This dataset simulates anonymized mobile screen time and app usage data collected from Android/iOS users over a 3-month period (Jan–April 2024). It captures daily usage trends across various app categories including:

Productivity: Google Docs, Notion, Slack

Entertainment: YouTube, Netflix, TikTok

Social Media: Instagram, WhatsApp, Facebook

Utilities: Chrome, Gmail, Maps

For YouTube, additional engagement statistics such as views, likes, and comments are included to analyze video popularity and content consumption behavior.

The dataset enables exploration of:

Productivity vs. entertainment screen time patterns

Daily usage fluctuations

App-specific user engagement

Correlation between time spent and user interactions

YouTube content virality metrics

This is a great resource for:

EDA projects

Behavioral clustering

Dashboard development

Time series and anomaly detection

Building recommendation or focus-assistive apps

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.

This dataset provides comprehensive insights into mobile app usage patterns across different categories, including education, social media, productivity, entertainment, health, news, and shopping applications. It contains screen time data for 500 users with demographic information such as age and gender, making it valuable for analyzing digital behavior patterns and productivity correlations.

From Harvard Dataverse

Description: We surveyed 10,208 people from more than 15 countries on their mobile app usage behavior. The countries include USA, China, Japan, Germany, France, Brazil, UK, Italy, Russia, India, Canada, Spain, Australia, Mexico, and South Korea. We asked respondents about: (1) their mobile app user behavior in terms of mobile app usage, including the app stores they use, what triggers them to look for apps, why they download apps, why they abandon apps, and the types of apps they download. (2) their demographics including gender, age, marital status, nationality, country of residence, first language, ethnicity, education level, occupation, and household income (3) their personality using the Big-Five personality traits This dataset contains the results of the survey.

Author: Lim, Soo Ling, 2014, "Worldwide Mobile App User Behavior Dataset", https://doi.org/10.7910/DVN/27459, Harvard Dataverse, V1

Author filliation: University College London

SCREEN TIME AND APP USAGE SURVEY

A Screen Time and App Usage Survey dataset typically includes information on how users interact with their mobile devices, particularly focusing on the amount of time spent on different activities or applications. Key elements captured in such datasets include:

**Screen Time Duration: **The total time a user spends using their device, often broken down by daily, weekly, or monthly intervals.
**App Usage Statistics: **Data on specific apps used, including the duration and frequency of use.
**User Demographics: **Information such as age, gender, occupation, and device type, helping to analyze trends in different population segments.
**Time of Day:** The periods during which users are most active on their devices, revealing peak usage hours.
Categories of Apps: Classification of apps (e.g., social media, productivity, entertainment) to understand how different app types contribute to total screen time.

This dataset helps in understanding behavioral patterns, dependencies, and potential impacts of excessive screen time on health and productivity.

This dataset provides daily, aggregated mobile app usage statistics, including launch counts, session lengths, and retention rates, segmented by platform, country, and device type. It enables detailed analysis of user engagement, retention, and growth trends across different mobile applications and markets, supporting strategic decisions for app development and marketing.

This dataset has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

The User Profile Data is a structured, anonymized dataset designed to help organizations understand who their users are, what devices they use, and where they are located. Each record provides privacy-compliant linkages between user IDs, demographic profiles, device intelligence, and geolocation data, offering deep context for analytics, segmentation, and personalization.

Built for privacy-safe analytics, the dataset uses hashed identifiers like phone number and email and standardized formats, making it easy to integrate into big-data platforms, AI pipelines, and machine learning models for advanced analytics.

Demographic insights include gender, age, and age group, essential for audience profiling, marketing optimization, and consumer intelligence. All gender data is user-declared and AI-verified through image-based avatar validation, ensuring data accuracy and authenticity.

The dataset’s Device Intelligence Layer includes rich technical attributes such as device brand, model, OS version, user agent, RAM, language, and timezone, enabling technical segmentation, performance analytics, and targeted ad delivery across diverse device ecosystems.

On the location and POI front, the dataset combines GPS-based and IP-based coordinates—including country, region, city, latitude, longitude —to provide high-precision geospatial insights. This enables mobility pattern analysis, market expansion planning, and POI clustering for advanced location intelligence.

Each user record contains onboarding and lifecycle fields like unique IDs, and profile update timestamps, allowing accurate tracking of user acquisition trends, data freshness, and activity duration.

🔍 Key Features • 1st-party, consent-based demographic & device data • AI-verified gender insights via avatar recognition • OS-level app data with 120+ daily sessions per user • Global coverage across APAC and emerging markets • GPS + IP-based geolocation & POI intelligence • Privacy-compliant, hashed identifiers for safe integration

🚀 Use Cases • Audience segmentation & lookalike modeling • Ad-tech and mar-tech optimization • Geospatial & POI analytics • Fraud detection & risk scoring • Personalization & recommendation engines • App performance & device compatibility insights

🏢 Industries Served Ad-Tech • Mar-Tech • FinTech • Telecom • Retail Analytics • Consumer Intelligence • AI & ML Platforms

China is leading the ranking by number of smartphone users, recording 859.38 million users. Following closely behind is India with 700.58 million users, while Seychelles is trailing the ranking with 0.05 million users, resulting in a difference of 859.33 million users to the ranking leader, China. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

The number of smartphone users in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total five million users (+22.17 percent). After the nineteenth consecutive increasing year, the smartphone user base is estimated to reach 27.51 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Kuwait and Israel.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

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.

The App Session Data (APAC) dataset delivers comprehensive insights into mobile app usage, user engagement, and behavioral patterns across the Asia-Pacific region. Collected through AI-modeled, OS-level keyboard applications, it represents one of the largest and most reliable sources of first-party, consented mobile session intelligence.

Our applications operate at the OS level, enabling continuous, privacy-safe data collection with an average of 170+ sessions per user per day. Each session is tracked with precise open and close timestamps, allowing for granular measurement of session duration, engagement frequency, and app usage behavior. The dataset is daily refreshed, ensuring high data freshness and relevance.

📊 Key Features & Attributes: • MAID-Based App Usage Data: Includes hashed Mobile Advertising IDs (GAID) for privacy-compliant user-level analysis. • App Metadata: App package name, category, and app name — enabling detailed segmentation by vertical (e.g., social, video, shopping). • Session Events: Includes open_timestamp, close_timestamp, and session_duration to measure precise engagement intervals per user. • Mobile Attribution Data: Captures device manufacturer, model, carrier, OS type/version, device language, and user agent for deep device-level intelligence. • Geolocation Data: Includes country, region, and city attributes derived from GPS/IP-based signals for contextual analytics. • Demographic Layer: Consented and AI-verified gender data linked with hashed device identifiers. • Daily Refreshed: Ensures up-to-date visibility into app engagement and behavior across millions of active users.

🌍 Geographic & Temporal Coverage: Covers active users across major APAC markets including India, Indonesia, Malaysia, Thailand, and the UAE. Each user has up to 10+ location updates and 170+ session events per day, providing a dynamic and temporal view of engagement patterns.

⚙️ Use Cases: • Ad-Tech & Attribution: Enhance MAID-based audience targeting and campaign measurement. • Behavioral Analytics: Analyze session duration, frequency, and app usage across device types and categories. • App Intelligence: Benchmark user engagement and session performance across verticals. • Geospatial Insights: Combine location, app, and device data for regional behavioral mapping. • Fraud & Identity Verification: Detect abnormal device patterns and session anomalies.

Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal

The number of social media users in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total six million users (+28.05 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 27.42 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of social media users in countries like Israel and Kuwait.

We collect, validate, model, and segment raw data signals from over 900+ sources globally to deliver thousands of mobile audience segments. We then combine that data with other public and private data sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms then clean, enrich, unify and aggregate these data sets for use in our products. We have categorized our audience data into consumable categories such as interest, demographics, behavior, geography, etc. Audience Data Categories:Below mentioned data categories include consumer behavioral data and consumer profiles (available for the US and Australia) divided into various data categories. Brand Shoppers:Methodology: This category has been created based on the high intent of users in terms of their visits to Brand outlets in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Place Category Visitors:Methodology: This category has been created based on the high intent of users visiting specific places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Demographics:This category has been created based on deterministic data that we receive from apps based on the declared gender and age data. Marital Status, Education, Party affiliation, and State residency are available in the US. Geo-Behavioural:This category has been created based on the high intent of users in terms of the frequency of their visits to specific granular places of interest in the real world. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. Interests:This segment is created based on users' interest in a specific subject while browsing the internet when the visited website category is clearly focused on a specific subject such as cars, cooking, traveling, etc. We use a deterministic model to assign a proper profile and time that information is valid. The recency of data can range from 14 to 30 days, depending on the topic. Intent:Factori receives data from many partners to deliver high-quality pieces of information about users’ shopping intent. We collect data from sources connected to the eCommerce sector and we also receive data connected to online transactions from affiliate networks to deliver the most accurate segments with purchase intentions, such as laptops, mobile phones, or cars. The recency of data can range from 7 to 14 days depending on the product category. Events:This category was created based on the high interest of users in terms of content related to specific global events - sports, culture, and gaming. Among the event segments, we also distinguish categories related to the interest in certain lifestyle choices and behaviors. To create segments containing users with a high-affinity index, we use a precise determination of the number of occurrences at a given time. App Usage:Mobile category is a branch of the taxonomy that is dedicated only to the data that is based on mobile advertising IDs. It is based on the categorization of the mobile apps that the user has installed on the device. Auto Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of automobile and other automotive attributes via a survey or registration. These audiences are currently available in the USA. Motorcycle Ownership:Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring that they own a certain brand of motorcycle and other motorcycle-based attributes via a survey or registration. These audiences are currently available for the USA. Household:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on users' declaring their marital status, parental status, and the overall number of children via a survey or registration. These audiences are currently available in the USA. Financial:Consumer Profiles - Available for the US and Australia this audience has been created based on their behavior in different financial services like property ownership, mortgage, investing behavior, and wealth and declaring their estimated net worth via a survey or registration. Purchase/ Spending Behavior:Consumer Profiles - Available for the US and AustraliaThis audience has been created based on their behavior in different spending behaviors in different business verticals available in the USA. Clusters:Consumer Profiles - Available for the US and AustraliaClusters are groups of consumers who exhibit similar demographic, lifestyle, and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives. B2B Audiences;Consumer Profiles - Available for US and AustraliaThis audience has been created based on users declaring their employee credentials, designations, and companies they work in, further specifying business verticals, revenue breakdowns, and headquarters locations. Customizable Audiences Data Segment:Brands can choose the appropriate pre-made audience segments or ask our data experts about creating a custom segment that is precisely tailored to your brief in order to reach their target customers and boost the campaign's effectiveness. Location Query Granularity:Minimum area: HEX 8Maximum area: QuadKey 17/City

This dataset provides comprehensive real-time data from Google Play Store. It includes detailed app information, reviews, ratings, download statistics, and more for Android apps and games worldwide. The data covers app attributes like pricing, version history, content rating, size, permissions, and privacy details, as well as user reviews and ratings. Users can leverage this dataset for app market research, competitor analysis, and mobile app intelligence. The API enables real-time access to Play Store's vast app catalog and marketplace data, helping businesses make data-driven decisions about app development, marketing, and positioning. Whether you're conducting market analysis, tracking competitors, or building mobile app tools, this dataset provides current and reliable Play Store data. The dataset is delivered in a JSON format via REST API.

Install App dataset provides comprehensive, first-party app install intelligence across the APAC region, sourced from AI-driven OS-level keyboard and utility applications. It captures highly granular insights into mobile app installations, updates, and user behavior, enabling precise market analytics, attribution tracking, and growth optimization.

Each record includes hashed device and advertising identifiers, application metadata (package name, app version, category), and timestamped install/update events. The field is_new_install indicates whether the app installation is first-time or an existing reinstall/update, helping distinguish between new user acquisition and returning user activity — a critical signal for campaign performance and user lifecycle analytics.

Alongside app-level insights, the dataset provides detailed device intelligence — including manufacturer, model, OS type/version, language, and user agent — combined with IP-based location data (country, region, city) and daily server timestamps for freshness tracking.

All data is hashed, privacy-compliant, and refreshed daily, making it ideal for organizations seeking high-quality, real-world app install signals across Android and iOS ecosystems.

📊 Key Features • First-party, consented data from OS-level applications • Hashed identifiers (device_id, advertising_id) for privacy-safe integration • Install and update timestamps for temporal and behavioral analysis • is_new_install flag to separate new installs from reinstalls or app updates • Comprehensive app, device, and location attributes • Daily refreshed dataset ensuring data accuracy and timeliness

⚙️ Primary Use Cases • Mobile Attribution & User Acquisition Tracking – Identify new users vs. re-engaged ones via the is_new_install flag • Market Intelligence & Competitive Benchmarking – Analyze install trends across app categories and geographies • Audience Segmentation – Classify users by device type, OS version, and app install behavior • Ad Targeting Optimization – Refine lookalike and re-engagement audiences with verified install data • Product & Growth Analytics – Study retention, uninstall rates, and user churn patterns • App Store Strategy – Evaluate app update frequency and version distribution

📍 Industries Benefiting • Ad-Tech & Mar-Tech Platforms • Mobile App Publishers & Developers • Telecom Operators & Device OEMs • Market Research & Analytics Firms • E-commerce, Fintech & Gaming Companies • Media, Entertainment & OTT Platforms

With millions of verified app installs tracked across Android and iOS, this AI-powered, consent-based dataset delivers actionable insights into app discovery, engagement, and retention, driving smarter decisions in mobile marketing, audience intelligence, and growth analytics.

This dataset includes data from the Young and Well Towns (YAWT) Collaborative Research Centre (CRC) project. An uncontrolled trial was conducted that investigated the use and effect of mobile apps for mental health and wellbeing in young people. The study targeted adolescents and young adults (age 16 - 25) from Australia. Participants were asked to complete a profiling survey that assessed demographic characteristics, mental health, personality, and app use. Furthermore, they were asked to use and link a range of freely and commercially available health, fitness, or wellbeing apps. A range of app-specific metrics were assessed throughout the study period. Individuals were asked to use the mobile apps for a period of at least two weeks. Participants were continuously monitored over the study period with regard to subjective mood, sleep, rest and energy, through regular web-based self-report assessments.Date coverage: 2016-06-01 - 2017-01-31