Pixalate’s Q2 2025 Benchmarks for Made-for-Advertising (MFA) Mobile Apps explores advertising trends on likely MFA apps, offering insight into the impact of MFAs across the Google Play Store and Apple App Store. Gain a detailed view of ad spend estimates, common app registration countries, the most affected app categories and genres, and more.
For this report, Pixalate’s data science team analyzed 255K+ mobile apps across the Google Play Store and Apple App Store, including 27+ billion open programmatic ad impressions globally in Q2 2025 (June). Non-trend data points reflect measured MFA activity at the end of Q2 2025. Quarterly trend analysis is based on data from the final month of each quarter (June for Q2, March for Q1) and is used as a representative snapshot of end-of-quarter trends.
Programmatic ad transactions serve as proxies for advertising Share of Voice (SOV) and ad spend. Pixalate’s datasets, exclusively used to generate these insights, primarily consist of buy-side open auction programmatic traffic sources.
Pixalate determines MFA designations using observed traffic from its global data pool, rather than relying solely on crawlers, which can be easily manipulated.
Pixalate analyzes these traffic signals and flags apps as likely MFA when any of the factors are extreme outliers, determined through quantile analysis of all ad impressions per app.
Pixalate further classifies MFA apps as “medium” or “high” risk, based on the severity and number of measured MFA signals.
Pixalate flags apps as likely MFA on a monthly basis, meaning changes in advertising patterns may cause an app’s MFA classification to fluctuate from month to month.
Pixalate’s methodologies undergo continuous review and enhancement. For more information, visit Pixalate’s MFA knowledge base.
Pixalate calculates global open programmatic ad spend by combining externally sourced data with its internally tracked metrics. For this report, third-party data is used to estimate the total annual spend for the previous year. Pixalate then applies its derived quarterly share to determine quarterly values, and proprietary estimates are used to construct QoQ time series for 2024 and 2025.
As used herein, and per the MRC, “'Invalid Traffic' (IVT) is defined generally as traffic that does not meet certain ad serving quality or completeness criteria, or otherwise does not represent legitimate ad traffic that should be included in measurement counts. Among the reasons why ad traffic may be deemed invalid is it is a result of non-human traffic (spiders, bots, etc.), or activity designed to produce fraudulent traffic.”
Pixalate is a global platform for privacy compliance, ad fraud prevention, and data intelligence in the digital ad supply chain. Founded in 2012, Pixalate’s platform is trusted by regulators, data researchers, advertisers, publishers, ad tech platforms, and financial analysts across the Connected TV (CTV), mobile app, and website ecosystems. Pixalate is MRC-accredited for the detection and filtration of Sophisticated Invalid Traffic (SIVT).
Disclaimer: The content of this page reflects Pixalate’s opinions with respect to the factors that Pixalate believes can be useful to the digital media industry. Any proprietary data shared is grounded in Pixalate’s proprietary technology and analytics, which Pixalate is continuously evaluating and updating. Any references to outside sources should not be construed as endorsements. Pixalate’s opinions are just that - opinion, not facts or guarantees.
Per the MRC, “'Fraud' is not intended to represent fraud as defined in various laws, statutes and ordinances or as conventionally used in U.S. Court or other legal proceedings, but rather a custom definition strictly for advertising measurement purposes. Also per the MRC, “‘Invalid Traffic’ is defined generally as traffic that does not meet certain ad serving quality or completeness criteria, or otherwise does not represent legitimate ad traffic that should be included in measurement counts. Among the reasons why ad traffic may be deemed invalid is it is a result of non-human traffic (spiders, bots, etc.), or activity designed to produce fraudulent traffic.”
