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:
Prior to an app install, there are a couple other events that occur
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…
So based on the path an app must travel to get to the device and stay installed, the relevant number is much greater. Depending on how you calculate click-thru rate (CTR) and conversion rate (CVR). If we were to assume 10% (ten percent) retention after 90 days and the following values for ‘clicks’ and ‘impressions’, the calculation to determine the relevant number of devices this compares to, for UA purposes would look like the following:
Retained Users = 125,000
App Installs = 125,000 ‘retained users’ divided by 0.10 (10%) = 1,250,000 App Downloads;
Ads Clicked = 1,250,000 ‘app downloads’ divided by 0.050 (5%) = 25,000,000 Ads Clicked;
Ad Impressions (Audience Reach) = 25,000,000 ‘ads clicked’ divided by 0.050 = 500,000,000 Impression (Audience Reach)
When you consider the journey an app navigates to become a retained app, it puts the data into perspective.
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.
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.
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.
High level information regarding the App Economy. Includes the following regarding Apps & Device:
Ideally suited to “researchers” or people with a general interest in the App marketplace.
Actionable information to enable decision making. Conduct market research, identify new product opportunities, understand your competitors and identify valuable audience segments. Examples include:
This information is ideally suited to people in business & product development, marketing, and those involved in strategic decision making.
The Appfluencer® Targeting & Scoring Algorithm is updated on a daily basis.
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.
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). The unique advantage of the Appfluencer® is its view of the user from the vantage point of the apps on the device. A group of users with a high percentage of travel apps most likely has an interest in traveling, while a subset of active travelers has specific preferences for airline, hotel or ride sharing apps. We have found that devices where the app count is higher than average (20% of users) have a greater influence on app penetration than the other eighty percent (80%).
The Shared2you database tracks and measures daily changes in penetration and calculates growth rates to understand performance characteristics of apps. The information is useful in predictive modeling, profiling and targeting. By tracking app-related data and metrics, Shared2you identifies trends, and uncovers insights into the changing interests of users.
Appfluencer® is a Registered Trademark of Shared2you, Inc.