{"id":79,"date":"2022-01-04T09:15:31","date_gmt":"2022-01-04T09:15:31","guid":{"rendered":"https:\/\/persona.ly\/glossary\/real-time-bidding\/data-sharing-in-mobile-marketing\/"},"modified":"2022-12-11T12:40:53","modified_gmt":"2022-12-11T12:40:53","slug":"data-sharing-in-mobile-marketing","status":"publish","type":"page","link":"https:\/\/persona.ly\/glossary\/real-time-bidding\/data-sharing-in-mobile-marketing\/","title":{"rendered":"Data Sharing in Mobile Marketing"},"content":{"rendered":"\n

What is Data Sharing in Mobile User Acquisition?<\/h2>\n\n\n\n

Finding Your Target Audience<\/h3>\n\n\n\n
\"Data<\/figure>\n\n\n\n

Data sharing in mobile UA means providing your UA partner access to essential UA-related data, in the form of an audience list, comprised of existing users. Sharing this data fulfills two central goals: it helps your UA partner target lookalikes<\/strong>, which, in turn, helps improve your campaign results, and it serves as a suppression list<\/strong> (formerly known as a blacklist) to prevent targeting existing users.\u00a0
By using this data your campaigns are focused on acquiring new and relevant users, instead of wasting your budget on a long exploration phase and targeting existing and active users, or ones that already used your app and didn\u2019t retent.\u00a0
Sharing an audience list with your UA partner is relevant to partners who run data-driven UA campaigns. If the campaigns run by your partner are not, at the very least, programmatic, then sharing the data will only be used as a suppression list.<\/p>\n\n\n\n

A Holistic Approach to Mobile UA<\/h2>\n\n\n\n

Using an Audience List to Target Lookalikes<\/h3>\n\n\n\n

While it may sound abrasive, data sharing has a huge impact on mobile marketing campaigns when they are run through a DSP partner that employs machine-learning. Sharing an audience list means that from the early stages of the campaign, your DSP partner has the necessary information required to target lookalikes (given that the partner has the technical capability to do so).<\/p>\n\n\n\n

Using existing audience data can help your partner create a profile of a potential new user. Depending on the UA partner\u2019s targeting abilities and its existing data, this data can help recognize behavioral patterns<\/strong> from which it can build an initial targeting model.<\/p>\n\n\n\n

Some of the patterns that can be detected and targeted with are types of devices, OS versions, session depths, the time of day, and so on. Recognizing common behaviors between existing users allows for the creation of a targeting model (programmatic or fully based on machine learning<\/a>) which, in turn, learns based on its performance.<\/p>\n\n\n\n

For example, let\u2019s say the app at hand is a hyper-casual gaming app. You provide your UA partner with data about your existing users (i.e an audience list). Your partner uses this data to detect patterns and sees a specific time-of-day activity, a repeating average session length, and that these engaged users all are highly active in the hyper-casual genre. They might also recognize a more engaged gender or age group. These patterns can be picked up by the machine learning model and guide bidding decisions<\/a>, to ensure only users with a higher probability to install and engage with the app are bid on.<\/p>\n\n\n