Facebook Ads Insights Tool

Facebook Ads Cost Per App Install Benchmarks

Compare mobile acquisition cost benchmarks by industry, region, and platform.

Cost Per App Install

October 2025 - September 2026

Insights

Benchmark observations based on the selected data

Introduction

The clearest story here is volatility: global cost-per-app-install sat low through much of the year, then flashed two sharp surges — one in February 2026 and an extreme spike in July 2026. 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 in All countries compared to the global benchmark.

The story in the data

Starting in July 2025, the median cost-per-app-install was 9.95 and it closed the 13‑month window at 96.91 — an eye-catching rise of roughly +875%. Across the period the mean cost was about 21.8 and the median was ~14.9, reflecting a distribution skewed by outsized peaks. The lowest monthly median was 9.36 (December 2025); the highest was 96.91 (July 2026). February 2026 showed a notable interim high of 30.13, about 135% higher than January’s 12.83.

Volatility is substantial: population standard deviation is ~22.2, producing a coefficient of variation just over 100% — meaning month-to-month swings are large relative to the average. Looking at sequential changes, the average absolute month-to-month percent swing is roughly 77% when the July 2026 outlier is included; remove that single extreme month and the average absolute swing falls to about 33%.

Seasonal and monthly dynamics

The time series reads like a low‑baseline rhythm punctuated by two spikes. From July 2025 through January 2026 the series climbed gradually from ~10 to ~13, then jumped in February to ~30 before reverting toward the mid‑teens in March–June. Typical mid‑year medians sat between about 9 and 19, with December showing the period low. Late Q2 into July 2026 produced the dramatic departure from prior seasonality: a one‑month surge to 96.9 that dwarfs prior movement and dominates annual variance.

This pattern creates a cadence of steady baseline months interrupted by episodic lifts — February’s surge and July’s outlier — rather than a smooth seasonal cycle such as a steady Q4 peak or a consistent Q1 trough.

Country vs. Global

Because selected and baseline datasets here are identical (All industries in All countries), the market narrative is the global benchmark itself: the series is below, near, or above any short-term averages depending entirely on month. Relative framing shows most months (9 of 13) sat below the mean and median until February’s lift and July’s extreme reversal. In plain terms, the “global” cost-per-app-install for All industries in All countries was more volatile than many other ad metrics typically are (CPC trends or CPM analysis), with large month-to-month exposure to spikes that inflate averages compared with medians used in industry ad performance comparisons. For those tracking country-specific ad costs or CTR performance in parallel channels, these spikes will register as outliers relative to the ordinary mid‑teens range.

Closing

Understanding cost-per-app-install benchmarks for All industries in All countries gives a clear view of a baseline that is usually mid‑teens but can be driven far higher by episodic surges — a key signal when comparing Facebook Ads benchmarks, CPC trends, CPM analysis, CTR performance, country-specific ad costs, and broader industry ad performance.

About this data

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.

Why we use median instead of average

A small share of campaigns has extremely high CPI values. Those outliers can inflate an average. The median is the midpoint across campaigns, so it better represents a typical result.

Factors that affect Facebook Ad Costs

  • Competition within your selected industry and audience demographics
  • Ad quality and relevance score. Higher quality ads can lower costs.
  • Campaign objective and bid strategy
  • Timing and seasonality. Costs often increase during holiday periods.
  • Ad placement (News Feed, Instagram, Audience Network, etc.)

The data shows industry median benchmarks. Costs can vary with targeting, creative quality, and campaign optimization.

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The data behind the benchmarks

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.

What's a good CPI for iOS vs Android in 2026?

iOS CPIs often range from $2 to $5 or more. Android is usually cheaper, between $1 and $3. Your CPI will depend on geo, creative, and optimization goal.

Why is my app install cost higher in some countries?

Some regions like the US, UK, and Canada have higher competition and stricter privacy regulations, which drive up costs. Countries with lower purchasing power typically have cheaper CPIs.

What creatives drive the lowest CPI on Facebook?

Short videos that show app benefits, UGC-style content, and localized messaging tend to perform best. Clear CTAs and fast-paced visuals can lower CPI.

Should I optimize for installs or in-app actions?

Optimizing for installs increases volume. Optimizing for actions such as signups or purchases brings higher-quality users. Choose based on your goals and the importance of post-install behavior.

How do I lower CPI without tanking app retention or quality?

Align creative with the app experience, avoid misleading ads, and exclude people who already installed. Test lookalike audiences based on high-quality users rather than all installers.