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Attribution methods

MyTracker supports several attribution methods for mobile and web platforms. Method priorities are described for each platform separately.


Web traffic attribution works through tagged links. These can be MyTracker tracking links or links with UTM tags. Attribution by MyTracker links is prioritized, if they are not used, attribution is done by UTM-tags. If the traffic is not tagged, the source of the visit will be considered organic.

For details, refer to the Attribution section

iOS / Android

Attribution on mobile devices is based on ad or pre-install campaign markup. First, attribution will be performed on the labeled pre-installs. If there is no data, attribution will be performed on clicks, and if no clicks are found, attribution will be performed on ad impressions.

iOS attribution uses the Device ID matching method. If the device ID is unknown, MyTracker will perform attribution using SKAdNetwork data. If your application does not support SKAdNetwork, MyTracker will try to associate the installation with an ad campaign using the Probablistic method.

For Android attribution, MyTracker primarily tracks labeled pre-installs. If there is no data, MyTracker uses other attribution methods. If the application is placed in the Google Play store, MyTracker attributes the installation with the Android Refferer method, otherwise with the Device ID matching method. If all previous attribution methods are not suitable, MyTracker will associate the installation with the advertisement using the Probablistic method.

Pre-install (Android)

For pre-installs attribution, MyTraker supports the following methods:

  • PAI (Google Play Auto Install) when apps are automatically loaded and installed from the cloud after users activate the devices.

    To track pre-installs, generate referrer with PAI parameters in the MyTracker interface and share it with a device manufacturer to insert it in the utm_campaign parameter. When a user launches your app for the first time, MyTracker queries the Google Play Install Referrer API and retrieves pre-install data.

  • This method is available only for apps published on Google Play

  • ApkPreinstallParams, when retailers install the app in the device sale.

    To track pre-installs, generate APK parameters in the MyTracker interface, add them to the app build, and share the APK file with the retailer. With the customer's consent, the retailer installs the APK on the device. When a user launches your app for the first time, MyTracker reads APK parameters and retrieves pre-install data.

  • Use this method when no other options are possible. If the user decides to update the app before the first launch, APK parameters will be erased and the pre-install will not log.

  • System Properties when apps are installed on devices before they go on sale.

    To track pre-installs, generate key-value in the MyTracker interface and share it with a device manufacturer to insert it in the device’s system properties. When a user launches your app for the first time, MyTracker retrieves pre-install data.

  • System Properties (additional method) is an outdated method. A manufacturer places key-value in the special file and specifies the path to the file in the device's system properties.

For details, refer to the Pre-install tracking section

Android Referrer (Android)

Android Referrer works only with Android apps distributed through Google Play. In this method, the link to the app store must contain a special parameter, referrer, whose data identify the source of referral. Once an app is installed, it will receive the required parameter value through Android OS services. Then, that value will be transmitted to the MyTracker server and used to detect the source of installation.

To attribute installs from Facebook, you need to pass an Install referrer decryption key to MyTracker. Learn more

Device ID matching (iOS/Android)

This method is used when the platform where a tracking link is placed is capable of obtaining user's device ID and transmitting it to the tracking link. The following identifiers are most commonly used for this purpose:

  1. IDFA for iOS devices
  2. Waid for Windows devices
  3. GAID and AndroidID for Android devices

When a user clicks on a tracking link, MyTracker receives a device ID. After the user installs and runs the app for the first time, the app's built-in MyTracker SDK also reads the device ID, which can be used to search for a click with the same ID.

Device ID Matching is a fairly accurate and the most common attribution method. It works very well for in-app ads but is not usable when the tracking link is placed in an environment that is not capable of obtaining device identifiers. For example, email and SMS campaigns, print ads, web pages accessed through a browser, etc.

SKAdNetwork (iOS)

For iOS devices, MyTracker supports SKAdNetwork attribution, which allows you to attribute an app install to a specific ad campaign without using IDFA while maintaining user privacy.

When a user clicks on an advertisement, MyTracker calls the SKAdNetwork framework to log the conversion. SKAdNetwork sends a postback to the ad network, which made the install happen, and sends a copy of the postback directly to MyTracker.

For details, refer to the SKAdNetwork attribution section

Probabilistic (iOS/Android)

Probabilistic attribution uses machine learning models. This method identifies the source that brought new users without advertising device identifiers (IDFA, GAID, etc.), maintaining user privacy.

Probabilistic modeling is based on a large amount of historical data. It continually learns from newly available stats from devices that have provided access to the ad identifier and SKAdNetwork stats.

The probabilistic model assumes that the user appearance relates to a particular ad campaign and partner based on device characteristics and information about advertising interactions. Because this method uses probability estimation and multiple device parameters, the attribution time can take longer (about 1 hour on average).

MyTracker allows you to restrict or to disable Probabilistic, when, for example, you know that other attribution methods are in use.

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