Sensor Tower's Churn Analysis helps mobile app developers and publishers understand user loyalty by breaking down monthly active users into new, retained, and resurrected users. Churn rates vary by category: social media apps like Instagram have low single-digit churn, while retail apps like Etsy average 20-40% churn. BNPL apps like Affirm and Klarna have high churn.
The tool highlights that high new user growth may signal retention issues if MAU growth is slow. Retained users are crucial for revenue and advertiser value, with Facebook and Instagram retaining over 94% of users. Resurrected users, re-engaging after inactivity, are important for health and monetization, as seen in grocery delivery apps like Instacart.
This analysis enables data-driven decisions to combat churn and drive growth.
Subscription apps like Netflix and Spotify charge recurring fees for premium features. They offer predictable revenue, higher user loyalty, and better App Store rankings. Key models include flat-rate, tiered, and per-user pricing. Success requires value-driven pricing, free trials, and transparent practices.
Sensor Tower's Power User data measures days used per month, revealing app stickiness. The metric shows crypto apps' declining engagement, Netflix's SVOD loyalty, Duolingo's growing stickiness, and Instagram's daily dominance, offering insights into user behavior and monetization.
Retargeting spend is rising—AppsFlyer reports $31B and 29% of app marketing spend in 2025, up from 25%—because UA costs are inflated and retention now drives LTV. For ad ops decision-makers, success requires segmenting users into active, dormant, and lapsed cohorts and tailoring campaigns rather than using generic messaging. Active users respond to offers and release notes; dormant users need incentives to return; lapsed users require balanced ads and incentives, with disengagement timing identified. A free-to-play example shows refining LTV-based targets can improve long-term ROI. Treat retention as a core LTV lever.
Sensor Tower data aligns with Uber's 1Q23 earnings, showing 14% MAU growth, increased engagement, and all-time highs for Uber Driver app in international markets. Lyft's flat driver growth highlights its lack of global reach.
Japan's finance app category is outperforming, with H1 2026 installs up 8% YoY and sessions up 40%—nearly 5x the install growth and well ahead of global figures (5% and 29%). Stock trading is the standout subvertical: installs +90% YoY and average session length climbing to 11.7 minutes, driven by NISA reforms and a retail investor resurgence. Retention is improving across the full 30-day curve (D1 17% to 19%, D30 7% to 8%). For ad ops teams, the takeaway is clear: prioritize measurement depth, re-engagement, and channel-level quality over top-of-funnel volume alone.
Brand strategy is a top priority as marketers shift from performance to brand advertising due to privacy and economic challenges. Social media has limitations, but programmatic app-to-web offers broader reach, brand safety, and privacy-friendly solutions.
India’s app market is shifting from download scale to engagement and monetization. H1 2026 downloads rose only 3%, but IAP grew 34%. Perplexity’s Airtel partnership drove a 625% download spike and retained 14M MAUs, proving distribution can yield sustained usage. Faith apps now reach ~80M MAUs and show strong overlap with finance apps (Melooha users 66.7x more likely to use Wint Wealth), revealing non-obvious audience segments. Short Drama is pivoting to retention/regional competition. For ad ops, prioritize first-party/partner audience data, cross-category affinity targeting, retention metrics, and seasonal/geo/channel planning over pure reach.
Adjust's Device Usage dashboard in Datascape gives ad ops teams a prebuilt view of how active users are distributed across app versions, OS versions, SDK versions, and iOS ATT status, plus dimensions like device type, country, language, and date. The core insight is that release impact can lag because users update over days or weeks; segmenting DAU by version explains flat or shifting metrics. Teams can track SDK and app-version adoption, compare OS behavior, identify stale versions, and add device/country context to performance analysis. Actionable takeaway: use the dashboard before and after releases or SDK rollouts to validate adoption, isolate version-specific issues, and contextualize attribution and engagement trends.
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