As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

As of May 2023, Facebook collected the larger number of total unique data points from global iOS users, around 32 data points. Popular digital payment app PayPal and Airbnb collected 26 data points each, while AI tool photo and image editing apps Photoleap collected around 14 unique data points.

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

Key Apple App Store StatisticsApple App Store App and Game RevenueApple App Store Gaming App RevenueApple App Store App RevenueApple App Store App and Game DownloadsApple App Store Game...

As of January 2022, the money app Greenlight had the highest number of data segments tracked, collecting 22 different types of data from its users. Launched in 2017, Greenlight is a fintech app for children that allows parents to manage and monitor allowances and spending. Mobile gaming app Pokémon GO was the second most invasive mobile app used by children, collecting 17 different data segments from its users. Only the Amazon Kids+ app and the Kinzoo Social app appeared to collect data over sensitive information from their users.

App Revenue Key StatisticsMobile Ad SpendApp and Game RevenuesiOS App and Game RevenueGoogle Play App and Game RevenueGaming App RevenuesiOS Gaming App RevenueGoogle Play Gaming App RevenueApp...

Key App Subscription StatisticsApp Subscription RevenueiOS App Subscription RevenueGoogle Play App Subscription RevenueApp Subscription Revenue by RegionApp Spend DistributionAverage App Subscription...

Mobile games make up the majority of video games released each year, with thousands of new additions to the Apple App Store and Google Play Store every year. By the mid-2010s, mobile games had...

App Tracking Transparency Key StatisticsATT Opt-In Rate by App CategoryATT Opt-In Rate by Game CategoryATT Opt-In Rate by CountryiOS Apps User TrackingiOS Apps Background Location AccessiOS Apps...

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

Keeping track of your health is, for many people, a continuous task. Monitoring what you eat, how often you exercise and how much water you drink can be time-consuming, fortunately there are tens of...

Apple App Store dataset to explore detailed information on app popularity, user feedback, and monetization features. Popular use cases include market trend analysis, app performance evaluation, and consumer behavior insights in the mobile app ecosystem.

Use our Apple App Store dataset to gain comprehensive insights into the mobile app ecosystem, including app popularity, user ratings, monetization features, and user feedback. This dataset covers various aspects of apps, such as descriptions, categories, and download metrics, offering a full picture of app performance and trends.

Tailored for marketers, developers, and industry analysts, this dataset allows you to track market trends, identify emerging apps, and refine promotional strategies. Whether you're optimizing app development, analyzing competitive landscapes, or forecasting market opportunities, the Apple App Store dataset is an essential tool for making data-driven decisions in the ever-evolving mobile app industry.

Dataset Features

• url: The URL linking to the app’s page on the Apple App Store.
• title: The name of the app.
• sub_title: A brief subtitle or tagline for the app.
• developer: The name of the entity or individual that developed the app.
• top_charts: Indicates if the app appears in top charts.
• monetization_features: Information on monetization aspects (such as in-app purchases or advertisements).
• image: A reference to the main app image.
• screenshots: Contains screenshot images of the app.
• description: Detailed app description outlining main features.
• what_new: Details on the latest updates or new features.
• rating: The overall rating based on user reviews.
• number_of_raters: The total number of users who have rated the app.
• reviews_by_stars: Breakdown of the number of reviews by star rating.
• reviews: An aggregation of user reviews.
• events: Any associated events or promotions.
• data_linked_to_you: Indicates if any data is linked to the user.
• seller: The entity responsible for selling or distributing the app.
• category: The category or genre of the app.
• languages: Languages supported by the app.
• copyright: Copyright information provided by the developer.
• size: The file size of the app.
• compatibility: Device or OS compatibility details.
• age_rating: The recommended age rating for the app.
• price: The price of the app.
• In_app_purchases: Details on in-app purchase options.
• support: Information related to app support.
• more_by_this_developer: Suggestions for other apps by the same developer.
• you_might_also_like: Recommendations for similar apps.
• app_support: Additional support details.
• privacy_policy: Link or reference to the app’s privacy policy.
• developer_website: The website of the app developer.
• featured_in: Information on any features or showcases the app has being part of.
• country: The country from which the app’s data was sourced.
• timestamp: A timestamp indicating when the data record was last updated.
• latest_app_version: The most recent version of the app available.
• app_id: A unique identifier for the app.

Distribution

• Data Volume: 36 Columns and 68M Rows
• Format: CSV

Usage

This dataset is versatile and can be used for various applications: - Market Analysis: Analyze app pricing strategies, monetization features, and category distribution to understand market trends and opportunities in the App Store. This can help developers and businesses make informed decisions about their app development and pricing strategies. - User Experience Research: Study the relationship between app ratings, number of reviews, and app features to understand what drives user satisfaction. The detailed review data and ratings can provide insights into user preferences and pain points. - Competitive Intelligence: Track and analyze apps within specific categories, comparing features, pricing, and user engagement metrics to identify successful patterns and market gaps. Particularly useful for developers planning new apps or improving existing ones. - Performance Prediction: Build predictive models using features like app size, category, pricing, and language support to forecast potential app success metrics. This can help in making data-driven decisions during app development. - Localization Strategy: Analyze the languages supported and regional performance to inform decisions about app localization and international market expansion.

Coverage

• Geographic Coverage: Global

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: Can leverage this dataset for training machine learning algorithms and building predictive models concerning app tr

Use the OpenWeb Ninja Google Play App Store Data API to access comprehensive data on Google Play Store, including Android Apps / Games, reviews, top charts, search, and more. Our extensive dataset provides over 40 app store data points, enabling you to gain deep insights into the market.

The App Store Data dataset includes all key app details:

App Name, Description, Rating, Photos, Downloads, Version Information, App Size, Permissions, Developer and Contact Information, Consumer Review Data.

This web map visualizes the percentage of households in a given geography that do not subscribe to broadband internet services. Data are shown by tract, county, and state boundaries -- zoom out to see data visualized for larger geographies. The map also displays the boundary lines for the jurisdiction of Rochester, NY (visible when viewing the tract level data), as this map was created for a Rochester audience.This web map draws from an Esri Demographics service that is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2014-2018ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 19, 2019National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

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.

Cell Phone Tracker App Market Outlook

The global cell phone tracker app market size is projected to grow from USD 1.5 billion in 2023 to USD 3.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. The growth of this market is driven by increasing concerns regarding security, the rising adoption of smartphones, and advancements in geolocation technology. These factors contribute to the expanding use cases of cell phone tracker apps across various user segments, including individuals, enterprises, and law enforcement agencies.

A significant growth factor for the cell phone tracker app market is the rising concern for personal and family safety. With the increasing prevalence of smartphones, parents are becoming more cautious about their children's safety. This has led to a higher demand for apps that allow for real-time location tracking and monitoring of phone activities. Similarly, individuals concerned about the security of their own devices and data are turning to tracker apps to safeguard against theft and unauthorized access.

In the corporate sector, enterprises are adopting cell phone tracker apps to enhance employee productivity and ensure the security of corporate devices. With the growing trend of remote work and the BYOD (bring your own device) policy, companies need to ensure that their data remains secure regardless of employee location. Tracker apps offer functionalities such as geo-fencing, access controls, and activity monitoring, which are crucial for maintaining corporate data integrity and compliance with regulatory standards.

Law enforcement agencies are also increasingly relying on cell phone tracker apps for investigative purposes. These applications provide critical tools for tracking suspects, locating missing persons, and monitoring criminal activities. The ability to access real-time data and historical location information enables law enforcement to respond more swiftly and effectively to incidents. Additionally, advancements in tracking technology and data analytics enhance the accuracy and reliability of these apps, making them indispensable tools for modern policing.

The integration of Contact Tracing Service within cell phone tracker apps is emerging as a crucial tool in the realm of public health and safety. These services utilize the geolocation capabilities of tracker apps to identify and alert individuals who have been in close proximity to someone diagnosed with a contagious disease. By leveraging existing technology, contact tracing can be conducted more efficiently and accurately, reducing the spread of infections. This application not only enhances personal safety but also contributes to broader community health efforts, making it a valuable addition to the feature set of modern tracker apps.

Regionally, North America and Europe are leading the market due to high smartphone penetration and strong security awareness among users. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid adoption of smartphones and increasing concerns about safety and security. The growing internet penetration and the expansion of the digital economy in emerging markets such as India and China are also contributing to the market's growth in this region.

Operating System Analysis

The operating system segment of the cell phone tracker app market is primarily divided into iOS, Android, and Windows. Each operating system presents unique opportunities and challenges for tracker app developers. iOS, being the operating system for Apple devices, benefits from a robust security framework and a user base known for high spending power. The App Store's stringent guidelines ensure that only high-quality and secure tracker apps are available, which appeals to consumers who prioritize data privacy and security.

Android, on the other hand, dominates the global smartphone market share, offering a vast user base for tracker app developers. The open-source nature of Android allows for greater flexibility in app development, enabling developers to create a wide range of features and functionalities tailored to different user needs. However, this openness also poses security challenges, as malicious apps can more easily infiltrate the platform. Despite this, the large market share and diverse user demographics make Android a crucial segment for tracker app developer

Artificial intelligence has taken over the app world, with thousands of apps integrating AI and the top AI app developers receiving hundred billion dollar valuations. Generative AI, in the form of...

Mobile App Analytics Market Outlook

The global mobile app analytics market size was valued at approximately USD 2.8 billion in 2023 and is projected to reach around USD 9.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.5% from 2024 to 2032. This significant growth can be attributed to the increasing use of mobile applications across various sectors, the need for real-time data-driven decisions, and the surge in mobile marketing activities.

One of the primary growth factors driving the mobile app analytics market is the exponential rise in smartphone usage and mobile internet penetration. As smartphones become more affordable and accessible, the number of mobile app downloads has surged, creating a vast reservoir of data for analysis. Businesses are leveraging mobile app analytics to understand user behavior, enhance user experience, and drive engagement, which in turn boosts customer retention and loyalty. Moreover, the advent of 5G technology is expected to further catalyze the adoption of mobile apps, thus driving the demand for advanced analytics solutions.

Another significant growth driver is the shift towards data-driven decision-making in businesses. Organizations across various sectors are realizing the importance of data insights in shaping strategies and achieving competitive advantage. Mobile app analytics provide critical insights into user behavior, preferences, and patterns, enabling businesses to tailor their offerings and marketing strategies effectively. The ability to track performance metrics, in-app activities, and revenue streams through analytics tools empowers businesses to optimize their apps and enhance profitability.

The proliferation of e-commerce and digital advertising is also a key growth contributor to the mobile app analytics market. With the rise of online shopping and digital marketing campaigns, businesses are increasingly relying on mobile app analytics to gauge the effectiveness of their marketing efforts, track conversions, and optimize sales funnels. The integration of analytics tools with advertising platforms allows marketers to measure the impact of their campaigns in real-time, make informed decisions, and allocate budgets more efficiently. This growing reliance on analytics to drive marketing success is expected to fuel the market's growth over the forecast period.

Regionally, North America dominates the mobile app analytics market due to the high adoption rate of advanced technologies and the presence of major market players. The Asia Pacific region, however, is anticipated to witness the highest growth rate during the forecast period, driven by the rapid digitization, increasing smartphone penetration, and growing focus on mobile-first strategies by businesses. The Europe market is also expected to grow steadily, supported by the increasing demand for mobile apps and the emphasis on data privacy and security regulations.

Component Analysis

In terms of components, the mobile app analytics market is segmented into software and services. The software segment holds a significant share of the market, driven by the rising demand for advanced analytics tools that can provide deep insights into user behavior and app performance. Analytics software solutions are designed to collect, process, and analyze vast amounts of data generated by mobile apps, enabling businesses to make data-driven decisions. The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in analytics software is further enhancing their capabilities, allowing for more accurate predictions and personalized user experiences.

The services segment is also gaining traction as businesses increasingly seek expert guidance and support in implementing and optimizing their analytics strategies. These services include consulting, implementation, and maintenance, helping organizations to effectively leverage analytics tools and maximize their ROI. The growing complexity of mobile app ecosystems and the need for continuous monitoring and optimization are driving the demand for professional services. Additionally, the rise of managed services, where third-party providers manage the entire analytics infrastructure, is providing businesses with a cost-effective and efficient solution.

Within the software segment, there is a wide range of solutions available, including real-time analytics, user analytics, performance analytics, and marketing analytics. Real-time analytics solutions are particularly valuable for businesses that need to make immediate decisions based on cu

Intelligent Apps Market size was valued at USD 35.17 Billion in 2024 and is projected to reach USD 338.1 Billion by 2032, growing at a CAGR of 36.07% during the forecast period 2026-2032.

Global Intelligent Apps Market Drivers

The market drivers for the Intelligent Apps Market can be influenced by various factors. These may include:

Increasing Use of AI and Machine Learning: Applications are becoming more and more capable and efficient as a result of the increasing integration of AI and ML technologies. The need for intelligent apps that can enhance operational efficiency and offer customised experiences is fueled by this. Growing Need for Data-Driven Decision Making: Companies are using data analytics to make better decisions more and more. By real-time analysis of huge amounts of data, intelligent apps enable businesses to obtain useful insights and streamline their operations. Proliferation of Smart Devices: The market for intelligent apps is increased by the extensive usage of smartphones, tablets, and other smart devices. With the sophisticated capabilities of smart devices, such sensors and networking, these apps provide cutting-edge and engaging features. Empowering Intelligent Applications: Cloud computing is growing since cloud platforms offer the services and infrastructure required to facilitate the creation and implementation of intelligent applications. Intelligent applications are encouraged to be adopted by companies by cloud computing's scalability, flexibility, and affordability. Unlocking Customer Happiness: Intelligent apps use AI to comprehend user preferences and behaviour, so providing better user experiences. Increased customer happiness and engagement follow, which propels the market expansion. Growing Attention to consumer Engagement: Companies are emphasising on enhancing consumer involvement by means of customised interactions. Companies may provide tailored information, suggestions, and services thanks to intelligent apps, which increases client retention and loyalty. Projects for Digital Transformation: To remain competitive, companies in a variety of sectors are going through digital transformation. Through automation of procedures, increased efficiency, and data-driven insights, intelligent apps are essential to this revolution. Natural Language Processing (NLP) Technology Advancements: More efficient comprehension and response of human language by intelligent apps is made possible by advances in NLP. This improves chatbots', virtual assistants', and other conversational AI systems' capabilities and encourages their use. Empowering Enterprises: Enhanced data security and regulatory compliance can be achieved by enterprises using intelligent apps by means of sophisticated analytics and automated monitoring. Their acceptance is driven by this, especially in sectors with strict compliance standards. Increasing Investment in AI Startups and Innovations: New intelligent app development and innovation are encouraged by the increase in investments in AI and associated technologies. The competitive market environment this produces encourages more developments and acceptance.

Running Apps Market Outlook

The global Running Apps market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The robust growth of this market can be attributed to increasing awareness about health and fitness, technological advancements in mobile applications, and the rising penetration of smartphones globally. As more individuals seek convenient ways to track their fitness routines, the demand for running apps is expected to rise significantly.

The primary growth factor for the Running Apps market is the increasing awareness and emphasis on health and wellness. With rising concerns about lifestyle-related diseases and the growing popularity of fitness trends, more individuals are turning to technology to assist in their fitness journeys. Running apps provide users with real-time data on their performance, track progress over time, and offer motivational features such as goal-setting and social sharing. This fusion of fitness and technology is proving to be a major driver for the market.

Technological advancements in mobile applications are also playing a significant role in the growth of the Running Apps market. Features such as GPS tracking, heart rate monitoring, and personalized training plans have become standard offerings in modern running apps. The integration of artificial intelligence (AI) and machine learning (ML) algorithms enables these apps to provide customized recommendations and adaptive training programs based on the user's performance and fitness level. The continuous innovation in app functionalities is likely to sustain market growth.

Another key growth factor is the increasing penetration of smartphones and internet connectivity worldwide. As smartphone usage continues to rise, especially in emerging markets, more people have access to running apps. In addition, the proliferation of wearable fitness devices that can sync with these apps is further driving their adoption. This widespread availability and accessibility are making it easier for users to incorporate running apps into their daily routines, thereby contributing to market expansion.

The integration of pedometers into running apps has further enhanced their functionality, allowing users to track their step count alongside other fitness metrics. Pedometers provide a simple yet effective way to monitor daily activity levels, encouraging users to stay active throughout the day. By incorporating pedometer data, running apps can offer a more comprehensive view of a user's physical activity, beyond just running. This feature is particularly beneficial for users aiming to increase their overall movement and achieve daily step goals. As pedometers become more sophisticated, with features such as automatic step detection and integration with other health metrics, they are becoming an essential component of modern running apps. This integration not only enhances user engagement but also supports the broader goal of promoting a healthy and active lifestyle.

From a regional perspective, North America is expected to hold the largest market share during the forecast period, followed by Europe and the Asia Pacific. The high adoption rate of advanced technologies and a strong fitness culture in these regions are significant contributing factors. In contrast, the Asia Pacific region is projected to witness the highest growth rate, driven by increasing health consciousness and the rapid adoption of smartphones in countries like China and India. Government initiatives promoting physical activity and wellness are also expected to boost the market in these regions.

Operating System Analysis

The Running Apps market is segmented by operating systems, primarily focusing on iOS, Android, and Windows. Each of these operating systems has unique characteristics and user bases that significantly impact the market dynamics. iOS, developed by Apple, is known for its high security and seamless user experience. Running apps on iOS often leverage advanced hardware and software integration, providing users with accurate and reliable fitness tracking features. The popularity of iOS in developed markets like North America and Europe contributes to its significant share in the market.

Android, developed by Google, holds the largest share in the global smartphone market, which directly translates into a substan