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

Description:

• The dataset comprises anonymized data on apps available on the Google Play Store, capturing various aspects such as ratings, downloads, and categorization.
• The dataset has 10,841 entries, with some columns containing missing values, particularly in "Rating," "Type," "Content Rating," "Current Ver," and "Android Ver".
• This dataset can be utilized for analyzing trends in mobile app usage, user preferences, and app performance metrics across different categories.
• It can aid in understanding the impact of factors like app size, rating, and category on user downloads and popularity.
• This dataset can be utilized for analyzing app popularity, user preferences, and the relationship between app features (e.g., size, price) and downloads.
• It can help in identifying trends in app categories, assessing user satisfaction through ratings and reviews, and providing insights for app developers and marketers on user engagement and app performance.

Features:

🚀 MobileViews: A Large-Scale Mobile GUI Dataset

MobileViews is a large-scale dataset designed to support research on mobile agents and mobile user interface (UI) analysis. The first release, MobileViews-600K, includes over 600,000 mobile UI screenshot-view hierarchy (VH) pairs collected from over 20,000 apps on the Google Play Store. This dataset is based on the DroidBot, which we have optimized for large-scale data collection, capturing more comprehensive interaction details while… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/MobileViews.

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

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.

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.

Don't forget to hit the upvote🙏

This DataSet Contains Detailed Insights into Mobile App Usage Patterns, including ScreenTime, notifications received, and app openings. The data spans multiple days in August and some popular apps, offering a granular view of digital behavior.

Features:

1. Date: The date of the recorded data.

2. App: The name of the mobile application.

3. Usage (minutes): Total minutes spent using the app on a given day.

4. Notifications: Number of notifications received from the app.

5. Times Opened: How many times the app was launched.

As of May 2023, the mobile app version of popular ********************************* used ** of the data points they collected to track their iOS users, as well as collecting ** data points connected to the user's identity. Facebook, which was identified as the most data-hungry app among all the mobile social media, used ***** of its ** collected data points to track users. Dating app ****** collected ** data points collected to the users' identity, as well as **** data points to track users activity.

ChatGPT is no longer a web-first product. Its mobile app crossed 1.1 billion monthly active users in April 2026 and has been downloaded more than 1.9 billion times across iOS and Android since launching in May 2023 — making it one of the most-installed consumer apps of the decade and the only AI product anywhere near that scale. This page tracks how mobile adoption grew, how the install curve has cooled from its 2025 peak even as engagement and revenue climb, how consumer spending inside the app has compounded, and how adoption splits between Apple's App Store and Google Play.

Mobile Game In-App Purchase Dataset

Dataset Overview

This comprehensive synthetic dataset contains detailed information about mobile game players and their in-app purchase behaviors, specifically designed for analyzing spending patterns and user segmentation in the mobile gaming industry. The dataset focuses on the classic "whales vs minnows" segmentation model, providing insights into different player spending categories and their gaming habits.

Dataset Characteristics

• Total Records: 3,024 user entries
• Features: 13 columns covering user demographics, gaming behavior, and transaction data
• Target Application: Mobile game monetization analysis, user behavior prediction, and revenue optimization
• Data Quality: Contains realistic null values (~2-5%) to simulate real-world data collection scenarios

Column Descriptions

Spending Segment Distribution

• Whales: 2% of users contributing high-value purchases ($500-$5,000)
• Dolphins: 13% of users with moderate spending ($20-$500)
• Minnows: 85% of users with low or no spending ($0-$20)

Key Use Cases

1. Revenue Optimization: Identify high-value user characteristics and optimize monetization strategies
2. User Segmentation: Develop targeted marketing campaigns for different spending segments
3. Churn Prediction: Analyze purchase patterns to predict user retention
4. A/B Testing: Compare monetization strategies across different user segments
5. Market Analysis: Understand regional and demographic spending behaviors
6. Product Development: Align game features with high-spending user preferences

Data Quality Notes

• Contains intentional missing values to simulate real-world data collection challenges
• Realistic spending distributions based on mobile gaming industry standards
• Diverse geographic representation covering major mobile gaming markets
• Balanced device platform distribution reflecting current market share

This dataset provides a robust foundation for mobile game analytics, user behavior modeling, and revenue optimization strategies in the competitive mobile gaming landscape.

Dataset Card for Dataset Name

  Dataset Summary

MobileRec is a large-scale app recommendation dataset. There are 19.3 million user\item interactions. This is a 5-core dataset. User\item interactions are sorted in ascending chronological order. There are 0.7 million users who have had at least five distinct interactions. There are 10173 apps in total.

  Supported Tasks and Leaderboards

Sequential Recommendation

  Languages

English

  How to use the… See the full description on the dataset page: https://huggingface.co/datasets/recmeapp/mobilerec.

Aggregate metrics over 4,109,116 apps across the Apple App Store (1,170,541) and Google Play (2,938,575): category mix, ratings, pricing, attention concentration, and global storefront availability — queryable via REST API.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

As of February 2025, video apps accounted for around 76 percent of global mobile data usage every month. Second-ranked social networking accounted for eight percent of global mobile data volume. The two categories, though, can easily overlap, as users can watch videos via video applications, as well as on social networking applications. Most popular social media platforms with video content Facebook, YouTube, and Instagram were among the most popular social networks in the world, as of October 2021. Each of these platforms allow to post, share, and watch video content on a mobile device. One of the fastest growing global brands, Tiktok, is also a social media platform where users can share video content. In September 2021, the platform reached 1 billion monthly active users. Leading types of mobile video content in the U.S. The United States was the third country in the world based on the number of smartphone users as of May 2021, with around 270 million users. Therefore, mobile content usage in the country was one of the highest in the world, and a big part of it was video content. As of the third quarter of 2021, more than 80 percent of survey respondents in the United States reported watching YouTube on their mobile devices. Social media videos were the second most popular type of content for mobile audiences, with almost six in 10 respondents watching videos on social media platforms like TikTok and Twitter.

As of March 2021, YouTube Kids and Facebook Messenger Kids were the mobile apps for children found to collect the largest amount of data from global iOS users. The apps collected a total of 15 data points from each of the examined data types,. Language learning app Lingokids and educational app ABCmouse followed with 10 data points. The type of data that the examined children's apps collected mostoften were contact information and diagnostics.

Children mobile privacy From online education to gaming and social media, children and young users are increasingly active in online environments via mobile devices. In 2021, playing online games and watching YouTube videos figured among the most popular mobile activities for kids worldwide, while less than five in 10 reported using their phones to complete assignments for school. As vulnerable users, children are entitled to institutional protection and lower interference from tech companies. However, mobile apps designed for children still collect data from their young users. As of the beginning of 2022, money management and gaming apps were the app categories found to track the largest number of data segments from children, with 10.1 and 9.3 data points tracked, respectively.

Child proof social media? While the impact of social media on younger users’ development is yet to be fully understood, parents and educators were quick to realize that social media expands the range of dangers children can encounter while being online. In 2021, children in the United States and in the United Kingdom spent an average of 98 minutes per day on TikTok, as well as 83 minutes daily on Snapchat. In the U.S., both Snapchat and TikTok agreed to respect the age limit restrictions set by the Children's Online Privacy Protection Act (COPPA), and while Snapchat discontinued its children-specific Snapkidz app in 2016, TikTok relies on its TikTok Younger Users platform for users younger than 13. Despite the majority of social media services requiring users to be at least 13 years old, a survey conducted in 2021 in the United Kingdom has found that 60 percent of all surveyed kids aged between eight and 11 had their own social media profile.

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.

As of May 2023, the mobile app of shopping and marketplace platform Etsy used approximately half of its collected data points to track users. In comparison, health app Noom used only *** of its collected user data point for tracking purposes.

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: 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: 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: 10.1109/ACCESS.2023.3332644. 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.

This project was sponsored by Universitas Padjadjaran and supported by the Research Center for Artificial Intelligence and Big Data (AIDA) Universitas Padjadjaran

Quantify mobile app popularity and user acquisition by tracking downloads across major app stores, offering insights into market penetration and growth.

Aaditya1/Mobile-Application-Data dataset hosted on Hugging Face and contributed by the HF Datasets community