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

Get access to information about all apps in the Google Playstore to understand your competitors, market to app developers etc. This dataset includes all the fields available in the play store such as:

• Name, description, rating information etc.
• Technical information such as size, app version etc.
• Permissions.
• Developer information.
• Contact information.
• Parsed app-ads.txt information for publisher domains.
• Reviews (more than 100 million reviews available)

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

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

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

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

This dataset encompasses 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.

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

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

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

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

The App Store Data dataset includes all key app details:

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

A vast collection of data which includes the Top 100 Free Applications in the iOS App Store for each day since February 2024.

Features:

• Date of chart
• Rank
• App name
• App identifier
• Chart collection

Usage:

Market trend analysis, business strategy development.

Coverage:

This will cover the top free app chart in the UK iOS App store.

License:

CCO

Who can use it:

Product Owners or Project Managers can use this data set.

How to use it:

The data set could be used to track specific applications and their position within the App store chart over time.

Context

We a group of students from a Data Science and Machine Learning Bootcamp in which we have decided to create an AI logo generator for our capstone project. We need huge amount of logos for training our deep learning model - DCGAN so we have scraped over 365k data from the Apple App Store to download the logos of the apps for training purposes.

GitHub Link of our Project - LOGO⅃ : https://github.com/jackychansky/Logo-Generator-by-DCGAN/blob/main/README.md

Content

We have used Rapid API to acquire the data we need and we have scraped over 10,000,000 apps infomation (link to the dataset: https://www.kaggle.com/fentyforte/365k-ios-apps-dataset) thus downloading all the logos from the logolink scraped from the dataset to create the current large logo dataset.

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

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 ---

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

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.

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 ---

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd7e02bf38f4b08df2508d6b6e42f3066%2Fchatgpt2.png?generation=1700233710310045&alt=media" alt="">

Based on their wikipedia page

ChatGPT (Chat Generative Pre-trained Transformer) is a large language model-based chatbot developed by OpenAI and launched on November 30, 2022, that enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. Successive prompts and replies, known as prompt engineering, are considered at each conversation stage as a context.

These reviews were extracted from Google Store App

Usage

This dataset should paint a good picture on what is the public's perception of the app over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of the app had the most positive feedback, the worst.
3. Use topic modeling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator

The TousAntiCovid app TousAntiCovid is an application that allows everyone to be an actor in the fight against the epidemic. This is an additional barrier gesture that is activated at all times when you have to redouble your vigilance: at the restaurant, in the canteen, when you go to a gym, when you participate in a professional event, when there is a risk that not everyone will respect the other barrier gestures. TousAntiCovid complements the action of doctors and sickness insurance, aimed at containing the spread of the virus by stopping the chains of contamination as soon as possible. The principle is as follows: prevent, while guaranteeing anonymity, people who have been close to a person tested positive, so that they can get tested and taken care of as soon as possible. It also makes it possible to stay informed about the evolution of the epidemic and the conduct to be held and thus to remain vigilant and adopt the right actions. It allows easy access to other tools available to citizens wishing to be involved in the fight against the epidemic: DepistageCovid which gives map of nearby labs and wait times and MesConseilsCovid which provides personalised advice to protect and protect others. The installation of the TousAntiCovid app is done on a voluntary basis. Everyone is supported even if they choose not to use the app. The app is downloaded from the Apple Store and Google Play: Hello.tousanticovid.gouv.fr/ ### Description of the data This dataset informs for each day since the launch of the application on 2 June 2020: — Cumulative total of the number of registered applications minus the number of deregistrations. — Cumulative total of users notified by the application: the number of users notified by the application as risk contacts following exposure to COVID-19, since 2 June 2020. — Cumulative total of users reporting as COVID-19 cases per day: the number of users who reported as COVID-19 cases in the application, since 2 June 2020.

In this table you will find information about CoronaMelder. This concerns two variables: 1. The number of people who downloaded CoronaMelder 2. The number of people who warned others via CoronaMelder 1. The number of downloads is based on data from: - App Store (iOS) - Play Store (Android) - Huawei App Gallery (Android) 2. If you have tested positive for corona, you can voluntarily indicate this in the app, together with an employee of the GGD. The numbers show how many people have done this.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2F186cf4f6172ca2c696819b7b09931bd3%2Fimage3.jpg?generation=1584955857130173&alt=media" alt="">

The presence of business in the digital space is a must now. Indeed, there’s hardly any company, be it a small startup or an international corporation, that wouldn’t be available online. For this, the company may use one of two options — to develop an app or a website, or both.

In the case of a limited budget, business owners often have to make a choice. Thus, considering that mobile traffic bypassed the desktop’s in 2016 and continues to grow, it becomes obvious that the business should become accessible and convenient for smartphone users. But what is better a responsive website or a mobile application?

Entrepreneurs often turn to development companies to ask this question. Lacking sufficient knowledge, they hope to get answers to their questions from people with experience in this field. So, we decided to compile a guide that will give you clear and understandable information.

Mobile app

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2F0541557795519f24d812f78dfb51867e%2Fimage4.png?generation=1584955894277647&alt=media" alt="">

Let's look at the stats. It will help you understand why a mobile app may be the obvious choice for your client.

In 2019, smartphone users installed about 204 billion(!) applications on their devices. On average, this is more than 26 applications per inhabitant of the planet Earth. And if this is not enough evidence, here’s one more point. The expected revenue of mobile applications will be $189 billion in 2020.

It sounds impressive, but this does not mean that a mobile application is something indispensable for every business. Not at all. Let's go through the pros and cons of a mobile application and try to understand when it is needed.

Pros

• A new level of interaction. Mobile applications are a more convenient method of interaction. They load and process content faster. One more useful feature is notifications. Perhaps, applications are the best way to inform users about new updates, promotions, and other news (who will read long letters in the mail?).
• Personalized targeting. Mobile applications are ideal for products or services that need to be used on an ongoing basis. The options like creating accounts, entering profile information, etc., make applications more personalized than websites. All this allows the business to target their audience more accurately without wasting money.
• Offline usage. That’s another major advantage. Applications can provide users with access to content without an internet connection.

Cons

• Development costs. In order to reach the maximum audience with a mobile app, it is necessary to cover two main operating systems — iOS and Android. Development for each OS can be too expensive for small business owners and they will have to make difficult choices. The way out of this situation is cross-platform development. Why? Because there’s no need to guess which platform targets prefer using — iOS or Android. Instead, you create just one app that runs seamlessly on both platforms.

• Maintenance. The application is a technical product that needs constant support. Upgrades should be carried out in a timely manner. Often, users need to personally update applications by downloading a new version, which is annoying. Regular bug-fixing for various devices (smartphones, tablets) and different operating systems might be a real problem. Plus, any update should be confirmed by the store where the application is placed.

• Suitable for businesses that provide interactive and personalized content (refers to all lifestyle and healthcare solutions), require regular app usage (for instance, to-do lists), rely on visual interaction and so on. For games, like Angry Birds, creating an app is also a wise choice.

Website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4686357%2Fd4f5bf1fdd0d0e65fae38c7251f56f13%2Fimage1.jpg?generation=1584955919738648&alt=media" alt="">

In order to be convenient for users of mobile devices, a website should be responsive. We want to make an emphasis on this since it is critically important. Most of the traffic on the Internet comes from mobile devices, so your website should be adaptable, or in other words, mobile-friendly. If a mobile user needs to zoom in all the necessary elements and text to see something, they will immediately quit your website.

On the other hand, a responsive website has the following benefits.

Pros

• Maintenance. Maintaining a website is less costly. When compared to applications where the user mu...

Introducing the new and improved Main Roads GPS-SLK app, putting network location accuracy at your fingertips. The GPS-SLK app is backed by a dedicated support team and offers a number of functional benefits, which will continue to grow and evolve to meet future demands.Its features include: •Compatibility with iOS and Android (download anytime via the App Store or Google Play) •Location data for State and Local roads •Location data for cycle paths •Offline usage when GPS is enabled (no data, no worries) •Improved location sharing functionality with photo capture •Improved data update notifications Make sure to contact our team with any feedback, so we can keep improving the app! See Frequently Asked Questions for more information.