- An estimate of the number of installs or in-app actions she can drive with her bidding strategy.
- An estimate of the total number of impressions the app will receive across Google’s inventory.
- A recommendation of the bid she should set to compete against similar businesses.
- A recommendation of bid adjustments, based on different times of the day and week.
- App engagement
- App installs
- App notifications
- App interactions
- Reporting level
- Ad group level
- Campaign level
- Asset level
- Reach, demographics, reporting
- Reach, relevance, simplicity
- Reporting, relevance, simplicity
- Reporting, simplicity, demographics
- Content is taken from the website and social media associated with the app to design different ad formats.
- The system analyzes App campaigns run by similar businesses and identifies the best settings for your ads.
- The system will run a simulation to analyze how your app works, then design ads based on what it finds.
- If video or image assets aren’t manually uploaded, content may be pulled from the app store listings.
- Ad formats, budget, location
- Ad formats, language, location
- Budget, language, location
- Ad formats, budget, language
- Enter address
- Add payment
- Coupon redemption
- Video views
- Matched interest
- Machine learning can analyze many signals to determine the right audience for her ad.
- Machine learning can review competitor performance to optimize campaigns.
- Machine learning can create combinations of assets to deliver a relevant ad to the right user.
- Machine learning can apply industry insights to optimize campaigns.
- Reporting
- Optimization
- Decision making
- Budget setting
- The system starts by learning from manual management.
- Other campaigns in the account need to be analyzed first.
- There’s a lot of data needing to be processed.
- Manual input is needed from Google employees.
