Google Play Store dataset to explore detailed information about apps, including ratings, descriptions, updates, and developer details. Popular use cases include app performance analysis, market research, and consumer behavior insights.

Use our Google Play Store dataset to explore detailed information about apps available on the platform, including app titles, developers, monetization features, user ratings, reviews, and more. This dataset also includes data on app descriptions, safety measures, download counts, recent updates, and compatibility, providing a complete overview of app performance and features.

Tailored for app developers, marketers, and researchers, this dataset offers valuable insights into user preferences, app trends, and market dynamics. Whether you're optimizing app development, conducting competitive analysis, or tracking app performance, the Google Play Store dataset is an essential resource for making data-driven decisions in the mobile app ecosystem.

Dataset Features

• url: The URL link to the app’s detail page on the Google Play Store.
• title: The name of the application.
• developer: The developer or company behind the app.
• monetization_features: Information regarding how the app generates revenue (e.g., in-app purchases, ads).
• images: Links or references to images associated with the app.
• about: Details or a summary description of the app.
• data_safety: Information regarding data safety and privacy practices.
• rating: The overall rating of the app provided by its users.
• number_of_reviews: The total count of user reviews received.
• star_reviews: A breakdown of reviews by star ratings.
• reviews: Reviews and user feedback about the app.
• what_new: Information on the latest updates or features added to the app.
• more_by_this_developer: Other apps by the same developer.
• content_rating: The content rating which guides suitability based on user age.
• downloads: The download count or range indicating the app’s popularity.
• country: The country associated with the app listing.
• app_category: The category or genre under which the app is classified.

Distribution

• Data Volume: 17 Columns and 65.54M Rows
• Format: CSV

Usage

This dataset is ideal for a variety of applications:

• App Market Analysis: Enables market researchers to extract insights on app popularity, engagement, and trends across different categories.
• Machine Learning: Can be used by data scientists to build recommendation engines or sentiment analysis models based on app review data.
• User Behavior Studies: Facilitates academic or industrial research into user preferences and behavior with respect to mobile applications.

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: To train machine learning models for app popularity prediction, sentiment analysis, or recommendation systems.
• Researchers: For academic or scientific studies into market trends, consumer behavior, and app performance analysis.
• Businesses: For strategic analysis, developing market insights, or enhancing app development and user engagement strategies.

Suggested Dataset Name

1. Play store Insights
2. Android App Scope
3. Market Analytics
4. Play Store Metrics Vault

5. AppTrend360: Google Play Edition

Pricing

Based on Delivery frequency

~Up to $0.0025 per record. Min order $250

Approximately 10M new records are added each month. Approximately 13.8M records are updated each month. Get the complete dataset each delivery, including all records. Retrieve only the data you need with the flexibility to set Smart Updates.

• Monthly

New snapshot each month, 12 snapshots/year Paid monthly

• Quarterly

New snapshot each quarter, 4 snapshots/year Paid quarterly

• Bi-annual

New snapshot every 6 months, 2 snapshots/year Paid twice-a-year

• One-time purchase

New snapshot one-time delivery Paid once

[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!

Analysis of ‘Google Play Store Category wise Top 500 Apps’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shakthidhar/google-play-store-category-wise-top-500-apps on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Google Play stores top 500 app data based on their rankings on January 2022 for all the available categories. Link to scraping code: https://github.com/Shakthi-Dhar/AppPin Link to backup datafiles: github data files

Content

The dataset contains the top 500 android apps available on the google play store for the following categories: All Categories, Art & Design, Auto & Vehicles, Beauty, Books & Reference, Business, Comics, Communication, Education, Entertainment, Events, Finance, Food & Drink, Health & Fitness, House & Home, Libraries & Demo, Lifestyle, Maps & Navigation, Medical, Music & Audio, News & Magazines, Parenting, Personalization, Photography, Productivity, Shopping, Social, Sports, Tools, Travel & Local, and Video Players & Editors.

The app rankings are based on google play store app rankings for January 2022.

Abbreviations

In Review and Downloads, the alphabet T, L, Cr represents Thousands, Lakhs, Crores as per the google play store naming convention. They are similar to M, B which represent millions, billions. 1L (1 Lakh) = 100T (100 Thousand) 10L (10 Lakhs) = 1M (1 Million) 1Cr( 1 Crore) = 10M (10 Million)

Acknowledgements

This data is not provided directly by Google, so I used Appium an automation tool with python to scrape the data from the google play store app.

Inspiration

Inspired by Fortune500. Fortune500 provides data on top companies in the world, so why not have a data source for top apps in the world.

--- Original source retains full ownership of the source dataset ---

Context

App Permission data of 2.2 million android applications from Google Play store. Backup repo: https://github.com/gauthamp10/android-permissions-dataset

Content

I've collected the data with the help of Python and Scrapy running on a cloud virtual machine with the United States as geolocation. The data was collected on June 2021.

Also checkout:

• Google Play Store Apps dataset: https://www.kaggle.com/gauthamp10/google-playstore-apps
• Apple AppStore Apps dataset: https://www.kaggle.com/gauthamp10/apple-appstore-apps

Acknowledgements

I couldn't have build this dateset without the help of Digitalocean and github. Switched to facundoolano/google-play-scraper for sane reasons.

Inspiration

Took inspiration from: https://www.kaggle.com/gauthamp10/google-playstore-apps to build a big database for students and researchers who are interested to analyze and find insights on mobile application privacy.

Author

Gautham Prakash

My other projects: github.com/gauthamp10

Website: gauthamp10.github.io

Analysis of ‘Playstore Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/madhav000/playstore-analysis on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Google Play Store team had launched a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility. This feature will help bring more attention to newer apps that have the potential.

Analysis to be done:

The problem is to identify the apps that are going to be good for Google to promote. App ratings, which are provided by the customers, is always a great indicator of the goodness of the app. The problem reduces to: predict which apps will have high ratings.

Problem Statement:

Google Play Store team is about to launch a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility. This feature will help bring more attention to newer apps that have the potential.

Content:

Dataset: Google Play Store data (“googleplaystore.csv”)

Fields in the data: App: Application name Category: Category to which the app belongs Rating: Overall user rating of the app Reviews: Number of user reviews for the app Size: Size of the app Installs: Number of user downloads/installs for the app Type: Paid or Free Price: Price of the app Content Rating: Age group the app is targeted at - Children / Mature 21+ / Adult Genres: An app can belong to multiple genres (apart from its main category). For example, a musical family game will belong to Music, Game, Family genres. Last Updated: Date when the app was last updated on Play Store Current Ver: Current version of the app available on Play Store Android Ver: Minimum required Android version

--- Original source retains full ownership of the source dataset ---

This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the replication package to direct you to help replicate it for your dataset too.

Note: The dataset given are protected with password, and the password is available in our published paper

Dataset

This dataset was created by Cabinet Shah

Released under Data files © Original Authors

Contents

The global market for App Data Statistics Tools is experiencing robust growth, driven by the escalating demand for data-driven decision-making within the mobile app industry. The increasing complexity of app development and marketing necessitates tools that provide comprehensive insights into user behavior, app performance, and market trends. This allows developers and marketers to optimize their strategies, enhance user experience, and ultimately increase profitability. The market is segmented by tool type (customized vs. universal) and application type (social, information, games, shopping, etc.), with customized tools catering to specific needs and universal tools offering broader functionality. Companies like App Annie, Firebase, and Mixpanel are prominent players, competing on features, pricing, and data depth. The North American and European markets currently hold significant shares, but growth is projected in the Asia-Pacific region, fueled by the expanding mobile app ecosystem in countries like India and China. The market's growth is further propelled by the increasing adoption of advanced analytics techniques such as machine learning and AI for more precise predictions and data-driven insights. Furthermore, the rising popularity of mobile gaming and e-commerce apps is directly influencing the demand for sophisticated app analytics. The forecast period (2025-2033) anticipates continued expansion, fueled by technological advancements, rising competition, and the increasing adoption of subscription models for these services. While challenges remain – including data privacy concerns and the complexity of integrating diverse data sources – the overall market outlook remains positive. The continuous innovation in app development, coupled with the imperative to understand user behavior and optimize app performance, ensures the long-term viability and growth of the App Data Statistics Tool market. Companies are focusing on developing user-friendly interfaces, intuitive dashboards, and robust reporting capabilities to meet evolving customer needs. Strategic partnerships and acquisitions will also play a role in shaping the competitive landscape in the coming years. This growth is projected across all segments, but particularly strong growth is anticipated in the customized tools for specific app types like AR/VR gaming and fintech apps, which require specialized data analysis.

This dataset comprises 10,000 user reviews of the BCA Mobile app collected from the Google Play Store between December 24, 2023, and June 12, 2024. Each review includes the user's name, the rating they provided (ranging from 1 to 5 stars), the timestamp of when the review was created, and the text content of the review. The dataset is in Indonesian and focuses on feedback from users in Indonesia. This data can be used to perform sentiment analysis, understand user experiences, identify common issues, and assess the overall performance of the BCA Mobile app during the specified timeframe. The reviews are sorted based on the newest first, providing the latest feedback at the top.

This dataset provides detailed information about user reviews for the Google Pay application, collected from the Google Play Store. The context of this data is the widespread use of Unified Payments Interface (UPI) as the primary payment method in India, with Google Pay, PhonePe, and Paytm being major players. The purpose of compiling these reviews is to enable a comparative analysis of UPI applications based on user feedback, offering insights into aspects such as app usability, user interface effectiveness, occurrences of technical glitches, and overall customer satisfaction. The dataset serves as a valuable resource for understanding the nuances of user experience with a prominent mobile payment app.

Columns

• Review Number: A numerical identifier for each user review.
• reviewId: A unique identification string for every review.
• userName: The name of the individual who submitted the review.
• userImage: A web link to the user's profile picture or image.
• content: The complete text of the user's written review.
• score: A rating on a scale from 1 to 5, where 1 denotes a negative review and 5 indicates a positive one.
• thumbsUpCount: The total count of 'thumbs up' or upvotes received by the review.
• reviewCreatedVersion: The specific version of the Google Pay app that was reviewed.
• at: The date and time stamp indicating when the review was posted.
• replyContent: The text of any response provided by the app management team to the user's review.

Distribution

The dataset is typically provided in a CSV file format. It contains approximately 33,183 unique review identifiers. The review scores exhibit a notable distribution, with a substantial portion of reviews (19,046) having a high rating between 4.80 and 5.00, while 8,618 reviews are rated between 1.00 and 1.20. The majority of reviews (33,962) have a 'thumbsUpCount' ranging from 0.00 to 96.05. The data is structured with distinct columns to capture comprehensive details of each review.

Usage

This dataset is ideal for: * Conducting data science and analytics to understand user sentiment and behaviour towards payment applications. * Performing natural language processing (NLP) tasks such as sentiment analysis, topic modelling, and keyword extraction from user review content. * Comparing the usability, user interface, and technical performance of Google Pay against other UPI payment applications in India. * Identifying common technical glitches and areas for improvement in the Google Pay app based on direct user feedback. * Assessing overall customer satisfaction and identifying factors influencing positive and negative app experiences.

Coverage

• Geographic Scope: The reviews are specific to users primarily located in India, reflecting the usage patterns and issues within the Indian UPI payment ecosystem.
• Time Range: The reviews span a period from 1st October 2019 to 18th November 2021.
• Demographic Scope: The dataset reflects the experiences of a diverse user base of the Google Pay app on the Google Play Store.

License

CC0

Who Can Use It

• Data Scientists and Analysts: For sentiment analysis, trend identification, and performance benchmarking of mobile applications.
• Product Managers: To gather user feedback, prioritise feature development, and address critical issues reported by users.
• UX/UI Designers: To gain insights into user interface preferences and usability challenges.
• Researchers: For academic studies on mobile payment trends, user behaviour, and app review analysis.
• Business Intelligence Professionals: For competitive analysis within the Indian mobile payment market.

Dataset Name Suggestions

• Google Pay User Reviews India
• Google Play Store GPay Feedback
• Indian Google Pay App Reviews
• Google Pay App Performance Data
• UPI Google Pay User Insights

Attributes

Original Data Source: UPI Payment Apps review - Google Play Store

This dataset contains some stats about google play store app.

There's a story behind every dataset and here's your opportunity to share yours. Based on installs, reviews you can sort out the apps. A clear picture can be drawn of apps, you can find out apps of what category are the most expensive, most popular, have most installs. Also various comparison can be done based on the data given in the dataset.

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 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.

The global mobile application store market is experiencing robust growth, driven by the increasing penetration of smartphones and rising mobile internet usage worldwide. While precise figures for market size and CAGR are absent from the provided data, a reasonable estimation, considering current market trends and the growth observed in related sectors like mobile gaming and app development, would place the 2025 market size at approximately $150 billion USD. Assuming a conservative Compound Annual Growth Rate (CAGR) of 15% for the forecast period (2025-2033), this translates to a projected market value exceeding $600 billion USD by 2033. This expansion is fueled by factors such as the continuous evolution of mobile technology, the emergence of 5G networks facilitating faster app downloads and smoother in-app experiences, and the ongoing diversification of app categories, including increased adoption of mobile commerce, subscription-based apps, and augmented/virtual reality applications. Key segments within the market are showing diverse growth trajectories. The Android OS segment is expected to continue its dominance due to its larger global market share, although the iOS segment will remain lucrative, driven by its higher average revenue per user. Similarly, while free apps maintain higher download numbers, the paid apps segment is poised for stronger revenue growth fueled by a willingness of users to pay for premium features and high-quality content. Geographical analysis reveals significant regional variations. North America and Europe currently hold substantial market share, but Asia-Pacific, particularly India and China, are predicted to exhibit the most rapid growth, as mobile penetration and app usage surge in these regions. Challenges such as app store regulations, security concerns around data privacy, and increasing competition among app developers will impact future growth.

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

Snapshot img

Enterprise App Store Market Size 2024-2028

The enterprise app store market size is forecast to increase by USD 4.59 billion at a CAGR of 18.51% between 2023 and 2028.

The market is witnessing significant growth due to the increasing need to enhance business efficiency and productivity. The integration of advanced technologies such as artificial intelligence (AI), analytics, and machine learning into enterprise resource planning (ERP) software, hybrid cloud, and other enterprise application software is driving market growth. Additionally, the integration of blockchain technology is expected to provide enhanced security and transparency to enterprise applications. Digital transformation is another key trend In the market, with organizations increasingly adopting mobile apps for various business functions, including logistics, e-commerce, CRM, and ERP. The integration of deep learning and AI in mobile applications is enabling predictive analytics and automation, leading to improved business outcomes.

What will be the Size of the Enterprise App Store Market During the Forecast Period?

Request Free Sample

The market continues to experience significant growth as large enterprises and Small and Medium-sized Enterprises (SMEs) increasingly adopt mobile application development for digitization. Internal app marketplaces have emerged as a key trend, enabling organizations to distribute and manage mobile applications internally. Bring Your Own Device (BYOD) policies have further fueled this trend, with asset teams facilitating the deployment of enterprise mobility solutions. Patent activity In the mobile application development space reflects the market's dynamic nature, with businesses seeking to protect their intellectual property. Version control and self-service mobile applications are also gaining traction, allowing for efficient management and customization of business applications.
Moreover, enterprise mobility solutions encompass both on-premise and cloud deployment models, catering to various organizational needs. The market spans various industries, including IT, retail and e-commerce, health and fitness, and more. Confidential information security and strong security features remain paramount, as enterprise app stores increasingly handle sensitive business data. While gaming, music and entertainment, and social networking apps may be popular consumer categories, the market primarily focuses on business applications. Android is a dominant platform, though other operating systems also find use in specific enterprise contexts. Overall, the market shows no signs of slowing down, as businesses continue to leverage mobile technology for increased productivity and efficiency.

How is this Enterprise App Store Industry segmented and which is the largest segment?

The enterprise app store industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premise


Type

  Large enterprise
  SME


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

The enterprise app market encompasses cloud-based and on-premises app stores, catering to the needs of large enterprises and SMEs across industries, including IT, BFSI, and retail. Cloud-based enterprise app stores dominate the market due to their ability to offer centralized control, automation, and optimization of business processes for global organizations. This trend is particularly prominent in developing countries with increasing SME presence, such as India and China. Mobile application development in areas like Android and iOS mobility solutions, version control, and customizability is a significant driver for enterprise mobility solutions. Security features, including multi-factor authentication, encryption, and tamper-proofing, are essential considerations for these platforms, ensuring confidential information remains secure during remote work.

Get a glance at the Enterprise App Store Industry report of share of various segments Request Free Sample

The cloud segment was valued at USD 1.77 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 31% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market is currently driven by significant inves

This dataset provides a collection of over 12,000 user reviews for various applications from an app store. It includes user-assigned ratings, which can be used to classify reviews as either positive or negative. The dataset is a valuable resource for conducting sentiment analysis tasks and can assist beginners in working with annotated, real-world data to understand user feedback on mobile applications. It serves as a foundation for exploring consumer sentiment and application performance insights.

Columns

• reviewId: A unique identifier assigned to each individual review.
• userName: The username of the person who submitted the review.
• userImage: The location of the image associated with the user who posted the review.
• content: The full text of the user's review.
• score: The rating given to the application by the user, ranging from 1 to 5, where a score of 5 indicates the most positive sentiment and 1 signifies the most negative.
• thumbsUpCount: The total number of users who have upvoted a particular review.
• reviewCreatedVersion: The specific version of the application that the review pertains to.
• at: The precise date and time when the review was originally posted.
• replyContent: Any reply provided to the original user review by the app developer or another party.
• repliedAt: The date and time when a reply to the review was posted.

Distribution

The dataset contains over 12,000 distinct reviews, with 12,495 unique review identifiers recorded. Ratings are distributed across the 1 to 5 scale, with significant counts for scores like 1.00-1.20 (2,506 reviews), 2.00-2.20 (2,344 reviews), 3.00-3.20 (1,991 reviews), 4.00-4.20 (2,775 reviews), and 4.80-5.00 (2,879 reviews). The number of upvotes (thumbsUpCount) for reviews spans a wide range, from 0 to 397. Many reviews (17%) do not specify a version, while '1.5.11' accounts for 4% of review versions. A substantial portion of reviews (53%) do not have a corresponding reply content. The data is typically provided in a CSV file format.

Usage

This dataset is ideally suited for a variety of analytical and machine learning applications. It is particularly useful for: * Performing sentiment analysis to gauge public opinion on mobile applications. * Developing and training natural language processing (NLP) models, such as BERT-based sentiment classifiers. * Extracting key insights and trends from user feedback to inform app development and marketing strategies. * Educating beginners in the field of sentiment analysis and text mining using annotated, real-world data. * Analysing user engagement and the impact of replies on review visibility.

Coverage

The dataset offers a global scope, encompassing reviews from users worldwide. The time range for user-posted reviews extends from 8th February 2015 to 28th October 2020. Replies to reviews cover a slightly broader period, from 14th January 2013 to 28th October 2020. The data reflects feedback from real users of various app store applications, providing a diverse demographic perspective on mobile app usage and satisfaction.

License

CCO

Who Can Use It

This dataset is beneficial for a wide range of users, including: * Data Scientists and Machine Learning Engineers: For building and evaluating sentiment analysis models, text classification systems, and other NLP applications. * Researchers: To study user behaviour, app success factors, and the dynamics of online reviews. * App Developers and Product Managers: To understand user feedback, identify pain points, and prioritise feature development based on sentiment. * Market Analysts: To monitor brand perception, conduct competitor analysis, and track market trends in the app industry. * Students: As an excellent practical resource for learning about data cleaning, text preprocessing, and sentiment analysis techniques.

Dataset Name Suggestions

• Google Play Store User Reviews
• Mobile App Sentiment Analysis Dataset
• App Store Ratings & Feedback
• Digital Product Review Data
• Consumer App Review Dataset

Attributes

Original Data Source: Google Play Store Reviews

The global market for App Data Statistics Tools is experiencing robust growth, driven by the increasing adoption of mobile applications across various sectors and the rising need for data-driven decision-making. This market, estimated at $2.5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors, including the escalating demand for precise user behavior analysis, the necessity for enhanced app performance optimization, and the growing importance of personalized user experiences. The market is segmented by tool type (customized vs. universal) and application (social, information, gaming, e-commerce, tools, and others). The rise of sophisticated analytics platforms offering comprehensive data visualization and insightful reporting contributes significantly to the market's growth. Furthermore, the increasing adoption of cloud-based solutions simplifies data storage and analysis, enabling businesses of all sizes to leverage app data effectively. Competitive forces are shaping the landscape, with established players and emerging startups continuously innovating to offer advanced features and cater to the diverse needs of developers and businesses. The North American market currently holds a significant share, largely due to the concentration of technology companies and early adoption of advanced analytics tools. However, Asia-Pacific is expected to exhibit the fastest growth during the forecast period, driven by the burgeoning mobile app market in countries like India and China. The market faces certain restraints, such as data privacy concerns and the complexity of integrating different analytics tools. Nevertheless, the continued evolution of mobile app technology, alongside the development of more user-friendly and cost-effective analytics platforms, will continue to propel market expansion over the next decade. This growth underscores the strategic value of app data analytics in understanding user behavior, improving app functionality, and ultimately maximizing business success in the competitive mobile landscape.

Mobile App Review Analysis Tools Market Outlook

The global market size for Mobile App Review Analysis Tools is expected to grow significantly, with a projected compound annual growth rate (CAGR) of 15% from 2024 to 2032. In 2023, the market size was estimated at USD 1.2 billion and is anticipated to reach USD 3.5 billion by 2032. The increasing demand for actionable insights from user reviews to enhance customer experience and product quality is a major growth factor in this market.

A key driver for the growth of the Mobile App Review Analysis Tools market is the escalating need for businesses to understand user sentiments and improve their products in real-time. As mobile apps become a critical touchpoint for customer engagement across various industries, companies are increasingly relying on review analysis tools to gather actionable insights from user feedback. These tools help organizations to not only respond to user concerns promptly but also to identify trends and areas for improvement, which can dramatically enhance user satisfaction and retention rates.

Another significant growth factor is the rise in mobile app usage globally, driven by the proliferation of smartphones and mobile internet. With millions of apps available on platforms like Google Play Store and Apple App Store, the competition among app developers is intense. App review analysis tools enable developers to stay ahead by monitoring competitor apps, analyzing feature requests, and understanding user behavior patterns. This competitive intelligence is crucial for making data-driven decisions that can lead to the development of superior apps and the implementation of effective marketing strategies.

Advancements in artificial intelligence (AI) and machine learning are also propelling the market forward. Modern app review analysis tools leverage AI to provide more accurate sentiment analysis, feature extraction, and trend prediction. These technologies allow for the automation of complex data analysis processes, making it easier for businesses to derive meaningful insights from large volumes of unstructured data. As AI and machine learning technologies continue to evolve, the capabilities of app review analysis tools are expected to become even more sophisticated, further driving market growth.

Regionally, North America holds the largest market share due to the high adoption rate of advanced technologies and the presence of numerous app developers and tech-savvy consumers. However, Asia Pacific is expected to witness the highest growth rate during the forecast period. The increasing penetration of smartphones, coupled with the growing number of app developers in countries like China and India, is driving the demand for app review analysis tools in this region. Additionally, government initiatives to support digitalization and the growth of mobile internet are further boosting market expansion in Asia Pacific.

Component Analysis

The Mobile App Review Analysis Tools market is segmented into two primary components: Software and Services. Software solutions dominate the market as they provide the essential platforms for analyzing large volumes of app reviews quickly and efficiently. These software solutions employ advanced algorithms and machine learning techniques to parse through user feedback, identify key trends, and provide actionable insights. They are crucial for businesses that aim to stay ahead of the competition by continuously improving their mobile apps based on user feedback.

Software solutions can be further categorized into various types, including standalone applications and integrated systems. Standalone applications offer specialized functionalities such as sentiment analysis, feature extraction, and trend prediction. These tools are designed to perform specific tasks with high accuracy and are preferred by businesses that require focused analysis. On the other hand, integrated systems combine multiple functionalities into a single platform, providing a comprehensive solution for app review analysis. These systems are ideal for large enterprises that need a holistic view of user feedback to inform their strategic decisions.

Services, the second component, include consulting, implementation, and support services. These services are essential for businesses that lack the in-house expertise to deploy and manage complex software solutions. Consulting services help organizations understand their specific needs and select the most appropriate tools, while implementation services ensure that the chosen solutions are seamlessly integrated into exis

The global mobile app distribution platform market is experiencing robust growth, driven by the ever-increasing adoption of smartphones and the expanding app ecosystem. The market, estimated at $150 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $500 billion by 2033. This significant expansion is fueled by several key factors. Firstly, the continuous rise in smartphone penetration, particularly in emerging markets, creates a vast pool of potential app users. Secondly, the increasing sophistication of mobile apps and the growing demand for diverse functionalities across various sectors, from gaming and entertainment to e-commerce and productivity, are driving downloads and in-app purchases. Furthermore, the evolution of in-app advertising and subscription models provides lucrative revenue streams for both app developers and distribution platforms. However, the market also faces challenges, including increasing competition among distribution platforms, the rising cost of app development and marketing, and regulatory concerns related to data privacy and security. Key players like Amazon, Apple, Google, Microsoft, and others are actively shaping the market landscape through strategic partnerships, acquisitions, and continuous innovation in their platforms. The market is segmented by platform type (e.g., app stores, third-party stores), operating system (Android, iOS), app category (gaming, utilities, social media etc.), and geography. While established players dominate the market, emerging regional players and innovative business models are creating opportunities for disruption. The competitive landscape is characterized by a blend of direct competition and strategic collaborations, reflecting the dynamic and evolving nature of the mobile app ecosystem. Future growth will depend on factors such as the successful integration of new technologies like 5G and AI, evolving user preferences, and effective monetization strategies.