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

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

This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

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.

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.

Explore Crawlora's mobile app dataset: 4,109,116 apps across both stores — 1,170,541 on Apple's App Store and 2,938,575 on Google Play. Categories, ratings, install scale, pricing and global availability — with REST API access.

These data have been collected and supplied by Huq Ltd. and comprise of records for the period July 2016 to October 2020. The data contain aggregated geolocated activity counts derived from mobile phone app use across Great Britain.

Mobile phone applications seek user’s consent for recording and storing the mobile device’s location when the app is in use. Activity counts are derived from these locations as the sum of distinct devices per grid cell per day. These data can be used as proxy for estimating activity levels and footfall across the UK.

These aggregate data were created from record level data which comprised individual phone IDs, and multiple entries for each mobile device if it is used multiple times for one app or the user accesses multiple apps. Thus, the following data cleaning and aggregation process has been used to create the activity counts:

1. Cleaning: Daily records comprise unique device ID, time-stamp and location of each entry collected by any app. The time-stamp is reformatted as a single daily date attribute.

2. Spatial linkage to OSGB grid: After turning the daily data-frames into spatial objects, the files are joined to the 1km x 1km OSGB grid, and each impression is attributed a grid cell ID corresponding to its latitude and longitude.

3. Creation of activity counts: Activity counts are created following the previous steps by counting the number of unique device IDs per grid cell per date. This removes multiple appearances of the same device (one device may collect multiple impressions through different apps or due to frequent usage). The final activity count corresponds to the number of unique devices within a 1km square for that day.

4. Output: The output comprises cleaned aggregation counts for each grid cell and day

N.B. More detail on how the data was collected and coverage is available if requesting for this detail in your initial application purpose, or if contacting us by email once you have made your initial application and received the form. Applicants would need to sign a non-disclosure agreement before accessing this detail, and such as request will significantly increase the time for data delivery. You can, of course, make a full application for the data without first receiving this collection/ coverage metadata.

Content

These data are provided at 1km x 1km OSGB Grid cells.

Activity counts of 1-10 devices are masked and replaced by “*” in the database, as low counts present potentially identifiable information.

For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page.

Quality, Representation and Bias

Excellent quality and coverage for major towns and cities. The data may be less complete for smaller settlements or more rural areas. Data are subject to suppression of potentially disclosive low counts as detailed above. Huq collects data from a varying mix of apps, the identities of which are commercially sensitive. Apps may be added to or deleted from the secure and summary data products over time. This, along with increasing national coverage and mobile phone uptake, results in general increases in apparent activity over the period covered by the data.

The dataset would benefit from comparison with population estimates (e.g. census data) to investigate coverage issues. 2016 data have the highest percentage of suppressed counts, and data suppression generally decreases over time, particularly in metropolitan (Met) areas. Data suppression levels in metropolitan areas generally fall below 50% by 2020.

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.

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.

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.

This comprehensive iOS application reviews dataset contains thousands of authentic user reviews from the Apple App Store in English. The dataset provides valuable insights for app developers, marketers, and researchers studying mobile application performance and user sentiment.

Key Features:

• Real user reviews from popular iOS apps
• Star ratings from 1 to 5 stars
• Review dates and timestamps
• App store URLs and metadata
• User demographics and location data
• App version information
• Review titles and detailed feedback

Applications: Perfect for sentiment analysis, app store optimization, mobile app development research, user experience studies, and competitive analysis. This dataset enables businesses to understand user preferences, identify app improvement opportunities, and develop better mobile applications.

Data Quality: All reviews are genuine user feedback collected from the official Apple App Store, ensuring authenticity and reliability for research and business intelligence purposes. The dataset covers various app categories including fitness, shopping, education, entertainment, and productivity applications.

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.

This dataset provides information about 837 mobile applications (apps) published across the whole world to fight the COVID-19 crisis. This information includes: (a) information available in the mobile app stores (Apple App Store and Google Play) between 20/04/2020 and 02/08/2020; (b) complementary information obtained from manual analysis performed until mid-September 2020; and (c) status information about app availability on 28/02/2021, when we last visited the mobile app stores. The dataset is one of the outcomes of the JRC Unit B.6 multi-channel approach to the monitoring and analysis of COVID-19-related mobile apps.

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.

As of **********, three Meta-owned mobile apps, Facebook Messenger, Facebook, and Instagram were reported to collect the largest amount of data from global iOS users. Each of the three mobile apps collected ** data points across ** segments, including *** data points regarding user content, and **** data points regarding users' contact info. Mobile apps Line, PayPal, Amazon Shopping, and LinkedIn followed, collecting ** data points from users according to the privacy details section in the Apple App Store.

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.

Unlock the power of user feedback with our iOS App Store Reviews Dataset, a comprehensive collection of reviews from thousands of apps across various categories. This robust App Store dataset includes essential details such as app names, ratings, user comments, timestamps, and more, offering valuable insights into user experiences and preferences.

Perfect for app developers, marketers, and data analysts, this dataset allows you to conduct sentiment analysis, monitor app performance, and identify trends in user behavior. By leveraging the iOS App Store Reviews Dataset, you can refine app features, optimize marketing strategies, and elevate user satisfaction.

Whether you’re tracking mobile app trends, analyzing specific app categories, or developing data-driven strategies, this App Store dataset is an indispensable tool. Download the iOS App Store Reviews Dataset today or contact us for custom datasets tailored to your unique project requirements.

Ready to take your app insights to the next level? Get the iOS App Store Reviews Dataset now or explore our custom data solutions to meet your needs.

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 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.