Compare ecommerce conversion cost benchmarks by industry, region, and campaign type.
October 2025 - September 2026
Benchmark observations based on the selected data
Cost-per-purchase moved from roughly $49 in July 2025 to a surprisingly low $19.7 by July 2026, tracing a year of pronounced swings and a late-stage collapse. This analysis is based on $3B worth of advertising data from our dataset, which provides strong directional benchmarks. This analysis explores ad performance trends for All industries available in All countries compared to the global benchmark.
Across the 13-month window the median cost-per-purchase averaged about $47.6. The series began at $49.18 (Jul 2025), peaked at $56.0 in March 2026, and troughed at $19.7 in July 2026. Monthly movement was material: average absolute monthly change ran roughly $4.8 (about 10% of the mean), underlining persistent volatility rather than a flat seasonal curve.
Key movements read like a momentum chart. After an early lift from $49.18 into the low $50s through Sep–Oct 2025, there was a dip to $46.39 in Nov, a modest rebound into winter ($49–$50 in Dec–Feb), then a pronounced lift to $55.98 in March. From that March high the metric moved lower through spring and early summer — $49.85 in April, $47.37 in May, $42.97 in June — before a dramatic decline to $19.69 in July 2026. The start-to-end change represents roughly a 60% decline.
Rhythm in the data shows a mid-winter plateau (Dec–Feb) followed by a spring spike and then progressive softening into summer. November showed a softer period relative to October, while March stood out as the single-month peak. The most extreme monthly swing occurred from June to July 2026, a decline of about 54% month-over-month that defines the series’ end-state.
These patterns suggest typical peaks and troughs across Q4–Q1–Q2, but with an unusually steep end-of-window move. Overall volatility was higher than a benign seasonal profile: the mean monthly absolute change (~$4.8) signals more jagged month-to-month behavior than steady seasonal rolls.
Because the selected series (All industries, All countries) is identical to the baseline, the country-level and global benchmarks are the same for this period. In other words, All countries’ cost-per-purchase mirrored the global benchmark exactly through the full series — average ≈ $47.6, high ≈ $56.0 (Mar 2026), low ≈ $19.7 (Jul 2026). The profile was characterized by short-lived lifts and sharper declines rather than a smooth climb.
Understanding cost-per-purchase benchmarks for All industries in All countries provides a clear picture of how aggregated Facebook Ads benchmarks, CPC trends, CPM analysis and broader CTR performance signals can translate into country-specific ad costs and industry ad performance comparisons at scale.
Facebook advertising cost benchmarks
Facebook advertising costs vary by industry, target audience, ad placement, and campaign objective. Ad costs vary across industries because of competition, audience demographics, and conversion value. Geographic targeting affects ad costs through regional competition and user engagement. Campaign objectives affect costs because Facebook optimizes delivery for different goals. The data shows median values across multiple campaigns. Results can vary with ad quality, audience targeting, and campaign optimization.
A small share of campaigns has extremely high CPP values. Those outliers can inflate an average. The median is the midpoint across campaigns, so it better represents a typical result.
The data shows industry median benchmarks. Costs can vary with targeting, creative quality, and campaign optimization.
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The dataset includes over $3B in Facebook ad spend from thousands of ad accounts that use Superads to analyze and improve campaigns. Every data point is anonymized and aggregated. It does not expose an individual advertiser.
The dataset updates as new ad data is available.
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. A higher CPA can work when the margin supports it. Measure CPA 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 advertisers stay within a target CPA. It suits experienced advertisers who can monitor performance and adjust regularly. It provides more control and 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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