Analysis of Apps in the Google Play Store

Posted on Aug 8, 2018

Android is the dominant mobile operating system today with about 85% of all mobile devices running Google’s OS. The Google Play Store is the largest and most popular Android app store.

The purpose of our project was to gather and analyze detailed information on apps in the Google Play Store in order to provide insights on app features and the current state of the Android app market.

We collected descriptive information on over 3,100 apps across 11 different categories in the Google App Store. We focused on the following 11 categories: Business, Food & Drink, Books & Reference, Travel & Local, Health & Fitness, News & Magazines, Education, Social, Finance, Medical, and Entertainment.

Each app has its own web page where detailed information about the app is available. We used Selenium to scrape information about each of these apps for our analysis. We scraped app title, category, developer name, whether an app contains ads, rating, number of reviews, app size, number of installs, and price.

Exhibit 1: Scraping the Google Play Store

_________________________________________________________________________________________

                     

_________________________________________________________________________________________

Health & Fitness, Travel & Local and Education were the most common categories, accounting for about 15%, 14% and 13%, respectively, of the total number of apps in our dataset. Food & Drink was the least prevalent category with only 87 observations (less than 1% of the total number of apps in our dataset). Four categories of applications, including Health & Fitness, Travel & Local, Education and Finance, accounted for more than 50% of the apps in our dataset.

Exhibit 2: Number of Apps Available for Download by Category

Most of the apps (58%) in the Google Play Store were free to install at the time of data collection. However, 26% of free apps had in-app purchases feature, concealing their true cost. In-app purchasing refers to buying extra content or subscriptions inside an application on a mobile device. Free apps are more likely to have in-app purchases feature.

Exhibit 3: Free vs. Paid Apps

In general, advertising is the most popular app monetization model. Indeed, more than 5o% of free apps in our dataset were supported by advertising in contrast to the paid apps, among which less than 1% featured ads.

Exhibit 4: Advertising

It looks like certain app categories have more free apps available for download than others. In our dataset, the majority of apps in Food & Drink, News and Magazines, as well as Social categories were free to install. At the same time, Health & Fitness, Travel & Local, Education, and Medical categories had the biggest number of paid apps available for download.

Exhibit 5: Free and Paid Apps by Category

The Google Play Store offers a wide range of applications, but most of these apps had been downloaded only by a small number of people. In our dataset, 28% of the apps had been downloaded fewer than 100 times, and about 74% of the apps had been downloaded fewer than 50K times. Among the apps that had been downloaded between 500M to 1B+ times were Instagram, Facebook, Google + and Google News.

Exhibit 6: Number of Installs

An average rating of 4.4 (out of 5.0) is the most common rating in our dataset. Specifically, 266 apps out of 3,144 apps are rated with an average rating of 4.4. Most of the apps (74%) are rated within the range of 4.8 to 3.8.

Exhibit 7: Distribution of User Ratings

It looks like only a small number of users take the time to write an app review. On average, somewhere between 3% and 8% of users who download an app write reviews in the Google Play Store. Our analysis has shown that users tend to leave more reviews for apps in finance, health & fitness, and business categories.

Exhibit 8: Number of Reviews as Percent of Downloads

The boxplot below provides more details on the distribution of user reviews. Median number of reviews as percent of downloads is about 2.5%, with a range from as little as 0.03% to almost 10%.

Exhibit 9: Number of Reviews as Percent of Installs

Most of the paid apps in the Google Play Store are inexpensive. The majority of apps cost about $1. The average price of all paid applications in our dataset is about $6.9. App prices range from $0.99 to $399.99.

Exhibit 10: Distribution of App Prices

Counter-intuitively, $2-3 apps and $4-6 apps have more aggregated downloads than cheaper apps, which are below $2. Expensive apps, which are above $30, are less popular, and there are also fewer of these apps available for download in the Google Play Store.

Exhibit 11: Aggregated Downloads of Paid Apps

The Google Play Store shows apps’ actual download sizes. In 2015, Google increased the app size limit allowed on the Google Play from 50MB to 100MB, and in 2016 it started displaying apps’ true download sizes.

Average app’s download size in our dataset is 17.3MB, with a range between 0.02MB and 100MB. Education and Travel & Local app categories have the highest average download size, while Finance category is the smallest on average.

Exhibit 12: Average Download Size by Category

We found that there is no correlation between app features like size, rating, number of installs and price. Surprisingly, it appears that there is no correlation between price and rating as well as between rating and whether an app contains ads or not.

Exhibit 13: Correlations

Exhibit 14: Correlation Between Rating and Price

The chart below shows the number of unique developers in each category. Health & Fitness, Education and Finance are the most popular categories among developers.

Exhibit 15: Number of App Developers by Category

The majority of app developers build apps only in one category (2,251). Only a small number of developers build apps associated with two or more categories. For instance, only 34 developers made apps for two categories, 6 developers built apps for 3 categories and 2 developers built apps for 4 categories. Only one developer built apps for 8 categories and, not surprisingly, that developer was Google LLC.

Exhibit 16: Number of App Developers by Contribution to Each Category

Link to GitHub

Email: [email protected]

About Author

Related Articles

Leave a Comment

搜集了3100款App的数据,我发现了安卓用户的“潜规则” – What Happened Today?!? October 1, 2018
[…] 注:本文编译自科技博客《Analysis of Apps in the Google Play Store》,点击“阅读原文”查看。内容仅为作者观点,不代表DT数据侠立场。文中图片部分来自作者。 […]
搜集了3100款App的数据,我发现了安卓用户的“潜规则” - RSS Feeds October 1, 2018
[…] 注:本文编译自科技博客《Analysis of Apps in the Google Play Store》,点击“阅读原文”查看。内容仅为作者观点,不代表DT数据侠立场。文中图片部分来自作者。 […]

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI