See how your purchase costs compare. Explore ecommerce conversion cost benchmarks by industry, region, and campaign type
October 2024 - October 2025
Detailed observation of presented data
This analysis looks at cost per purchase trends for industry Textiles and target country Denmark compared to the global trend. The analysis is based on $3B worth of advertising data from our dataset, which provides strong directional benchmarks. We review monthly medians from October 2024 through August 2025 to benchmark Facebook Ads costs and contextualize seasonality, volatility, and level versus the global baseline.
Seasonality is evident:
Across the period, Textiles in Denmark remained above average versus the global baseline, with significant seasonal surges in Q4/early January and pronounced summer softness. Compared to the global trend, Denmark showed higher peaks, deeper troughs, and far greater month-to-month variability.
Understanding cost per purchase benchmarks on Facebook Ads in industry Textiles and Denmark helps advertisers make more efficient budget and creative choices.
Insights & analysis of Facebook advertising costs
Facebook advertising costs vary based on many factors including industry, target audience, ad placement, and campaign objectives. In the Textiles industry, Facebook ad costs can be influenced by seasonal trends and market competition. For campaigns targeting Denmark, advertisers should consider local market factors and user behavior. Different campaign objectives lead to varying costs based on how Facebook optimizes for your specific goals. The data shown represents median values across multiple campaigns, and individual results may vary based on ad quality, audience targeting, and campaign optimization.
We use the median CTR because the underlying distribution of click-through rates is highly skewed, with a small share of campaigns achieving extremely high CTRs. These outliers can inflate a simple average, making it less representative of what most advertisers actually experience. By using the median—which sits at the midpoint of all campaigns—we provide a more rigorous and realistic benchmark that reflects the true underlying data model and helps you set attainable performance expectations.
Note: This data represents industry median values and benchmarks. Your actual costs may vary based on specific targeting, ad creative quality, and campaign optimization.
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All data is sourced from over $3B in Facebook ad spend, collected across thousands of ad accounts that use Superads daily to analyze and improve their campaigns. Every data point is fully anonymized and aggregated—no individual advertiser is ever exposed.
This dataset updates frequently as new ad data flows in. It will only get bigger and better.
Christmas & Boxing Day (late Dec), Easter holidays (groceries, travel, tourism), Mother's Day and Valentine's Day
CPM and CPC could rise during Easter period due to travel-related campaigns. Late December ad competition might intensify in retail and hospitality. Whit Weekend might reduce weekday competition. Strict retail closures on holidays could drop competition, but pre-holiday CPMs may escalate.
It depends on your product price and margins. Most brands aim for $10 to $50. For higher-ticket products, a higher CPA may be acceptable as long as you're maintaining a strong return on ad spend.
Higher-priced products typically have a higher CPA because people take longer to convert. That's not necessarily a problem if your margin can support it. You should measure CPA in context with AOV and LTV.
Your AOV may be increasing, which helps maintain ROAS even if CPA rises. You could also be facing higher CPMs, lower conversion rates, or creative fatigue.
Manual bidding can help if you're struggling to stay within target CPA. It's best used by experienced advertisers who can monitor performance and adjust regularly. It gives more control, but also requires more effort.
Increase budget gradually, rotate creative often, and avoid overlapping audiences. Scaling too quickly can lead to audience saturation and rising CPAs.
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Average cost per purchase benchmarks across industries
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