About the Appfluencer®

The unique advantage of the Appfluencer® is its view of the user from the vantage point of the apps on the device. Users with a high percentage of travel apps could be considered “active travelers” with specific preferences for subsets that include airline, hotel and ride sharing apps.

The Appfluencer® uses data about the apps and other information collected from devices that have “opted-in” to data collection. We do not collect personally identifiable information (PII). We track and measure the daily changes in penetration to calculate growth rates and other performance characteristics of apps. The information is useful in predictive modeling, profiling and targeting. By tracking app-related data and metrics, we identify trends and uncover insights in the daily change in user interests.

Appfluencer® is a Registered Trademark of Shared2you, Inc.

Background

Data Collection

The Appfluencer® uses 1st party mobile data collected from devices that have “opted-in” to data collection. Appfluencer® views the user from the vantage point of the apps on the device. NO personally identifiable information (PII) is collected. So far, our technology has been integrated into 17 applications that have been downloaded more than 300,000 times. We currently maintain data on nearly 125,000 devices and regularly purge inactive devices. Each day over 24 Trillion measurements, analyses and calculations are performed to continually update the data contained in Appfluencer® App Market Research, Appfluencer® App Market Intelliegence, and the Appfluencer® Targeting & Scoring Algorithm.

This may not seem like a great deal of data, however, consider the path that an app follows before it is retained on the device:

    • App is downloaded from the App Store
    • App is opened on the device
    • App is on the device more than 30 days

Prior to an app install, there are a couple other events that occur

    • Impressions
    • Clicks
    • Installs

Once an app is installed on the device it needs to stay there, however within 30 days the average retention rate is less than 10%. While this is not true for all apps, Here is a brilliant blog on the topic from Andrew Chen, New data shows losing 80% of mobile users is normal…

App Counts Matter

App download’s are important – in fact, an app download is the very first step in the process of acquiring an engaged user.  App downloads are the beginning of the story…and unfortunately, sometimes they are the end.

On average, the 30 day retention rate for the average app is less than 10% (ten percent) and it continues to fall thereafter.  While this is not true for all apps, it is true for the majority.

For the past five years we have been studying information gathered about the apps installed on mobile devices.  Anyone who works with large amounts of data knows, having lots of data for the sake of having lots of data is a waste of resources.  We spent the first two years understanding the data and once we understood the value, began to keep all the daily data from the various data sets.

We have measured the change in daily app counts for the past 2 1/2 years, the change in penetration rates at the device level and began to understand the reasons for the small number of large successes, the larger, yet smallish percentage of moderate success, the vast number of Indy developers and the increasingly large number of zombie apps in the app stores.  We have studied the app economy, from the bottom up, at the device level. We have been able to segment devices by specific apps, manufacturer, competitor and have been able to create audiences from groups of cohorts that are only limited by imagination, and access to data.  Through our understanding of this information and by tracking the daily app installs and un-installs we have come to understand the app economy. Differently.

Transparency has presented us with the opportunity to see information that has been missing. Access and the opportunity to learn, study and share has helped in providing the answers.

Data Products

Appfluencer® App Market Research

High level information regarding the App Economy.  Includes the following regarding Apps & Device:

  • Top Apps by Platform
  • Penetration rates
  • Category Analysis by Reach, App Count and Penetration Rate
  • App & Game Counts by Country & Manufacturer

Ideally suited to “researchers” or people with a general interest in the App marketplace.

Appfluencer® App Market Intelligence

Actionable information to enable decision making. Conduct market research, identify new product opportunities, understand your competitors and identify valuable audience segments. Examples include:

  • Changes across time –
    • App Counts
    • User Profiles
  • Competitive Intelligence
    • Head to Head Comparisons
  • Identification of valuable users
  • Audience Insights
  • Soft Launch Planning

This information is ideally suited to people in business & product development, marketing, and those involved in strategic decision making.

Appfluencer® Targeting Algorithm

The Appfluencer® Targeting & Scoring Algorithm is updated on a daily basis.

  • Identify the best apps to target for new users
  • Exceed campaign goals with precise targeting, and
  • Enable device level targeting with transparency

For additional detail, check out the UA Targeting Demo to see how it works, then schedule a meeting for a custom analysis with detailed explanation.

Methodology

The Appfluencer® was born from the idea to study the users from the viewpoint of the apps on the device. For the past 4 years on a daily basis mobile devices provide a detailed listing of the apps on a mobile devices where our technology has been integrated. The individual’s right to privacy is very important. The only information collected is the daily changes to the apps on each device. No information contained in any application is captured. Information is reported in aggregate, with no weighting, normalization or injection of third party data.

Applications

By tracking app-related data and metrics trends can be identified. The data collected provides the basis for predictive modeling, profiling and targeting. For example, a group of users with a high percentage of travel apps most likely has an interest in, and a propensity to travel, while a subset of active travelers may have specific preferences for airline, hotel or ride sharing apps. Device groups can be created based on a homogenous set of mutual interests; specific app(s); behavior and similar device level groupings.   Comparisons can be made of segments like Hardcore Gamers to F2P Gamers, or more specific comparisons of Streaming Music providers like Pandora and Spotify users. The goal is to eliminate randomness by creating a defined audience for relevant comparisons.

Contact Us

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