App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

In 2023, mobile users in ********* spent over six hours using mobile apps on a daily basis, up by ** percent compared to 2022. ******** ranked second in the list of countries with the highest daily app usage in 2023, with **** hours daily spent using mobile apps. ******* and ********* ranked last with **** hours and **** hours spent by users daily on apps, respectively.

"Daily refreshed. MAID based app usage data Mobile attribution data including device model, carrier, user agent, os. Geolocation data like lat/long, city, zipcode, country.

Common use cases are: - MAID based lat/long data for accurate and daily geolocation use cases - Gaming app usage segments for targeting - Mobile app analytics

The graph shows a comparison for app downloads worldwide from 2018 to 2024, using data from Sensor Tower and data.ai. Global app downloads have plateaued in recent years, even declining, after seeing strong growth during the COVID-19 pandemic. For 2024, 136 billion unique downloads per user account were recorded. Why the difference? Source methodology explains the gap The discrepancy arises from significant differences in the methodology used by the sources to aggregate and generate the data. Sensor Tower reports only unique downloads per user account, excluding app updates, re-downloads, and installations on multiple devices by the same user. In contrast, data.ai includes these additional activities as well as downloads from third-party Android stores and a broader geographic scope, resulting in substantially higher total counts. As a result, Sensor Tower's numbers better reflect new user acquisition, while data.ai's encompass all market activity and total engagement. Despite stagnating downloads user spending is growing While the number of downloads is leveling off, consumer spending on in-app purchases and related revenue has grown in 2024 to 150 billion U.S. dollars, up from around 130 billion U.S. dollars in 2023. While gaming remains the highest grossing app category overall, other categories drove the growth. The entertainment, photo & video, productivity, and social networking categories each grew by at least one billion U.S. dollars in revenue in 2024 compared to the previous year.

Key US App Market StatisticsUS App Market SizeUS App Market Revenue by AppUS Smartphone UsersUS Smartphone PopulationTime Spent on Apps in the USUS App Market DownloadsUS Downloads by AppUS Daily...

Key Health App StatisticsTop Health AppsHealth & Fitness App Market LandscapeHealth App RevenueHealth Revenue by AppHealth App UsageHealth App Market ShareHealth App DownloadsKeeping track of...

The booming App Data Statistics Tool market is projected to reach $9.66 billion by 2033, growing at a CAGR of 18%. This report analyzes market size, trends, key players (like App Annie, Firebase, Mixpanel), segmentation (social, gaming, e-commerce apps), and regional growth. Discover insights to optimize your app strategy.

In a June 2021 survey of parents in the United States, 49 percent of respondents with children aged 10 to 12 years stated that they child had used social media apps in the past six months, whereas only a third of parents to 7 to 9 year olds stated the same.

This dataset captures detailed responses from a survey conducted to understand the mobile application usage patterns among various demographics. With 222 respondents, the data spans a range of topics including app usage hours, types of apps used, factors influencing app downloads, social media engagement, and the impact of design on app preference.

This dataset is ideal for analyzing:

Mobile app usage trends across different demographics. Factors influencing app download decisions. The relationship between app features and user satisfaction. Social media platform preferences and usage time. This data can be useful for app developers, marketers, and researchers interested in mobile app usage and trends.

Daily refreshed MAID based app activity data: - App name - App activity (install or post-install open) - session duration - IAP spend - Mobile attribution data including device model, carrier, user agent, os.

Great for the following use cases: - App analytics - Advertising targeting - fraud prevention - credit checking

Available in the following countries: AE – United Arab Emirates AU – Australia BR – Brazil CO – Colombia HK – Hong Kong TR – Türkiye (Turkey) ID – Indonesia IN – India JP – Japan KR – South Korea LK – Sri Lanka KSA – Saudi Arabia MX – Mexico MY – Malaysia NZ – New Zealand PH – Philippines SG – Singapore TH – Thailand TW – Taiwan VN – Vietnam

In 2024, more than 35 percent of the time that users worldwide spent on mobile devices was spent on social media apps. Entertainment apps represented the second most engaging category for mobile users, with a share of 32.7 percent of the total time spent on mobile apps. Utility and productivity apps were the third most engaging apps for global users, with around 14 percent of the total time spent on mobiles being spent on apps in this category.

Kindred SDK data contains two categories:

1. Location data: Daily refreshed MAID based lat/long data with six decimal points. Mobile attribution data including device model, carrier, user agent, os; timestamp.

Highly used for MAID based lat/long data for accurate and daily location-based targeting.

1. App Activity data: Daily refreshed MAID based app activity data:
2. App name
3. App activity (install or post-install open)
4. session duration
5. IAP spend
6. Mobile attribution data including device model, carrier, user agent, os.

Great for the following use cases: - App analytics - Advertising targeting - fraud prevention - credit checking

• Available countries are: United Arab Emirates Australia Brazil Colombia Hong Kong Türkiye (Turkey) Indonesia India Japan South Korea Sri Lanka Saudi Arabia Mexico Malaysia New Zealand Philippines Singapore Thailand Taiwan Vietnam

According to a survey on food delivery apps conducted by Rakuten Insight in April 2023, about ** percent of respondents from Hong Kong said they ordered more from food delivery apps during the coronavirus (COVID-19) pandemic. Another ** percent felt no impact of the pandemic on their food delivery app usage.

The file anonymized_app_data.csv contains a sample of smartphone app-fingerprints from 20,000 randomly selected individuals, collected in May 2016.Each record in the table corresponds to a (user, app) pair, and reveals that a given app was used at least once by a given user during May 2016. The table contains the following field:user_id : hashed user idapp_id: hashed id the smartphone app The data accompanies the publication: "Temporal and Cultural Limits of Privacy in Smartphone App Usage"

As of June 2023, convenience was a major driving force behind app usage for global Boomers. According to a survey of global app users, ** percent of users in the Boomer age group reported using their favorite apps because they were easy to use, while ** percent reported these apps simplified their lives. Having access to fresh and entertaining content was an important usage factor for ** percent of Gen Z users, while community, connection, and gamification was behind app usage for ** percent of users in the same demographic group.

This dataset encompasses mobile smartphone application (app) usage, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or surveying to understand the why. iOS and Android operating system coverage.

Tie app usage to web and location events using anonymized PanelistID for omnichannel consumer journey understanding.

Key Travel App StatisticsTop Travel AppsTravel App Market LandscapeTravel App RevenueTravel Revenue By AppTravel App UsersTravel App Market Share United StatesTravel App DownloadsThe online travel...

Context

This dataset provides insights into the daily mobile usage patterns of 1,000 users, covering aspects such as screen time, app usage, and user engagement across different app categories.

It includes a diverse range of users based on age, gender, and location.

The data focuses on total app usage, time spent on social media, productivity, and gaming apps, along with overall screen time.

This information is valuable for understanding behavioral trends and app usage preferences, making it useful for app developers, marketers, and UX researchers.

This dataset is useful for analyzing mobile engagement, app usage habits, and the impact of demographic factors on mobile behavior. It can help identify trends for marketing, app development, and user experience optimization.

Outcome

This dataset enables a deeper understanding of mobile user behavior and app engagement across different demographics.

Key outcomes include insights into app usage preferences, daily screen time habits, and the impact of age, gender, and location on mobile behavior.

This analysis can help identify patterns for improving user experience, tailoring marketing strategies, and optimizing app development for different user segments.

Data are presented as mean (SD).Shows usage data recorded by the Mums Step It Up app.

Next app prediction can help enhance user interface design, pre-loading of apps, and network optimizations. Prior work has explored this topic, utilizing multiple different approaches but challenges like the user cold-start problem, data sparsity, and privacy concerns related to contextual data like location histories, persist. The user cold-start problem occurs when a user has recently registered to the smartphone app system and there is not enough information about his/her preferences and his/her history of smartphone usage. In this work, we try to address the above issues. We introduce WhatsNextApp, an approach based on LSTM (Long Short-Term Memory) networks using sequences of app usage logs. Our approach is inspired by Word Embeddings and treats sequences of app usage logs as sequences of words. We collect a real-life data set consisting of 975 Android users with over 22 million app usage events. We build a generic (user-independent) WhatsNextApp model and the evaluation with our data set shows that it outperforms related studies for existing users where we achieve a recall@8 (recall for the top 8 apps) of 92%. For the user cold-start problem with the 500 most frequent apps, we achieve a recall@8 of 82.7%.