If you’ve ever tried to justify an app budget (or defend a roadmap), you’ve probably done what most teams do: grab a few big mobile app stats and drop them into a deck.
The problem is that many app statistics posts recycle numbers, mix definitions, and skip the part that matters: what to do with the data. That’s how teams celebrate downloads while retention quietly collapses.
This guide is built to out-rank and out-cite: you’ll get a tight set of high-confidence stats (with clear scope), plus a practical system for turning those numbers into decisions.
Mobile app download statistics measure how many installs occur from app stores within a defined period. Mobile app usage statistics describe what users do after install, sessions, time spent, and retention. Downloads indicate acquisition volume; usage reveals product value and habit. Always cite the source, year, geography, store coverage, and metric definition because providers measure downloads, spending, and retention differently.
- 2024 total downloads: ~110B installs across iOS + Google Play.
- 2024 store split: 28.3B iOS vs 81.4B Google Play downloads.
- 2024 consumer spending: $127B across App Store + Google Play (reported via Appfigures).
- 2024 in-app purchase revenue: $150B across iOS + Google Play (+13% YoY).
- Google Play listings shift: reported ~47% decline from ~3.4M → 1.8M apps (start of 2024 → April 2025).
Best practice: use downloads to measure reach; use retention + sessions + revenue per active user to measure success.
Sources used in this guide: TechCrunch (Appfigures), Sensor Tower, Adjust, data.ai. (Full list in Resources & Data Sources.)
Cite-Safe Mobile App Stats (2026)
These are high-signal, widely cited numbers you can reference without getting burned, as long as you keep the scope.
Metric | Value | Year | Geography | Store scope | Source |
Total app downloads | ~110B | 2024 | Global | iOS + Google Play | TechCrunch (Appfigures) |
iOS downloads | 28.3B | 2024 | Global | iOS App Store | TechCrunch (Appfigures) |
Google Play downloads | 81.4B | 2024 | Global | Google Play | TechCrunch (Appfigures) |
Consumer spending | $127B | 2024 | Global | App Store + Google Play | TechCrunch (Appfigures) |
In-app purchase revenue | $150B | 2024 | Global | iOS + Google Play | Sensor Tower |
IAP revenue growth | +13% YoY | 2024 | Global | iOS + Google Play | Sensor Tower |
Google Play app listings change | -47% | 2024-Apr 2025 | Global | Google Play listings | TechCrunch (Appfigures) |
Play Store apps (approx.) | 3.4M -> 1.8M | 2024-Apr 2025 | Global | Google Play listings | TechCrunch (Appfigures) |
Downloads vs installs vs usage
Teams get burned when they treat these as interchangeable:
- Download / install (store): an install event recorded by an app marketplace within a timeframe.
- First-time install: excludes reinstalls (depends on provider).
- Active users (DAU/MAU): users who complete a qualifying event in a time window.
- Retention (D1/D7/D30): % of users returning on Day 1/7/30 after install.
- Session: a continuous period of app activity, but session is defined differently across analytics vendors, which is why session counts can disagree across tools. Data.ai documents session measurement details and edge cases (e.g., when sessions end).
Google Play vs App Store: what the split means for strategy
The 2024 split is a useful reminder:
- Google Play tends to dominate download volume (81.4B vs 28.3B iOS in 2024).
- Monetization remains strong across stores, with 2024 marking a major IAP milestone.
What this means for planning
- If you need reach, Android distribution + localization often compounds faster.
- If you need premium monetization, iOS paywall testing, pricing, and packaging often matter earlier.
If you’re actively building or improving an app, align this with your delivery plan:
Explore mobile app development services to match platform strategy with product scope. Support launch visibility with app store optimization.
Turn installs into retention and revenue with a Digixvalley audit today
We identify your bottleneck metrics and give a clear action plan.
More Benchmarks Businesses Actually use (2024–2025)
This is where stats become operational.
Retention benchmarks
Retention is where most apps win or lose, yet it’s also where teams misuse benchmarks.
Adjust publishes global benchmark reporting that helps teams compare D1/D7/D30 retention directionally (with category and cohort caveats).
How to use benchmarks without fooling yourself
- Compare cohorts: paid vs organic, new vs returning, country A vs country B.
- Compare like-for-like periods: same seasonality, same campaigns, same release cadence.
- Use benchmarks to identify where to investigate, not to declare we’re fine.
If you want this set up cleanly, start with mobile app analytics setup so your dashboards reflect real cohorts,not blended noise.
How Downloads and Usage differ by Region, Category, and Audience
App stats are not one global truth. They’re a set of patterns that shift by context.
Region patterns (what changes in strategy)
- Emerging markets often create huge download volume, but monetization and device/network constraints can change engagement patterns.
- Mature markets may show slower download growth but stronger monetization per active user.
What to do with this: don’t only optimize for global totals. Segment by the markets you actually operate in, and cite scope when you present numbers.
Category patterns
- Shopping apps often win on lifecycle triggers (price drops, delivery status, re-order loops).
- Finance apps often win on trust, stability, and recurring check-in behaviors.
- Games apps often win on session frequency and content cadence.
What to do with this: pick the KPI stack that matches your category (activation and D7 mean very different things across categories).
B2B vs B2C patterns
- B2C often needs habit + retention loops.
- B2B often needs job done fast + high reliability (usage may be fewer sessions but high value per session).
Monetization Models that Change How you Interpret Usage
Usage is up can be great, or meaningless, depending on how you monetize.
Subscription apps
Track:
- paywall views → trial starts → trial-to-paid conversion
- renewals and cancel reasons
- revenue per active user (not just MAU growth)
IAP / transaction apps
Track:
- conversion to first purchase
- repeat purchase rate
- revenue per payer and payer rate
revenue per payer and payer rate
Ad-supported apps
Track:
- ad impressions per active user (balanced with user experience)
- retention impact of ad load
- session quality (short sessions can still monetize; long sessions can be unprofitable)
The business headline: global IAP revenue hit $150B in 2024, reinforcing that monetization remains a major strategic lever, not an afterthought.
Source map: Where to Get Each Type of Stat
Here’s the simplest rule: use one source per metric type and cite scope every time.
You need… | Use sources like… | Why it’s safer | Always include |
Downloads + store split | Store install reporting summaries | Scope is explicit | Year + stores + geography |
Spending / IAP revenue | Market intelligence reports | Consistent yearly reporting | Year + what “spend” includes |
Store listing count changes | Reporting tied to policy shifts | Explains why it moved | Date range + policy context |
Retention benchmarks | Benchmark reports | Category framing | Category + cohort notes |
Sessions definition | Vendor methodology docs | Removes ambiguity | Tool name + definition |
Checklist + Decision Tree: Turn Stats into Decisions
Weekly Mobile Metrics Review
- Confirm timeframe + geography + store scope for any stat you quote
- Check downloads by channel (organic vs paid)
- Check activation rate (first meaningful action)
- Check D1/D7 retention trend
- Check sessions/user/day trend
- Check revenue per active user trend
- Identify one bottleneck metric
- Pick one experiment to run
- Define success threshold
- Document results + what you learned
What changed | Most likely cause | What to check first | 2–3 actions |
Downloads ↑, usage ↔ | Low-intent traffic, onboarding friction | Store query intent, time-to-value | tighten store messaging; shorten onboarding; improve first-session guidance |
Usage ↑, revenue ↓ | Value mismatch, paywall timing | segment new vs returning; payer rate | move paywall later/earlier test; refine pricing/packaging; improve upsell triggers |
D7 retention ↓ | Weak value loop Days 2–7 | feature adoption, notification relevance | add Day 3–7 reinforcement; personalize nudges; improve “aha moment” repeatability |
Downloads ↓, retention ↑ | Fewer users but higher quality | channel mix shifts | double down on best cohorts; refine ASO; improve referral loops |
Sessions ↑, ratings ↓ | Engagement at cost of UX | reviews + crash/latency + ad load | fix friction/crashes; reduce spammy prompts; rebalance notifications/ads |
If onboarding is your bottleneck, invest in onboarding UX improvements
it’s one of the fastest ways to convert installs into habit.
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Mistakes to Avoid when Quoting Mobile App Statistics
- Mixing definitions (downloads vs active users vs first-time installs)
- Skipping store scope (iOS + Google Play ≠ “all Android stores”)
- Dropping geography (US numbers presented as “global”)
- Citing a number without the year (stats go stale fast)
- Using downloads as success (retention and monetization decide success)
- Ignoring ecosystem changes (e.g., listing count shifts tied to policy changes)
Key Takeaways
Pros & cons of using download stats as a success proxy
Pros
- Great for market sizing and acquisition trend tracking
- Useful early signal when expanding to new regions
Cons
- Doesn’t measure product value or habit
- Vulnerable to low-intent traffic and measurement differences
Best-for blocks
- Best for exec decks: pair downloads + monetization (reach + willingness to pay).
- Best for growth teams: lead with D7 retention + one experiment you’ll run this sprint.
At Digixvalley, we help product and growth teams turn numbers into outcomes, especially in the messy middle between we got installs and we built a habit. If you want examples of how teams translate metrics into UX, lifecycle, and monetization changes, browse case studies.
And once you ship improvements, don’t let analytics drift: keep performance stable with mobile app maintenance
so releases don’t quietly break funnels. Easy to misquote without scope/definitions
Optimize Your Digital Strategy with Digixvalley
In a mobile-first world, staying ahead takes more than launching an app and watching downloads climb. The teams that win in 2026 treat mobile as a living system: acquisition quality, onboarding speed, retention loops, and monetization all working together, measured with definitions you can trust and benchmarks that actually match your category and market.
That’s where Digixvalley comes in. We help businesses turn mobile data into decisions, so you’re not just reporting installs, you’re improving the funnel behind them. From clarifying your KPI stack and analytics setup to redesigning onboarding that lifts activation, we connect strategy, product, UX, and growth into one measurable roadmap.
Whether you’re building from scratch or upgrading an existing app, our team can support you across the full lifecycle:
- Build and scale with mobile app development services
- Improve discoverability with app store optimization
- Fix measurement and cohorts with mobile app analytics setup
- Increase activation with onboarding UX improvements
- Protect funnels post-release with mobile app maintenance
Want proof and examples? Browse our case studies
to see how teams translate metrics into better retention, stronger engagement, and sustainable revenue.
Build, optimize, and scale your mobile app growth with Digixvalley
FAQs:
How many apps were downloaded globally in 2024?
About 110 billion downloads across iOS App Store + Google Play in 2024, based on Appfigures reporting cited by TechCrunch. (Scope: iOS + Google Play only.)
Which store had more downloads in 2024: Google Play or the App Store?
Google Play had more downloads: about 81.4B on Google Play vs 28.3B on iOS in 2024 (Appfigures via TechCrunch).
Are downloads the same as installs?
In most app-store reporting, downloads generally refer to installs, but some providers distinguish first-time installs vs re-installs, so always cite the provider’s definition.
Do more downloads mean an app is successful?
Not necessarily. Downloads measure acquisition volume; success depends on activation, retention (D1/D7/D30), engagement, and monetization.
What’s the difference between app downloads and app usage statistics?
Downloads are installs; usage describes behavior after install—sessions, time spent, and retention.
How much money do users spend in apps globally?
Two common global frames are: consumer spending via app stores and in-app purchase (IAP) revenue. In 2024, $127B consumer spending was reported via Appfigures/TechCrunch, and $150B IAP revenue was reported by Sensor Tower.
What’s the difference between consumer spending and in-app purchase revenue?
Consumer spending is typically broader (may include paid apps, subscriptions, and IAP depending on the report). IAP revenue focuses on in-app purchases and often uses a specific methodology, always cite what’s included.
Why do different sources report different app download totals?
Totals differ due to store coverage (iOS+Google Play vs additional Android stores), geography, and definitions (downloads vs first-time installs vs reinstalls).
Why did Google Play’s total app listings drop in 2024–2025?
TechCrunch reported about a 47% decline in Google Play listings from early 2024 to April 2025, citing Appfigures, tied to stronger quality and policy enforcement.
What is a session in mobile analytics?
A session is a period of app activity, but rules vary by analytics tool (e.g., when sessions start/end), which is why two platforms can report different session counts.
What’s the most important usage metric for business decisions?
For most apps, D7 retention is one of the most decision-driving early metrics because it reflects repeat value, not just first-time curiosity.
12) What’s a reasonable retention benchmark?
It depends on category, cohort, country, and channel. Use benchmark reports (like Adjust) directionally and compare only against similar cohorts (paid vs organic, same geo, same category).
What should businesses track after downloads?
A practical post-download stack is activation rate → D1/D7/D30 retention → sessions per user/day → revenue per active user.
What’s the most common mistake when presenting app stats to leadership?
Mixing definitions (downloads vs active users) and omitting scope (year, geography, stores covered) is the fastest way to lose credibility.
How should I cite mobile app statistics correctly?
Use a consistent format: Metric + value + year + geography + store coverage + provider + definition note (especially for sessions, spending, and installs).
