Data and Methodology

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Our Data

Mobile apps share download and revenue data with Apptopia directly. We use this data (which makes up over 40 billion data points) to build hundreds of models to accurately estimate download, revenue, and usage data for every mobile app in more than 50 countries. In addition to these direct data sources, we own proprietary technology and indexing engines that scan both iOS and Google Play App Stores hourly for every existing public data point, allowing us to create granular time series of app market data, which includes more than 35 different metadata points (rank, ratings, price reviews, etc..). Combining these two data sources allows us to accurately estimate performance for the entire market.

Software Development Kit (SDK) Data

Our proprietary SDK Recognition technology tracks what SDKs are installed or uninstalled from the top 500,000 free apps across both the App Store & Google Play, accounting for approximately 10,000 monitored SDKs.

We do so by:

  • Downloading the app
  • Decompiling the app
  • Decrypting the app
  • Analyzing the code and fingerprinting known SDKs currently installed in the app

The SDK Recognition technology is automatically deployed every time an app pushes out a release, creating a timeline of every tool the app has worked with. In addition, API call data goes beyond install and uninstall data to provide granular insight into the specific SDK features being used by each app.

SDK Collection

Data & Methodology Enhancements

To build our performance models we use real app performance from over 225K apps in all categories and countries who share their data directly with Apptopia over the last 11 months. As our technology and understanding of the app economy have evolved, we are now able to include a more comprehensive (200+ predictors of performance at the app, day, and country level) and nuanced view of the complex constellation of current and historical app store data to predict performance of these apps; and in turn, predict the performance in the rest of the app universe. We trained, tested, compared and visualized hundreds of models using bleeding edge techniques from the fields of machine learning and AI and developed our own proprietary techniques for optimizing the consolidation of app performance data to ensure robust estimates. We then selected the best models based on maximum predictive power and robustness through a variety of lenses.

Most Recent Improvments

  • Using this technique we have enhanced our iOS models 2.2x over previous models and are applying the same methodology to improve Google Play models. We are relentlessly improving our models and techniques to have the most accurate mobile app analytics on the market.
  • Our ability to estimate the important “tail” (i.e. Top Ranked Apps) of app performance has significantly improved. Due to an influx of new data and improvements to methodology we see a general increase in estimates for top ranked apps in all categories.
  • Significant increase in the stability of estimates across the board (i.e. less erratic and untenable behavior)

* Our new models struggle with predicting lower performing apps (e.g. apps that have 1 to 10 downloads on any given day/country) and may over predict by a factor of 2-3x.

What Makes Our Data Unique?

The Quality, Accuracy, and Breadth of Data

The overwhelming majority of data providers specialize in specific subsets of data. Some might exclusively track SDKs, whereas others might focus their efforts on discerning key app performance metrics. While their data is generally reliable, it is narrow in focus and paints an incomplete picture of the overall app ecosystem. Developers who rely on “specialized” companies are forced to use many of them—Those who rely on 'specialized' data providers end up with a patch-work solution to mobile intelligence that's expensive, dis-jointed, and unreliable. Apptopia is the only mobile data provider that's built a quality, comprehensive solution. Our breadth of mobile app and app store data not only provides a more complete market analysis, but it also has a significant effect on data accuracy. With such a wealth of disparate data points analyzed, Apptopia provides users a more accurate picture of the entire app ecosystem, not just a specialized subset.

Data Quality
Easy To Use

Easy-To-Use Business Intelligence Tool

Apptopia’s tool is designed to make it easy to get quick insights and unprecedented access to the driving forces behind today’s most compelling apps and publishers. Unlike other competitors who just “data dump,” forcing users to extrapolate meaning on their own or rely on heavy enterprise software such as Tableau, Apptopia has invested significant time in building features that allow users to gain insight directly within our tool.

For example, Apptopia’s Report Builder grants users the ability to analyze the mobile app market and set “trip wires” throughout an app store to get real-time notifications anytime an app or publisher crosses a desired benchmark (i.e., Photo App X has 75,000 daily active Users (DAU) and a competitor’s SDK installed). Nearly all of our data is consumable via notifications, allowing users to make sure important events are pushed directly to your inbox. This means that brand and app developer decisions are proactive instead of reactive.

Everything Included in the Apptopia Web Tool

Meta Data

  • App Name
  • Category
  • Subcategory
  • Price
  • Description
  • App URL
  • In-App Purchases
  • Total Ratings
  • Initial Release date
    iOS = back to inception
    GP = back to 4/1/2014
  • Date of Last Update
  • Version # (per update)
  • “What’s New” text (per update)
  • App Icon (image file or URL)
  • Screenshots (image file or URL)
  • Publisher Name
  • Publisher URL
  • SDK Name
  • SDK URL
  • SDK Function

App Perfomance Data

  • Overall Rank
  • Daily Active Users
  • Category Rank
  • Monthly Active Users
  • Subcategory Rank
  • User Engagement
  • Downloads
  • 24-Hr Rank Change
  • Total Revenue
  • Breakout Likelihood
  • In-app Purchase Revenue
  • Top New Release
  • Advertising Revenue
  • Report-Downloads/Day
  • Paid Download Revenue
  • Top New App Reports
  • Demographics
  • Average Revenue Per User
  • Sessions
  • 30 Day Retention

Publisher Perfomance Data

  • Downloads
  • Monetization Breakdown
  • Total Revenue
  • LinkedIn URL
  • In-app Purchase Revenue
  • Web URL
  • Advertising Revenue
  • HQ Country
  • Paid Download Revenue
  • Average Revenue Per User
  • Daily Active Users
  • Monthly Active Users
  • User Engagement

SDK Perfomance Data

  • App Level

Installed SDKs

Uninstalled SDKs

Installed and Uninstalled Dates

Active/Inactive installed SDKs

  • SDK Vendor

Total of SDK Installs

Total of SDK Uninstalls

Total Install

  • Per Category
  • SDK Breakout Data (i.e. dynamic install counts on growing SDKs)

Stores Data

  • Total Submissions
  • Submission Comparison
  • Top Categories
  • (Download, Highest Grossing, Most New Apps)
  • Number of publishers
  • Avg number of apps per publisher
  • New publisher

Category Data

  • Average monthly income
  • Top Publishers
  • Number of apps
  • Top Apps
  • Number of publishers
  • Percentage of category market
  • Downloads
  • Share captured (revenue and download) by top 50 apps
  • Revenue
  • Apps Added
  • Publishers Added
  • Winners, Losers, and
  • Top New Releases

Countries Included

  • Argentina
  • Australia
  • Austria
  • Belgium
  • Brazil
  • Canada
  • Chile
  • China
  • Colombia
  • Croatia
  • Czech Republic
  • Denmark
  • Egypt
  • Finland
  • France
  • Germany
  • Greece
  • Hong Kong
  • Hungary
  • India
  • Indonesia
  • Ireland
  • Israel
  • Italy
  • Japan
  • Kenya
  • Korea
  • Kuwait
  • Malaysia
  • Mexico
  • Netherlands
  • New Zealand
  • Nigeria
  • Norway
  • Philippines
  • Poland
  • Portugal
  • Romania
  • Russian Federation
  • Saudi Arabia
  • Singapore
  • South Africa
  • Spain
  • Sweden
  • Switzerland
  • Taiwan
  • Thailand
  • Turkey
  • Ukraine
  • United Arab Emirates
  • United Kingdom
  • United States
  • Venezuela
  • Vietnam
Countries Included