Clothing Ad Benchmarks: Meta vs. Google ROAS, CPA, and CPC

Insights from an analysis based on over 100 million eCommerce orders processed via AdScale, with benchmarks for clothing and apparel retailers.

AdScale’s original analysis is based on aggregated performance data from over 100 million eCommerce orders processed via AdScale. The benchmarks reflect clothing and apparel retailers using AdScale, with Google metrics focused specifically on non-brand campaigns.

In this clothing benchmark analysis, Meta recorded a 9.82× return on ad spend (ROAS) and an $11.13 cost per acquisition (CPA). Google non-brand campaigns recorded 5.63× ROAS and a $19.41 CPA. The reported average order value was $109.29.

That puts Meta’s CPA about 43% below Google non-brand in this dataset. But it does not mean every clothing brand should move its budget to Meta. Before using these numbers, you need to understand what each platform figure includes and how much of each order your store keeps after costs.

The comparison below gives you the benchmarks, explains their limits, and shows how to turn them into a useful starting point for your next advertising test.

What Are the Average Ad Benchmarks for Clothing Brands?

The table summarizes the reported clothing and apparel results from AdScale’s analysis. Google figures cover non-brand campaigns.

Average order value across the clothing retailers in the analysis: $109.29. The original analysis uses this shared AOV as the baseline for its comparison.

Source: AdScale clothing industry benchmark analysis. These are historical results for advertisers using AdScale, not universal targets or a forecast. See the methodology and data sources below.

Key Takeaways

• Meta’s reported CPA was $8.28 lower per purchase, a difference of approximately 43%.
• Meta combined a lower CPC with a higher reported conversion rate in this dataset.
• Google non-brand had a slightly higher reported CTR, but CTR alone does not establish traffic quality or profitability.
• Google non-brand and the Meta aggregate are not necessarily equivalent campaign groups.
• Your own contribution margin, return costs, and customer mix determine whether a ROAS is good for your store.

What Is a Good ROAS for Clothing Ads?

A good ROAS covers the costs associated with the orders it generates and leaves enough to support your profit target. An industry average can show you where to investigate, but it cannot set that target for you.

In this analysis, Meta’s 9.82× ROAS means $9.82 in attributed revenue per $1 of advertising spend. Google non-brand’s 5.63× means $5.63 per $1 spent. Both are revenue ratios. Neither tells you how much profit remains.

Start with your contribution margin before advertising:

Contribution margin before advertising = (revenue − variable costs other than advertising) ÷ revenue

Include product costs, fulfillment, payment fees, and the expected financial impact of returns. Use a consistent revenue basis. For example, if revenue is already net of refunds, do not subtract the refunded revenue again as a cost.

Then calculate:

Break-even ROAS = 1 ÷ contribution margin before advertising

These are illustrative calculations. At break-even, the orders cover the variable costs included in the calculation and the advertising spend. They have not yet contributed anything toward fixed overhead or profit, so your operating target will usually need to be higher.

For example, a store with a 40% contribution margin before advertising needs 2.50× ROAS just to cover those costs. A 3.00× campaign clears that threshold, even though it sits below both AdScale benchmarks. Whether it is worth scaling depends on the remaining contribution and the store’s goals.

What Is the Average CPA for Clothing eCommerce?

AdScale’s clothing analysis reports an average CPA of $11.13 on Meta and $19.41 on Google non-brand. The difference is $8.28 per purchase.

Read CPA alongside the value and costs of the orders acquired. A lower CPA is helpful, but a campaign that sells heavily discounted products may leave less contribution than one with a higher CPA and healthier margins.

What Remains After Advertising, Before Other Costs

Using the original analysis’s $109.29 AOV:

The difference is $8.28 per order, exactly as in the original analysis. These amounts still need to cover product costs, fulfillment, payment fees, returns, and other business expenses. They are not profit figures.

An Illustrative Contribution Margin Calculation

The following example adds an assumed margin to explain how to apply the benchmarks. It does not introduce new dataset findings.

Consider an illustrative order worth $109.29 with a 40% contribution margin before advertising. That leaves approximately $43.72 available to pay for advertising and contribute toward overhead and profit.

This example applies an assumed 40% margin and the same order value to both platforms. It is not a measurement of the stores’ actual profitability.

This additional calculation shows what remains after the variable costs included in your margin calculation, extending the original AOV-minus-CPA comparison.

Also distinguish cost per purchase from new-customer acquisition cost. A campaign can generate purchases from existing customers. Unless the analysis separates first-time buyers, its CPA should not be presented as the cost of acquiring a new customer.

What Are the Average CPC, CTR, and Conversion Rates?

The reported CPC was $0.28 on Meta and $0.39 on Google non-brand. Conversion rates were 2.52% and 2.01%, respectively.

Lower click costs and higher conversion rates are consistent with Meta’s lower CPA in this dataset. However, campaign mix, audience, attribution, and measurement definitions can all affect the comparison.

Google’s reported CTR was 2.67%, compared with 2.48% on Meta. That small difference does not establish which platform brought more valuable visitors. Before comparing your own figures, check that the click definitions match, including whether Meta CTR measures link clicks or all clicks.

Use these metrics to diagnose a problem:

• Higher CPC: investigate auction competition, audience, placement, and creative relevance.
• Lower conversion rate: check the offer, landing page, sizing information, delivery terms, and checkout experience.
• Strong CTR but weak sales: investigate whether the ad’s promise matches the product page and whether the visitors fit the offer.

Change one major variable at a time so you can interpret the result.

How Should You Compare Google and Meta for Clothing Ads?

Keep Google brand and non-brand separate

Someone searching for your store’s name is in a different position from someone discovering your products for the first time. Including branded traffic can materially change a Google account’s blended results.

The Google figures in this article exclude brand campaigns. Compare them with your own non-brand results, and check which campaign types are included before drawing conclusions.

Match the Meta campaign scope

The Meta results are presented as an aggregate. They are not broken out here into prospecting, retargeting, or existing-customer activity.

That means the table should not be treated as a controlled comparison of new-customer acquisition on two platforms. When assessing your own budget, separate those customer groups where possible and compare campaigns with similar objectives.

Align attribution and revenue definitions

Check the attribution window, which interactions can receive credit, and whether refunds are reflected in revenue. Different settings can make two campaigns look further apart than they are.

Multiple platforms may also claim credit for the same order. Compare platform reports with your store’s total revenue, advertising spend, and contribution after advertising. A common reporting approach improves consistency, but attribution alone does not prove how many additional sales the advertising caused.

Allow for different order values

Your product mix can change what you can afford to pay for a purchase. Compare the AOV and contribution of the orders each campaign generates, rather than applying one storewide average everywhere.

AdScale’s separate analysis of apparel order value by basket size offers another way to examine that mix. Its UK versus US clothing order-value comparison also explores differences between markets. Those are separate analyses, so their figures should not be merged into this benchmark table.

How Do Returns Affect Clothing Advertising Profitability?

A clothing order can look profitable when it is placed and look very different after a refund, return shipment, or exchange.

Use your store’s actual return experience when setting advertising targets. Include the revenue you expect to retain and the costs you expect to incur, allowing for any inventory value recovered. A return rate by units is not automatically the same as a refund rate by revenue.

Where volume allows, review returns by product, campaign, and customer group. Two campaigns with the same reported ROAS can generate different contribution if one sells products that are returned more often.

Give recent orders enough time to pass through your normal return window before treating their profitability as settled.

How Should Clothing Brands Use These Benchmarks?

1. Pull a consistent comparison period. Review your last 90 days, with promotions and seasonal changes clearly marked. Separate Google brand and non-brand activity and, where possible, new and returning customers.
2. Calculate your own thresholds. Use contribution before advertising to set a break-even ROAS and a maximum CPA. Then leave room for overhead and your desired profit.
3. Find the largest meaningful gap. Start with an issue that affects contribution, such as expensive clicks, weak conversion, low-value orders, or high return costs. Avoid treating every difference from the benchmark as a problem.
4. Choose a specific test. If conversion is weak, test the product page or offer. If acquisition is too expensive, investigate creative and targeting. Consider a budget shift only after checking that the campaigns and customer groups are comparable.
5. Set limits before changing spend. Define the test budget, success measure, and review point. Allow for conversion delays and enough purchases to interpret the result. Historical average CPA does not tell you what the next increment of spend will cost.
6. Check the storewide outcome. Review total contribution after advertising, new-customer orders, and return-adjusted revenue alongside platform results. Scale a test when it improves the business outcome you set out to measure.

For more on connecting customer and order data with campaign decisions, see how AdScale approaches AI advertising for eCommerce.

Methodology and Data Sources

This analysis is based on aggregated performance data from over 100 million eCommerce orders processed via AdScale. Benchmarks reflect average results across clothing and apparel retailers using AdScale, with Google metrics focused specifically on non-brand campaigns.

Results are directional and intended for benchmarking purposes only. Actual performance may vary based on factors such as product mix, pricing, creative, audience targeting, seasonality, and budget allocation. Benchmarks reflect historical averages and are not predictive of future performance.

Frequently Asked Questions

Start With the Campaign You Can Explain

Choose one campaign, calculate the contribution from the orders it generates, and compare its performance with the relevant benchmark. Identify what is driving the gap before changing the budget.

The useful outcome is a clearer decision: improve the offer, fix the buying experience, test new creative, or increase spend where additional orders can support it.

A benchmark becomes useful when it helps you make a more profitable next decision.