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.

This dataset provides detailed, event-level records of mobile app feature usage, including user interactions, device context, session information, and user segmentation. It enables product teams and UX researchers to analyze feature adoption rates, engagement patterns, and user cohorts, supporting data-driven decisions for app improvement and user experience optimization.

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 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.

This dataset provides detailed, user-level engagement and transaction metrics for a fintech app, including feature usage, transaction volumes, device types, and premium status. It enables startups to analyze user behavior, optimize product features, and track engagement trends for targeted improvements and growth strategies.

It's the raw data of iOS game app. It's included postback events which in-app game environment.

I'd like to share the small dataset that you could practice how to optimize the mobile game using in-app data. I believe that it may help you to practice if you're just a beginner or never seen such a dataset before. You could simulate how to optimize the in-app game data. Which channel is good or bad.

Let's think about it if you are working at a game company and you are a game performance marketer which channel should be optimized ASAP? You could practice following the below KPI. Good Luck!

[Information of App] Publisher: - Category: Game OS: iOS Language: Korean Age: 12+ Price: Free

[Information of Event Postback] open: app open af_complete_registration: registration join_the_guild: join the guild purchase: purchase af_level_5_achieved: achieved level 5 af_level_8_achieved: achieved level 8 af_level_10_achieved: achieved level 10 af_level_15_achieved: achieved level 15 af_level_20_achieved: achieved level 20 auto_play : after level5, it’s allowed autoplay

=====Collect data(.csv file)===== - Raw data (1)channel_event.csv (2)d1.csv

• Pivoting data (using raw data) (1)gagong.csv

[Information of channel_event.csv] event: postbacked event name channel: channel name country: country language: language os: mobile phone operating system device: mobile device

[Information of d1.csv] channel: channel name install: counted installs day1: day 1 retention

[Information of gagong.csv] channel: channel name install: counted installs af_complete_registration: once a user completed the registration it's pushed(counted) af_level5_achieved: achieved level5 af_level8_achieved: achieved level8 af_purchase day1: purchase nru: new registered user rate Lv5: achieved level 5 rate purchase_rate: purchase rate day1_retention: day 1 retention rate

Unlock valuable insights with the Google Play Store Android Apps Dataset in CSV format, featuring detailed information on over thousands of Android apps available on the Google Play Store. This comprehensive dataset includes key attributes such as App Name, App Logo, Category, Description, Average Rating, Ratings Count, In-app Purchases, Operating System, Company, Content Rating, Images, Email, Additional Information, and more.

Perfect for market researchers, data scientists, app developers, and analysts, this dataset allows for deep analysis of app performance, user preferences, and industry trends. With data on app descriptions, content ratings, in-app purchases, and company information, you can track trends in the mobile app market, evaluate user satisfaction, and conduct competitive analysis.

The dataset is ideal for businesses looking to optimize app strategies, enhance user experience, and improve app performance based on real user feedback. Easily import the data into your favorite analysis tools to gain actionable insights for your app development or research.

With regularly updated data scraped directly from the Google Play Store, the Google Play Store Android Apps Dataset is an invaluable resource for anyone looking to explore trends, track performance, or enhance their app strategies.

Notifications are important for the user experience in mobile apps and can influence their engagement. However, too many notifications can be disruptive for users. In this work, we study a novel centralized approach for notification optimization, where we view the opportunities to send user notifications as items and types of notifications as buyers in an auction market.

The full dataset, instagram_notification_auction_base_dataset.csv, contains all generated notifications for a subset of Instagram users across four notification types within a certain time window. Each entry of the dataset represents one generated notification. For each generated notification, we include some information related to the notification as well as information related to the auctions performed to determine if the generated notification can be sent to users. See the README file for detailed column decriptions. The dataset was collected during an A/B test where we compare the performance of the first-price auction system with that of the second-price auction system.

The two derived datasets can be useful to study fair online allocation and Fisher market equilibrium. See the README for details and a link to the scripts that generate the derived datasets.

Snapshot img

Mobile Application Market Size 2025-2029

The mobile application market size is valued to increase USD 2630 billion, at a CAGR of 31.1% from 2024 to 2029. Growing penetration of smartphones will drive the mobile application market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 39% growth during the forecast period.
By Platform - Android market segment was valued at USD 236.40 billion in 2023
By Type - Gaming segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 978.60 billion
Market Future Opportunities: USD 2630.00 billion
CAGR from 2024 to 2029 : 31.1%

Market Summary

The market represents a dynamic and continually evolving landscape, driven by the increasing penetration of smartphones and the growing number of mobile apps for IoT devices. Core technologies, such as artificial intelligence and machine learning, are revolutionizing application development and usage, while service types like mobile app testing and analytics are becoming essential components of the mobile app ecosystem. The cost associated with mobile app development and operation continues to be a significant challenge for businesses, yet the opportunities for innovation and engagement are immense.
According to recent estimates, over 51% of all internet traffic comes from mobile devices, underscoring the importance of a strong mobile application presence for businesses seeking to reach and engage their customers effectively.

What will be the Size of the Mobile Application Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Mobile Application Market Segmented?

The mobile application industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Platform

  Android market
  iOS market
  Others


Type

  Gaming
  Music and entertainment
  Health and fitness
  Social networking
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Platform Insights

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

In the dynamic and evolving market, location-based services have gained significant traction, enabling users to access customized content based on their geographical location. User authentication systems ensure secure access to applications, while user interface design and software testing methodologies ensure seamless user experiences. Database management systems and mobile analytics platforms facilitate data-driven decision-making, while backend infrastructure and application performance management optimize application functionality. The market embraces various development methodologies, including the waterfall development method, cloud computing services, and agile development process. Payment gateway integration and in-app purchase systems facilitate monetization strategies. Software development kits, application performance monitoring, and app development lifecycle tools streamline the development process.

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The Android market segment was valued at USD 236.40 billion in 2019 and showed a gradual increase during the forecast period.

User interaction design and mobile UI design focus on enhancing user experience, while mobile app monetization strategies cater to diverse revenue models. Hybrid mobile development, responsive web design, frontend development, and data encryption methods ensure versatility and security. Software deployment strategies, cross-platform development, version control systems, and code repository management enable efficient development and maintenance. Scalable architecture, native mobile development, push notification services, and application security testing ensure robustness and reliability. As of 2023, approximately 60% of Android users access the Google Play Store, with adoption growing by 18%. Future industry growth is expected to reach 25%, driven by the increasing demand for mobile applications across various sectors.

The Android operating system, with its vast user base and versatile development tools, continues to dominate the market.

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Regional Analysis

APAC is estimated to contribute 39% 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.

See How Mobile Application Market Demand is Rising in APAC Request Free Sample

The Asia-Pacific (APAC) region dominate

App Store Optimization AI Market Outlook

According to our latest research, the App Store Optimization (ASO) AI market size reached USD 1.35 billion in 2024 globally, demonstrating robust expansion driven by the rapid proliferation of mobile applications and the increasing need for data-driven visibility strategies. The market is forecasted to grow at a CAGR of 19.2% from 2025 to 2033, reaching an estimated USD 6.17 billion by 2033. This exceptional growth trajectory is propelled by the integration of advanced artificial intelligence algorithms in ASO tools, enabling app developers, enterprises, and marketers to optimize app discoverability, improve conversion rates, and gain actionable insights in an increasingly competitive digital ecosystem.

Several factors are fueling the growth of the App Store Optimization AI market. The exponential increase in the number of mobile applications across platforms such as iOS and Android has made it imperative for app publishers to leverage sophisticated optimization techniques. Traditional ASO methods are being rapidly supplanted by AI-powered solutions that analyze vast datasets, identify trending keywords, and optimize app metadata in real time. The demand for enhanced organic app discovery, reduced customer acquisition costs, and improved app lifecycle management is driving widespread adoption of AI-driven ASO tools among developers and enterprises alike. Moreover, the surge in mobile-first digital consumption patterns, especially in emerging economies, is further accentuating the need for scalable and automated ASO solutions.

Another significant growth factor is the evolving complexity of app store algorithms and the increasing emphasis on user experience metrics. App stores are continuously refining their ranking algorithms, factoring in parameters such as user retention, engagement, and conversion rates. AI-powered ASO platforms are uniquely equipped to decipher these multifaceted ranking signals, allowing app publishers to adapt their strategies dynamically. The integration of machine learning and natural language processing enables these platforms to provide personalized recommendations for keyword optimization, creative asset testing, and performance analytics. As a result, organizations are able to achieve higher visibility, increased downloads, and improved return on investment, further accelerating the expansion of the App Store Optimization AI market.

The proliferation of mobile commerce, gaming, and utility applications has intensified competition within app marketplaces, necessitating more sophisticated optimization approaches. AI-enabled ASO solutions empower marketers and developers to conduct granular competitor analysis, automate A/B testing, and derive actionable insights from user reviews and behavioral data. This data-centric approach not only enhances app visibility but also drives sustained user engagement and monetization. Additionally, the growing availability of cloud-based ASO platforms has democratized access to advanced optimization tools, enabling small and medium-sized enterprises to compete effectively with larger organizations. The increasing collaboration between ASO solution providers and digital marketing agencies is also contributing to the overall growth and innovation within the market.

From a regional perspective, North America currently dominates the App Store Optimization AI market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of major technology companies, high smartphone penetration, and early adoption of AI-driven marketing technologies have positioned North America as a key growth engine for the market. Meanwhile, Asia Pacific is witnessing the fastest growth rate, driven by the rapid expansion of the mobile app ecosystem in countries such as China, India, and Southeast Asia. The region’s large and digitally savvy population, coupled with increasing investments in AI and cloud infrastructure, is expected to propel significant market expansion in the coming years. Europe is also experiencing steady growth, underpinned by stringent data privacy regulations and the rising demand for localized ASO solutions.

Component Analysis

The App Store Optimization AI market by component is bifurcated into software and services, each playing a pivotal role in shaping the industry landscape. The software segment encompasses a wide array of AI-powered tools and platforms designed to a

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.

The app analytics market, valued at $7.29 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 21.09% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of mobile applications across diverse industries, coupled with the rising need for businesses to understand user behavior and optimize app performance, fuels the demand for sophisticated analytics solutions. Furthermore, advancements in data analytics technologies, including artificial intelligence (AI) and machine learning (ML), are enabling more insightful and actionable data analysis, further propelling market expansion. The diverse application of app analytics across marketing/advertising, revenue generation, and in-app performance monitoring across various sectors like BFSI, e-commerce, media, travel and tourism, and IT and telecom significantly contributes to this growth. The market is segmented by deployment (mobile apps and website/desktop apps) and end-user industry, with mobile app analytics currently dominating due to the widespread adoption of smartphones. The competitive landscape is characterized by a mix of established technology giants like Google and Amazon alongside specialized app analytics providers like AppsFlyer and Mixpanel. These companies are continuously innovating, integrating new technologies, and expanding their product offerings to cater to the evolving needs of businesses. While the North American market currently holds a significant share, the Asia-Pacific region is expected to witness substantial growth in the coming years driven by increasing smartphone penetration and digitalization initiatives. However, factors like data privacy concerns and the rising complexity of integrating various analytics tools could pose challenges to market growth. Nonetheless, the overall outlook for the app analytics market remains positive, indicating substantial opportunities for players across the value chain. Recent developments include: June 2024 - Comscore and Kochava unveiled an innovative performance media measurement solution, providing marketers with enhanced insights. This cutting-edge cross-screen solution empowers marketers to understand better how linear TV ad campaigns impact both online and offline actions. By integrating Comscore’s Exact Commercial Ratings (ECR) data with Kochava’s sophisticated marketing mix modeling, the solution facilitates the measurement of crucial metrics, including mobile app activities (such as installs and in-app purchases) and website interactions., June 2024 - AppsFlyer announced its integration of the Data Collaboration Platform with Start.io, an omnichannel advertising platform that focuses on real-time mobile audiences for publishers. Through this collaboration, businesses leveraging the AppsFlyer Data Collaboration Platform can merge their Start.io data with campaign metrics and audience insights, creating a more comprehensive dataset for precise audience targeting.. Key drivers for this market are: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Potential restraints include: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Notable trends are: Media and Entertainment Industry Expected to Capture Significant Share.

Install App dataset provides comprehensive, first-party app install intelligence across the APAC region, sourced from AI-driven OS-level keyboard and utility applications. It captures highly granular insights into mobile app installations, updates, and user behavior, enabling precise market analytics, attribution tracking, and growth optimization.

Each record includes hashed device and advertising identifiers, application metadata (package name, app version, category), and timestamped install/update events. The field is_new_install indicates whether the app installation is first-time or an existing reinstall/update, helping distinguish between new user acquisition and returning user activity — a critical signal for campaign performance and user lifecycle analytics.

Alongside app-level insights, the dataset provides detailed device intelligence — including manufacturer, model, OS type/version, language, and user agent — combined with IP-based location data (country, region, city) and daily server timestamps for freshness tracking.

All data is hashed, privacy-compliant, and refreshed daily, making it ideal for organizations seeking high-quality, real-world app install signals across Android and iOS ecosystems.

📊 Key Features • First-party, consented data from OS-level applications • Hashed identifiers (device_id, advertising_id) for privacy-safe integration • Install and update timestamps for temporal and behavioral analysis • is_new_install flag to separate new installs from reinstalls or app updates • Comprehensive app, device, and location attributes • Daily refreshed dataset ensuring data accuracy and timeliness

⚙️ Primary Use Cases • Mobile Attribution & User Acquisition Tracking – Identify new users vs. re-engaged ones via the is_new_install flag • Market Intelligence & Competitive Benchmarking – Analyze install trends across app categories and geographies • Audience Segmentation – Classify users by device type, OS version, and app install behavior • Ad Targeting Optimization – Refine lookalike and re-engagement audiences with verified install data • Product & Growth Analytics – Study retention, uninstall rates, and user churn patterns • App Store Strategy – Evaluate app update frequency and version distribution

📍 Industries Benefiting • Ad-Tech & Mar-Tech Platforms • Mobile App Publishers & Developers • Telecom Operators & Device OEMs • Market Research & Analytics Firms • E-commerce, Fintech & Gaming Companies • Media, Entertainment & OTT Platforms

With millions of verified app installs tracked across Android and iOS, this AI-powered, consent-based dataset delivers actionable insights into app discovery, engagement, and retention, driving smarter decisions in mobile marketing, audience intelligence, and growth analytics.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

This dataset provides granular records of mobile data sessions from 4G LTE and 5G networks, including session timing, data volume, subscriber identifiers, network quality, and detected application/service types. It is ideal for telecom analytics, network optimization, billing, and user behavior studies, offering deep insights into mobile data usage patterns and service performance.

This first-party, anonymized mobile app usage dataset provides session-level behavioral intelligence across millions of devices in the APAC region. Designed for AdTech and MarTech applications, it delivers deep insights into how users interact with apps, their install and engagement behavior, device characteristics, and location patterns — all refreshed daily and privacy-compliant.

Core Features • MAID-based behavioral dataset with detailed app install, session, and engagement insights. • Location intelligence (country, region, city-level) for geo-based targeting. • Device intelligence (model, OS, carrier, user agent) for premium vs budget segmentation. • Freshness: Daily refreshed, session-level data. • Consent-first data collection, anonymized and compliant with GDPR/CCPA.

🎯 Key Use Cases 1. Precision Audience Building • Build custom segments based on real app usage and session frequency. • Example: “Users with 20+ sessions on Swiggy, Zomato & Blinkit in metro cities.” • Identify cohorts like brand switchers, category enthusiasts, or premium buyers. 2. Media Planning & Reach Forecasting • Estimate addressable audience size per app or category. • Cross-app overlap analysis (e.g., “60% of Hotstar users also have Prime Video”). • City or region-level reach availability for campaign planning. 3. Competitive Intelligence • Track competitor app adoption and engagement over time. • Measure user migration and churn trends between brands. • Generate market share insights based on install base. 4. Campaign Optimization • Build lookalike audiences from high-value converters. • Enable retargeting based on recency and frequency of app usage. • Exclude audiences already using a client’s app. 5. Creative Optimization • Analyze language preferences, device segments, and time-of-day usage to localize creatives and optimize ad delivery windows.

🏆 Competitive Advantages • Broader visibility than walled gardens like Meta or Google. • Richer insights than survey or panel data — 110M+ users vs. 100K samples. • Pre-install intent signals not captured by MMPs. • Real-time, session-level granularity unavailable in aggregator datasets.

🌍 Industries Served • Advertising & Media Agencies • DSPs & Ad Tech Platforms • Consumer Insights & Analytics Firms • Brand Marketing Teams • Market Research Companies

Leverage the most reliable and compliant global mobility and foot traffic dataset on the market. Veraset Movement (Mobile Device GPS Mobility Data) offers unparalleled real-time insights into footfall traffic patterns globally.

Covering 200+ countries, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement.

Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's mobile location data helps in shaping strategy and making data-driven decisions.

Veraset Global Movement panel (mobile location) includes: - 1.8+ Billion Devices Monthly - 200 Billion Pings Monthly Device and Ping counts by Country are available upon request

Common Use Cases of Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Please visit: https://www.veraset.com/docs/movement for more information and schemas

Goodwill-and-Other-Intagible-Assets Time Series for Applovin Corp. AppLovin Corporation engages in building a software-based platform for advertisers to enhance the marketing and monetization of their content in the United States and internationally. It operates through two segments, Advertising and Apps. The company offers AppDiscovery, an advertising solution, which matches advertiser demand with publisher supply through auctions; MAX, an in-app bidding technology that optimizes the value of a publisher's advertising inventory by running a real-time competitive auction; Adjust, a measurement and analytics marketing platform that provides marketers with the visibility, insights, and data needed to scale their apps marketing; and Wurl, a connected TV platform, which distributes streaming video for content companies and provides advertising and publishing solutions through its AdPool, TVBits, BrandDiscovery, ContentDiscovery, and Global FAST Pass products. It also provides SparkLabs, which uses app store optimization to enhance ad visibility; AppLovin Exchange, which connects buyers to mobile and CTV devices through a single RTB exchange; and Array, an end-to-end app management suite for mobile operators and end users. In addition, the company operates various free-to-play mobile games through its own or partner studios. It serves individuals, small and independent businesses, enterprises, advertisers and advertising networks, mobile app publishers, and indie studio developers. AppLovin Corporation was incorporated in 2011 and is headquartered in Palo Alto, California.

This dataset was collected and curated as part of an ongoing PhD research project focusing on user-centered requirement analysis and satisfaction modeling in Saudi mobile banking applications. It contains raw customer review data, including the review date, source store (Google Play or App Store), bank name, textual review content, and user rating (1–5 scale).

As a PhD student researcher, I intend to release an updated and refined version of this dataset in the future that will include additional processed attributes such as sentiment polarity, user intent, Kano classification (Must-Be, Performance, Attractive), and structural metrics derived from ontology-based analysis.

This initial version serves as a foundational resource for researchers interested in sentiment analysis, feature prioritization, or satisfaction–achievement modeling within the context of Saudi mobile banking services.

An application, optimized for mobile devices, used by public health staff to visualize coronavirus cases in their community.