Compare ecommerce conversion cost benchmarks by industry, region, and campaign type.
October 2025 - September 2026
Benchmark observations based on the selected data
Retail cost-per-purchase in this dataset tells a clear, measurable story: across “All countries available” retail advertisers ran below the global benchmark for most of the 13-month window, with a mid‑season lift into spring 2026 followed by a steep summer decline. 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 Retail in All countries available compared to the global benchmark.
Retail cost-per-purchase (CPP) averaged about $35.7 over the period (July 2025 → July 2026), starting at $35.61 in July 2025 and finishing at $26.79 in July 2026 — a net decline of roughly 25%. The monthly high for Retail was $44.32 in April 2026; the low was $26.79 in July 2026. By contrast the global baseline averaged roughly $47.6, with a high near $56.0 (March 2026) and an anomalous low of $19.69 in July 2026.
Key monthly moves for Retail include modest gains through late 2025 (peak near $39 in September/October), a sharp dip into December 2025 ($27.21), a rebound into Q1 2026 reaching $41.28 in March and $44.32 in April, and then a pronounced slide into summer — a nearly 40% fall from the April high to the July low. Month‑to‑month absolute changes averaged about $4.1 (≈12% of the Retail mean), signaling notable month-level swings rather than a flat trend.
There’s a familiar seasonal rhythm: autumn steadiness into early Q4, a softer December trough, and a strong rebound across Q1 into spring. Retail CPP rose into March–April 2026 before trending down through May and June into a July trough. December softness and a spring rebound are consistent with competitive and consumer-behavior cycles across retail. The large drop in July 2026 for the global baseline creates a distinct end‑point dynamic: while Retail continued downward, the baseline’s fall was sharper, inverting the usual gap.
Across the window Retail (All countries available) ran below the global benchmark by roughly 25% on average. Early in the period the gap was steady — Retail ~28% lower in July 2025 — but the relationship shifted: Retail’s peak in April (≈$44.3) narrowed the gap to about 11% below global that month, and by July 2026 the baseline’s collapse to $19.7 made Retail about 36% above the global number for the first time in the series. Volatility comparisons show Retail had average monthly absolute moves of ~$4.1 (≈11.6% of its mean), versus the baseline’s ~$4.8 (≈10.1% of its mean), meaning Retail experienced slightly larger relative swings month to month.
Understanding cost-per-purchase movement in Retail against the global baseline provides a clear signal of seasonal pressure and mid‑year momentum shifts. This summary frames Facebook Ads benchmarks, CPC trends, CPM analysis, CTR performance context, and country-specific ad costs in an industry ad performance lens for Retail across All countries available.
Facebook advertising cost benchmarks
Facebook advertising costs vary by industry, target audience, ad placement, and campaign objective. In the Retail industry, Facebook ad costs can be influenced by seasonal trends and market competition. 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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