Mobile app user trends for Ramadan 2026 | Adjust

Summary

Ramadan significantly influences mobile app usage as daily routines shift, presenting opportunities for marketers. A new report from Adjust, 'Ramadan app trends decoded: Benchmarks and insights for 2026,' reveals that average daily app installs in METAP increased 11% during Ramadan 2025 and rose a further 2% after Eid al-Fitr. Average daily session length across all verticals grew from 19.34 to 20.08 minutes during Ramadan, staying above 20 minutes post-Ramadan.

Gaming app installs surged 24% during Ramadan and 27% around Eid. E-commerce session growth was highest in Egypt (+132%), UAE (+101%), and Pakistan (+93%). The report helps marketers identify trends and plan campaigns effectively.

Analyst Note

This article arrives at a strategic inflection point for UA and monetization teams targeting the METAP region. With Ramadan 2026 beginning in March, the benchmarks from Adjust’s report—such as the 11% install lift and sustained session length increases during the 2025 Ramadan—offer a timely, data-driven baseline. The key implication is that historical trends are not merely directional; they reveal granular vertical and country-level shifts, like gaming installs surging 24% and e-commerce session growth in Egypt spiking 132%.

For ad ops professionals, this granularity enables more precise campaign optimization and budget allocation ahead of the peak window. What's notable here is the emphasis on post-Eid performance, suggesting that the engagement tail extends beyond Ramadan itself. The report also implicitly underscores the importance of reliable attribution in a mobile measurement landscape shaped by privacy changes, as Adjust’s first-party data provides a privacy-safe way to validate these trends.

Competitively, teams that leverage these benchmarks early can calibrate creative strategies and bidding models to capture the observed uplift, especially in under-penetrated categories like gaming. The article signals that successful Ramadan campaigns increasingly depend on real-world performance data rather than generalized assumptions.