Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: An analytics system determines the number of downloads for a content item during a first time interval. The system receives engagement data from a plurality of client devices in a panel of client devices. Based on the engagement data, a partial install base for the content item during the first time interval is determined. Based on the determined partial install base for the content item and historical data for other content items, a preliminary install base for the content item during the first time interval is determined. Responsive to determining that the engagement for the content item is greater than a threshold value, an estimated number of downloads for the content item during the first time interval is determined. The estimated number of downloads for the content item during the first time interval is determined based on the preliminary install base for the content item for the first time interval.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.

Abstract: Content is inserted into conversations hosted on a takeoff site using creatives that provide lures and links to other material that users engaged in the conversation might find helpful and relevant. Such insertions are made in a contextually meaningful fashion through the use of a matching and decision process that determines best candidate creatives and an appropriate time and place for their insertion into the conversation.