Application metrics are numerical properties that AppTweak has been able to gather or estimate about applications. Metrics are exposed through two endpoints:

The Current endpoint which returns the latest - most recent - data available
The History endpoint which returns the historical daily values over a given period of time

Input values

The App Metrics endpoints take the following query parameters into account: apps, metrics, country
, device, start_date and end_date. The date parameters only apply to App Metrics - History. You can find more information about the query parameters directly in the API endpoint definition.

See the table below for the list of available metrics.

Metric Name	Description	Origin	Historical Data Available?
downloads	The daily number of first time downloads generated the application	AppTweak estimation	✅
revenues	The daily revenues generated by the application. Including app or in-app purchases.	AppTweak estimation	✅
app-power	A number between 1 and 100 indicating how visible and popular the application is. Higher is better.	AppTweak-defined KPI	✅
ratings	The total number of 1, 2, 3, 4; and 5 stars ratings and the average score of the app.	Gathered directly from the app store pages	✅
daily-ratings	A number between 1 and 5 indicating how many users like this application. Higher is better. Shows the new ratings for the day.	Gathered directly from the app store pages	✅

All metrics are available for both iOS and Android applications.

Response format

The response will be a JSON grouped by app ID and metric. For each metric the response will contain a hash (for App Metrics - Current) or a list of hashes (for App Metrics - History). The content of the hash is specific to each metric. You will find the content for each metric listed below.

downloads

"downloads": { 
        "value": <int>, // number of estimated daily 1st time downloads
        "date": <string>, // date in YYYY-MM-DD format
        "precision": <float> // indicator for the accurancy of our estimates
      } 
}

revenues

"revenues": {
        "value": <int>, // number of estimated revenues in USD
        "date": <string>, // date in YYYY-MM-DD format
        "precision": <float> // indicator for the accurancy of our estimates
        "currency": "USD"
      }
}

❗️
Precision field is deprecated since March 2023
Our new algorithm released in March 2023 doesn't take into consideration the precision indicator anymore. This field can therefore be ignored.

ratings

The total number of 1, 2, 3, 4, and 5 stars ratings, the total number of ratings, and the average score of the app as it appears on the store app page.

"ratings":
  {
    "value": <float>, // average rating for the app
    "breakdown": {
    "1": <int>, // total number of 1 star ratings
    "2": <int>, // total number of 2 star ratings
    "3": <int>, // total number of 3 star ratings
    "4": <int>, // total number of 4 star ratings
    "5": <int>, // total number of 5 star ratings
    "total": <int>, // total number of ratings
    "avg": <float> // average rating for the app
  },
  "date": <string> // date in YYYY-MM-DD format
  }

daily-ratings
The total number of new 1, 2, 3, 4; and 5 stars ratings and the average score of the app for the day. Negative values for the number of star ratings means that either users deleted their rating or the Apple or Google deleted ratings. The average rating takes only newly added ratings into account.

"daily-ratings":
{
  "breakdown": {
    "1": <int>, // number of new 1 star ratings
    "2": <int>, // number of new 2 star ratings
    "3": <int>, // number of new 3 star ratings
    "4": <int>, // number of new 4 star ratings
    "5": <int>  // number of new 5 star ratings
  },
    "date": <string> // date in YYYY-MM-DD format
    "value": <float> // average rating of the based on the new ratings. 
}

app-power

The "App Power" is a KPI (Key Performance Indicator) that shows the overall performance of an app in the store. The App Power is built on a scale from 0-100, 100 being the highest score and 0 the lowest. A high App Power means that an app is performing well in the given country and that it probably has a high download rate.

The App Power is country and store specific, which means that the same app can have different App Powers in different countries (typically if it is performing well in a country and not so well in another).

"app-power":
{
  "value": <int>,
  "date": <string> // date in YYYY-MM-DD format
}

Data overview

What do we predict?

Downloads:

IOS: First-time downloads (previously App Units) → when a user downloads your app to their device for the first time, not including re-downloads
Android: Unique Device Installs

Revenue:

iOS: Sales metric from the Itunes Connect Console
Android: Revenue estimates include all purchases that go through the stores (in-app purchases, paid apps, ...). So it includes renewed subscriptions as long as they go through the stores.

Extra information for Apple Sales :

→ Apple’s console proceeds correspond to the estimated amount of proceeds you will receive from your sales. This is the Customer Price minus applicable taxes and Apple’s commission, per Schedule 2 of the Paid Applications Agreement.. Sales therefore correspond to the whole amount before the commission.

📘
Estimation window
We can only compute downloads estimates if the app is ranked in the top 500 for IOS and 200 for Android within a -7 /+3 days from the considered day, as 500 for IOS and 200 for Android was the lowest rank possible at the time we trained the model.
Therefore estimations can vary slightly up to 7 days after the first estimation.
Note: We were able to compute the top 500 for IOS and Android before. However, on the 24th February Google decided to show until the top 200.

How do we compute the download and revenue estimates?

To calculate our download estimates, we use various indicators such as:

Category rank: The rank of an app in its category (free or paid) gives an indication of the level of daily downloads that the app is making. Of course, each category and country has its own level of competitiveness. Download estimates are based on the category ranking of an app in the Free or Paid Top Charts and Revenue estimates are based on category ranking of an app in the Grossing Top Charts.
Seasonality: Seasonality and specific events can highly affect app downloads. The impact depends on various elements such as the apps’ type, the country, and the event itself. These subtleties are captured by our estimates model.
A broad range of data: With the help of deep learning, our model uses a broad range of data to compute estimates, which results in a higher level of accuracy. More info on how we use deep learning in this blog post.

Why doesn’t AppTweak give download / revenue estimates for my app?

We only have category ranks up to 500 for Apple and up to 200 for Google → Our algorithms were only trained on apps that rank in the top 500 for Apple and top 200 for Google results, therefore, since download estimates heavily depend on category rank, they cannot be predicted for apps that don’t rank within the top 500 for Apple and top 200 for Google (estimates will be 0).
Do we always have non-zero data for apps that are ranking in the Top 200/500 of Top Charts? No, in small countries / small categories, you can find apps at the bottom of the top 200 for which we predict 0 downloads!
For revenue estimates, we look at the app’s rank in the Top Grossing Charts, therefore, we cannot predict revenue estimates for apps that don’t rank within the top grossing 500 for Apple and top grossing 200 for Google (estimates will be 0).