquisition cost (CAC), blended ROAS, average order value (AOV), conversion rate, and repeat purchase rate. Each tells you something different, and mixing them up is how founders end up chasing the wrong number.
CAC and ROAS are channel-dependent. They move with your ad mix, your creative, your bidding strategy, and the platform you're buying on. A brand running 80% of spend on Meta will have a different ROAS profile than one leaning on Google Shopping or TikTok, even with identical margins.
AOV and conversion rate are more store-dependent. They're shaped by your pricing, your product page, your checkout flow, and your site speed, less by which ad platform sent the traffic in.
Repeat purchase rate is the outlier here, and it's the most sector-sensitive metric of the five. A one-time big-ticket purchase, think mattresses or furniture, might sit under 10% repeat rate and that's completely normal. A consumable, like coffee or supplements, should be pushing 40%+ if the retention engine is working. Comparing those two numbers side by side tells you nothing. Comparing a coffee brand's 18% repeat rate to the 40%+ sector norm tells you a lot.
Here's where directional ranges actually help, as long as you treat them as ranges and not targets to hit on a dashboard.
Apparel and fashion: conversion rates typically run 1.5-3%, AOV lands around $60-120. Return rates are brutal in this category, often 20-30%, and that distorts what "good" margin actually looks like after the fact. A strong ROAS on paper can look a lot worse once returns settle.
Beauty and personal care: conversion rates run a bit higher, often 2-4%, helped by lower price points and strong subscription attach rates. Repeat purchase rate tends to be the standout metric here, since consumables and routine-based products lend themselves naturally to reorders.
Electronics and home goods: conversion rate drops to 1-2%, but AOV climbs past $150 in many cases. Buyers in this category research longer, compare more, and convert slower. Don't panic if your conversion rate looks low against an apparel benchmark. It's a different buying decision entirely.
Food, beverage, and consumables: AOV is usually the lowest of the group, but repeat purchase rate is the highest. ROAS targets here tend to be more forgiving of thinner per-order margins, because the real value shows up over the second, third, and tenth order, not the first.
A caveat worth repeating: these ranges shift with store maturity and ad spend mix. A six-month-old store spending aggressively on cold traffic will post worse numbers across the board than a five-year-old brand with a warm email list and organic search traffic doing half the work. Don't benchmark a startup against a category leader's numbers and call it a problem.
If you want to sanity-check your own ROAS math against spend and revenue, the ROAS calculator is a quick way to see where you land before comparing against any sector range.
This is where a lot of founders trip up. Amazon ACoS and TACoS benchmarks are not the same animal as Shopify blended ROAS, and treating them as interchangeable leads to bad conclusions.
Amazon traffic is largely high-intent search traffic. Someone typing a product name into the Amazon search bar is often close to a purchase decision already, so ACoS numbers tend to look stronger on paper. Amazon's own discovery engine, its recommendation widgets, its Buy Box mechanics, does a chunk of the acquisition work for you.
Shopify carries the full weight of cold traffic. Every dollar spent on Meta or Google for a Shopify store is often trying to build awareness and intent from scratch, not just capture demand that already exists. That's a heavier lift, and it shows up in blended ROAS.
So when a founder compares their Shopify-only brand's 2.5x ROAS to a competitor's Amazon-heavy 6x ACoS-implied return and concludes they're losing, that's usually an apples-to-oranges read. The platforms are solving different problems. If you're running both, you need Amazon-specific benchmarks for Amazon traffic and Shopify-specific benchmarks for your DTC funnel, reported separately, not folded into one blended number that hides which channel is actually carrying the business.
Before you compare yourself to anyone else, compare yourself to your own trailing 12-month trend. That's the benchmark that actually matters day to day. Is your conversion rate up or down quarter over quarter? Is CAC creeping, flat, or improving? That internal trend line will tell you more than any sector average will.
Once you've got that baseline, segment by acquisition channel before you do anything else. A blended CAC of $45 might look fine, but if Meta is sitting at $65 and organic search is at $12, the blended number is hiding a real problem. Break it out by Meta, Google, TikTok, and organic, and you'll usually find one channel dragging the average down while another quietly overperforms.
Seasonality matters too, and it matters unevenly across sectors. Apparel and gifting categories swing hard around Q4 and major shopping events, so comparing November numbers to a July baseline without adjusting for seasonality will make you think something broke when nothing did. Consumables are flatter year-round, so seasonality adjustments matter less there. Know which kind of business you're running before you panic over a monthly dip.
Static benchmark tables, including the ranges above, go stale fast. Ad platforms change their auction dynamics, attribution windows shift, and a number that was accurate six months ago can be off today. The real fix isn't finding a better static table. It's having a live view of your own numbers, segmented by channel and by the metrics that actually matter for your sector, updated continuously instead of checked once a quarter.
That's the gap Trivas is built to close: pulling Amazon, Shopify, and ad platform data into one reporting layer so you're tracking CAC, ROAS, AOV, and repeat purchase rate against your own trend, not a vendor's blended average from a roundup article.
If you want to see your sector-relevant numbers instead of a generic benchmark, start a trial and connect your store.
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