In 2021, the number of mobile users worldwide stood at 7.1 billion, with forecasts suggesting this is likely to rise to 7.26 billion by 2022. In 2025, the number of mobile users worldwide is projected to reach 7.49 billion.

North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming ** gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** 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 *** 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 the Americas and Asia.

South Korea

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.

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

In 2022, the average data used per smartphone per month worldwide amounted to ** gigabytes (GB). The source forecasts that this will increase almost four times reaching ** GB per smartphone per month globally in 2028.

United States rose 107.6% of Average per Capita Monthly Mobile Data Use in 2014, compared to the previous year.

User Feedback Dataset from the Top 15 Downloaded Mobile Applications

This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling.

List of Included Applications:

• TikTok
• Instagram
• Facebook
• WhatsApp
• Telegram
• Zoom
• Snapchat
• Facebook Messenger
• Capcut
• Spotify
• YouTube
• HBO Max
• Cash App
• Subway Surfers
• Roblox

Data Columns and Descriptions:

• review_id: Unique identifiers for each user feedback/application review.
• content: User-generated feedback/review in text format.
• score: Rating or star given by the user.
• TU_count: Number of likes/thumbs up (TU) received for the review.
• app_id: Unique identifier for each application.
• app_name: Name of the application.
• RC_ver: Version of the app when the review was created (RC).

Terms of Use:

This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:

1.Paper

M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3332644

2.Dataset

Asnawi, M. H., Pravitasari, A. A., Herawan, T., & hendrawati, T. (2023). User Feedback Dataset from the Top 15 Downloaded Mobile Applications [Data set]. In The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling (1.0.0, Vol. 11, pp. 130272–130286). Zenodo. https://doi.org/10.5281/zenodo.10204232

Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

India: Mobile phone subscribers, per 100 people: The latest value from 2023 is 80.56 subscribers per 100 people, an increase from 80.18 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for India from 1960 to 2023 is 23.1 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 85.97 subscribers per 100 people was recorded in 2017.

This dataset provides a comprehensive analysis of mobile device usage patterns and user behavior classification. It contains 700 samples of user data, including metrics such as app usage time, screen-on time, battery drain, and data consumption. Each entry is categorized into one of five user behavior classes, ranging from light to extreme usage, allowing for insightful analysis and modeling.

Key Features: - User ID: Unique identifier for each user. - Device Model: Model of the user's smartphone. - Operating System: The OS of the device (iOS or Android). - App Usage Time: Daily time spent on mobile applications, measured in minutes. - Screen On Time: Average hours per day the screen is active. - Battery Drain: Daily battery consumption in mAh. - Number of Apps Installed: Total apps available on the device. - Data Usage: Daily mobile data consumption in megabytes. - Age: Age of the user. - Gender: Gender of the user (Male or Female). - User Behavior Class: Classification of user behavior based on usage patterns (1 to 5).

This dataset is ideal for researchers, data scientists, and analysts interested in understanding mobile user behavior and developing predictive models in the realm of mobile technology and applications. This Dataset was primarily designed to implement machine learning algorithms and is not a reliable source for a paper or article.

Forecast: Average per Capita Monthly Mobile Data Use in Italy 2024 - 2028 Discover more data with ReportLinker!

Mexico: Mobile phone subscribers, in millions: The latest value from 2023 is 144.74 million subscribers, an increase from 135.96 million subscribers in 2022. In comparison, the world average is 54.59 million subscribers, based on data from 156 countries. Historically, the average for Mexico from 1960 to 2023 is 39.97 million subscribers. The minimum value, 0 million subscribers, was reached in 1960 while the maximum of 144.74 million subscribers was recorded in 2023.

Forecast: Average per Capita Monthly Mobile Data Use in Brazil 2024 - 2028 Discover more data with ReportLinker!

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total **** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach ****** 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).

Brazil Number of Mobile Phone User: Female data was reported at 75,080.687 Person th in 2017. This records an increase from the previous number of 72,942.032 Person th for 2016. Brazil Number of Mobile Phone User: Female data is updated yearly, averaging 74,011.360 Person th from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 75,080.687 Person th in 2017 and a record low of 72,942.032 Person th in 2016. Brazil Number of Mobile Phone User: Female data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.TB010: Number of Cell Phone User: by Sex and Age.

Forecast: Average per Capita Monthly Mobile Data Use in India 2024 - 2028 Discover more data with ReportLinker!

Chad: Mobile phone subscribers, per 100 people: The latest value from 2023 is 70.19 subscribers per 100 people, an increase from 65.44 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for Chad from 1960 to 2023 is 12.59 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 70.19 subscribers per 100 people was recorded in 2023.

Poland: Mobile phone subscribers, per 100 people: The latest value from 2023 is 135.15 subscribers per 100 people, a decline from 137 subscribers per 100 people in 2022. In comparison, the world average is 120.02 subscribers per 100 people, based on data from 156 countries. Historically, the average for Poland from 1960 to 2023 is 50.27 subscribers per 100 people. The minimum value, 0 subscribers per 100 people, was reached in 1960 while the maximum of 148.91 subscribers per 100 people was recorded in 2013.