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

In 2024, the United States was the leading app market, with the Apple App Store and the Google App Store generating approximately 31 billion U.S. dollars of in-app revenues. China was the second-largest app market, as in-app revenues in the region generated approximately 17.34 billion U.S. dollars. Japan ranked third, as the region generated around 11.25 billion U.S. dollars in app revenues for the examined period.

As of May 2024, 44 percent of the total revenues generated by the global app market came from subscriptions. Other monetization methods such as paid downloads and in-app purchases represented the most popular types of revenue streams for global app publishers. Overall, 56 percent of total app revenues came from other monetization methods.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

During the first quarter of 2024, YouTube shorts recorded the highest engagement rate across all short video platforms and in-app features analyzed. Content hosted on YouTube in form of shorts had an engagement rate of 5.91 percent, while TikTok reported an engagement rate of approximately 5.75 percent. Facebook Reels had an engagement rate of around two percent, making the platform rank last for short-format user engagement.

Between February 2023 and 2024, female mobile gamers worldwide spent an average of 21.6 minutes daily on word games, compared to only 20.9 minutes among male mobile gaming audiences. Male gamers in Latin America had the lowest daily user engagement with this genre.

Context

This dataset contains reviews for one of the most popular mobile app - tiktok. All the publicly posted reviews are scraped from the google play store.

Inspiration

• The dataset can be used to identify key insights related to the app, key problems/issues people have raised.
• Perform sentiment analysis of the reviews and find what people are talking about.
• Perform topic modeling to identify key topics mentioned in the review over time
• Generate visualizations of different worlds / n-grams / topics extracted from the reviews.

Five files are provided: 01_synthetic_data_model.r 02_app_data.rda 03_mean_center_da.rda 04_Bayesian_Tobit_II.stan 05_fit.RData File 01: This R file loads synthetic data and estimates a Bayesian Tobit II model with four outcomes. File 02: The dataset app_data.rda contains 15,481 observations of 100 users. It contains synthetic observations of all variables needed for the analyses. File 03: This dataset includes the means of the transformed variables in the original data, such that variables can be mean-centered analogously in the synthetic data. File 04: This .stan file provides the Bayesian Tobit II Stan code which is integrated into the R file (see File 01) as a stand-alone file in case it needs to be inspected with syntax highlighting or run on a different dataset. File 05: This file includes the output derived from fitting the Stan model on the synthetic data (i.e., a so-called "stanfit object"). If one does not want to run the model, one can directly import this file in R. The readme.txt describes details of the replication files.

Context

To date (April 2020), Android is still the most popular mobile operating system in the world. Taking into account billion of Android users worldwide, mining this data has the potential to reveal user behaviors and trends in the whole global scope.

Content

There are 2 CSV files: - app.csv with 53,732 rows and 18 columns. - comment.csv with 1,468,173 rows and 4 columns.

The scraping was done in April 2020.

Acknowledgements

This dataset is obtained from scraping Google Play Store. Without Google and Android, this dataset wouldn’t have existed.

The dataset is first published in this blog.

Inspiration

Business trends on mobile can be explored by examining this dataset.

In August 2024, over half a million unique devices used the Chinese AI tool Aishenqi. Artificial intelligence tools include a broad range of artificial intelligence services. China's leading AI tools include code writing support, as well as a digital language study companion.

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.

Request Free Sample

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.

Request Free Sample

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

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This dataset explains the subsidy status of the SMEs Mobile Smart Application Project over the years, providing relevant information such as subsidy recipients, subsidy amounts, affiliated municipalities or counties, and approval dates, providing a reference for the industry to promote mobile smart application.

The Yelp dataset is a subset of businesses, reviews, and user data for use in personal, educational, and academic purposes. It contains 6.9M online reviews for 150k businesses. It also includes more than 200,000 images related to the reviews.

The data consists of multiple sub datasets:

1. Yelp Business data: Contains business data including location data, attributes, and categories.
2. Yelp Review data: Contains full review text data including the user_id that wrote the review and the business_id the review is written for.
3. Yelp User data: User data including the user's friend mapping and all the metadata associated with the user.
4. Yelp Checkin data: Checkins on a business.
5. Yelp Tip data: Tips written by a user on a business. Tips are shorter than reviews and tend to convey quick suggestions.
6. Yelp Photo data: Contains photo data including the caption and classification (one of "food", "drink", "menu", "inside" or "outside").

Available as JSON files, use can use it to teach students about databases, to learn NLP, or for sample production data while you learn how to make mobile apps.

The Yelp dataset is a subset of our businesses, reviews, and user data for use in connection with academic research. Available as JSON files, use it to teach students about databases, to learn NLP, or for sample production data while you learn how to make mobile apps.

• 6,990,280 reviews
• 150,346 businesses
• 200,100 pictures
• 11 metropolitan areas
• 908,915 tips by 1,987,897 users
• Over 1.2 million business attributes like hours, parking, availability, and ambience
• Aggregated check-ins over time for each of the 131,930 businesses

Terms of Use

The Get It Done program allows residents and visitors to report certain types of non-emergency problems to the City using the Get It Done mobile app, web app, or by telephone. This dataset contains all Get It Done reports the City has received since the program launched in May 2016. New! We have reorganized the data into a single file of currently open reports and closed reports by year. Users who would prefer to get reports by problem type should refer to the datasets for: 72-hour parking violations Graffiti Illegal Dumping Potholes The scope of this data is limited to information from the reports citizen users submit through Get It Done. The data includes fields for the date and time a report was submitted, what the problem was, the location of the problem, and the date when the user was notified that the City addressed the problem. This data does not include details about any work performed to fix a problem or the date and time work was completed. Reports that are referred outside of the Get It Done system have a status of “Referred”. Please note that this data includes every user-submitted report and should not be considered an official record of City maintenance work. For example, users might submit problems that have already been reported, that are the responsibility of another government agency or private business, that cannot be found or verified, or that are already scheduled to be fixed in a long-term maintenance plan. The details about how the City addressed each report are outside of the scope of this dataset. If you have any questions about this data, please contact pandatech@sandiego.gov. If you have questions about your Get It Done report, please refer to your confirmation email.

This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can create a capture-recapture dataset about devices, that is, users, that “visited” the business: the units are owners of mobile devices and the capture occasions are time intervals such as days. A unit is captured when she activates an application, provided that this activation is recorded by the platform providing the data. Statistical capture-recapture techniques can be applied to the app data to estimate the total number of users that visited the business over a time period, thereby providing an indirect estimate of foot traffic. This article argues that the robust design, a method for dealing with a nested mark-recapture experiment, can be used in this context. A new algorithm for estimating the parameters of a robust design with a fairly large number of capture occasions and a simple parametric bootstrap variance estimator are proposed. Moreover, new estimation methods and new theoretical results are introduced for a wider application of the robust design. This is used to analyze a dataset about the mobile devices that visited the auto-dealerships of a major auto brand in a U.S. metropolitan area over a period of 1 year and a half. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Techsalerator’s Business Technographic Data for Vietnam: Unlocking Insights into Vietnam's Technology Landscape

Techsalerator’s Business Technographic Data for Vietnam provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Vietnam. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Vietnam, enabling technology vendors to target potential clients and allowing analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as accounting systems, customer management software, and cloud services. Understanding a company's technology stack is key to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can use this information to assess the level of technology adoption and interest among companies in Vietnam.

• Industry Sector: This field specifies the industry in which the company operates, such as manufacturing, retail, or finance. Knowing the industry helps vendors tailor their products to sector-specific demands and emerging trends in Vietnam.

• Geographic Location: This field identifies the company's headquarters or primary operations within Vietnam. Geographic information aids in regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Vietnam

• E-commerce Expansion: With a rapidly growing digital consumer base, Vietnamese companies are increasingly investing in e-commerce platforms, digital marketing, and online payment systems to capture a larger market share and enhance customer experience.

• Fintech Innovations: Vietnam’s fintech sector is experiencing significant growth, with businesses adopting advanced financial technologies such as mobile payment solutions, digital wallets, and blockchain to improve financial transactions and services.

• Smart Manufacturing: The manufacturing sector in Vietnam is embracing Industry 4.0 technologies, including automation, IoT, and AI-driven analytics, to enhance productivity, efficiency, and competitiveness in the global market.

• Cloud Computing and SaaS: Cloud-based solutions and Software-as-a-Service (SaaS) offerings are gaining traction, providing Vietnamese businesses with scalable and flexible IT infrastructure that supports remote work and digital transformation initiatives.

• Cybersecurity Enhancements: As digital activities increase, so does the need for robust cybersecurity measures. Companies in Vietnam are investing in advanced security solutions, including threat detection systems and data protection tools, to safeguard their operations and customer data.

Top 5 Companies with Notable Technographic Data in Vietnam

• Vietcombank: A leading financial institution, Vietcombank is implementing cutting-edge digital banking solutions, including mobile banking apps and secure online transaction systems, to enhance customer service and operational efficiency.

• Vingroup: As a major conglomerate, Vingroup leverages advanced technologies across its diverse business segments, including real estate, retail, and healthcare, integrating smart technologies and digital platforms into its operations.

• FPT Corporation: A major IT services and software development company, FPT is at the forefront of digital transformation in Vietnam, offering solutions in cloud computing, AI, and cybersecurity to both domestic and international clients.

• Masan Group: A leading consumer goods and retail company, Masan Group is adopting digital tools and e-commerce platforms to optimize its supply chain, enhance customer engagement, and drive business growth.

• VNPT: Vietnam’s largest telecommunications provider, VNPT is expanding its network infrastructure and investing in advanced technologies such as 5G and IoT to improve connectivity and support the digital economy.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Vietnam, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access ...

Goodwill-and-Other-Intagible-Assets Time Series for Allgeier SE. Allgeier SE provides information technology (IT) solutions and software services in Germany. It operates in two segments, Enterprise IT and mgm technology partners. The company provides software lifecycle services, nearshore-/offshore delivery, big data / business intelligence, industry solutions and cloud, managed services & app management, mobile enterprise/apps, process and IT consulting, IT security, enterprise content management, and IT infrastructure services. It is also involved in designing, developing, launching, and operating business software solutions, such as document management; enterprise resource planning; e-commerce, business process management; business digitalization platform and business efficiency solutions; IT services and open-source software development; consultancy, software solutions; cloud transformation and cloudnative application development, as well as provides field service and asset management. The company is engages in Management consultancy and digital consulting, Business analysis and requirements engineering, software modelling and development, design and usability, web and application security, quality assurance, testing automation, SAP integration, process optimization, cloud services. Allgeier SE was founded in 1977 and is headquartered in Munich, Germany.