What Makes a Successful App?

Posted on Jul 29, 2018

Struggling getting more users downloading your app?

For the last ten years, Apple iOS store has now seen more than 170 billion downloads and more than 130 billion in consumer spend. By today, there are more than 2.2 million apps included in Apple Store. While the most popular apps such as Facebook, Netflix, and Instagram see hundreds of thousands of downloads per day, a huge number of apps don't get downloaded once in a month. As an app developer, you might wonder what makes people download an app and what makes the most popular apps successful. With such concern, I developed this shiny app as a reference tool for app developers to gain insights of the Apple Store market.Β 

Categorize your app wisely.Β 

In case you are not clear which genre your app falls in, the genre comparison panel will be a good start. While Games has dominated the app market by taking overΒ  50%, Social Networking apps received the most reviews with an average number of 45496. Not surprisingly, Facebook is the most popular app with a total reviews over 3 million. Before you publish your app, you may want toΒ  choose your competitors wisely.Β In this Genre Comparison panel, you can compare genres by the number of reviews, rating scores, App Size, App price, etc.Β Β 

The developers of Tinder must have thought this through and therefore categorized them as a lifestyle app, which makes them the second most popular app in this category.Β 

Functionality is the key.Β 

What do the successful apps have in common? In other words, what are the predictors of a popular/successful app? By analyzing the pattern in the popular apps, we can find the clue. On the correlation panel of the Shiny App, app developers can explore the predictors of success by looking at the correlations between variables.Β 


Take Social Networking as an example, App size turns out to be the strongest predictor of the popularity. The larger the size, the more reviews an app received.Β 

Take-away Message for App Developers:

  1. Choose your Competitors wisely by putting your app in the right category.
  2. Enrich your functions.Β 

(Link to the ShinyApp; See my Github Code)

About Author

Silvia Lu

Silvia is currently working as a Data Analysis Intern at the Aviation Planning Department of Port Authority of New York and New Jersey. She also has a Psychology background and is a second-year graduate student in New York...
View all posts by Silvia Lu >

Leave a Comment

No comments found.

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