Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: In some embodiments, a contact stream is generated or modified based on configuration data received from a machine-learning model. Multiple contact items are selected for a contact stream, to be delivered to a user device via electronic communication channels. In addition, a success metric is identified indicating an engagement with the contact stream or an action performed following the engagement. A machine-learning model is applied to the contact items, where the machine-learning model is trained to identify relationships among actions in an online environment and configuration parameters that control delivery of contact streams. The machine-learning model provides an output indicating configuration data or a success probability for the contact stream. The configuration data includes configuration parameter values computed by the machine-learning model for achieving the identified success metric.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Abstract: Systems and methods for software management are described. One or more embodiments of the present disclosure receive first organization data about a first organization that uses a first software system and second organization data about a second organization that uses a second software system; receive first event data from the first organization and second event data from the second organization; generate first converted event data and second converted event data by converting the first event data and the second event data to a common data format; predict organization output based on using the first software system and based on using the second software system; compute a first rating for the first software system and a second rating for the second software system for use in the third organization; and installing the first software system in a computer system of a third organization based on the first rating.

Abstract: Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Abstract: The present disclosure is directed toward systems and methods for identifying contributing audience segments associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify audience segments associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors and segments that caused the particular anomaly and how those factors correlate with each other.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Abstract: Systems and methods for software management are described. One or more embodiments of the present disclosure receive first organization data about a first organization that uses a first software system and second organization data about a second organization that uses a second software system; receive first event data from the first organization and second event data from the second organization; generate first converted event data and second converted event data by converting the first event data and the second event data to a common data format; predict organization output based on using the first software system and based on using the second software system; compute a first rating for the first software system and a second rating for the second software system for use in the third organization; and installing the first software system in a computer system of a third organization based on the first rating.

Abstract: Systems and methods for software management are described. One or more embodiments of the present disclosure receive first event data from a first software system and second event data from a second software system, wherein the first event data is formatted using a first data format and the second event data is formatted using a second data format, generate first converted event data and second converted event data by converting the first event data and the second event data to a common data format, generate combined time series data by combining the first converted event data and the second converted event data, compute attribution information indicating a causal relationship between a first metric from the first software system and a second metric from the second software system based on the combined time series data, signal the attribution information indicating the relationship between the first metric and the second metric.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating derived dimensions within ingested data collections. In particular, in one or more embodiments, the disclosed systems generate derived dimensions that modify or correct one or more organizational schemas of an ingested data collection when querying the ingested data collection. For example, the disclosed systems receive a user-defined definition, and generates a fallback expression based on the user-defined definition and aligned to the schema of a particular ingested data collection. In at least one embodiment, the disclosed systems generate the derived dimension by identifying and modifying one or more existing dimensions in the schema based on the fallback expression when reading data from the ingested data collection.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

Abstract: This disclosure generally covers systems and methods that create sequential segments from analytics data to enable investigation of events that occurred before or after a certain sequence of events—that is, pre-sequence or post-sequence events. In particular, certain embodiments of the disclosed systems and methods receive a segment query of certain analytics data to identify events that occurred before or after a defined sequence of events within a network and—in response to the segment query—provide a query result that identifies pre-sequence events or post-sequence events. By providing such query results, the disclosed systems and methods enable users to examine correlations between a sequence of events and any pre-sequence or post-sequence events, including any data associated with those events at a granular level.

Abstract: The present disclosure is directed toward systems and methods that allow users to efficiently and effectively create and identify segments of usage patterns. For example, systems and methods described herein allow marketers to query and return sequential segments including sequence conditions based on user-defined dimension item values. Furthermore, systems and methods described allow marketers to query and return sequential segments including sequential events based on user-defined dimension variables. In addition to the foregoing, systems and methods described herein allow marketers to query and return sequential segments defined by repeated events performed at given regularity or frequency.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that provide an attribution user interface that integrates attribution models as native components within the interface to configure analytics visualizations. By integrating attribution models and corresponding functions as native components of a user interface, the disclosed methods, non-transitory computer readable media, and systems can implement attribution models as parameters of attribution distributions or of any attribution visualizations, where the attribution models function as event categories. For instance, the disclosed methods, non-transitory computer readable media, and systems can provide analytics tools to generate visualizations of different attribution distributions of events across dimension values (or other visualizations) based on different attribution models.

Abstract: Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Abstract: This disclosure generally covers systems and methods that create sequential segments from analytics data to enable investigation of events that occurred before or after a certain sequence of events—that is, pre-sequence or post-sequence events. In particular, certain embodiments of the disclosed systems and methods receive a segment query of certain analytics data to identify events that occurred before or after a defined sequence of events within a network and—in response to the segment query—provide a query result that identifies pre-sequence events or post-sequence events. By providing such query results, the disclosed systems and methods enable users to examine correlations between a sequence of events and any pre-sequence or post-sequence events, including any data associated with those events at a granular level.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Abstract: This disclosure involves performing event analytics on-the-fly based on user input to an analysis interface. An event analytics system correlates a plurality of event datasets to include a common visitor identifier. The system causes display, via the analysis interface, of information about the plurality of event datasets. The system receives, via the analysis interface, user selection of one or more event datasets, of the plurality of event datasets. Based on the selected one or more event datasets, the system generates a combined event dataset. The event analytics system receives, via the analysis interface, user input specifying information requested about the combined event dataset. The system obtains the requested information about the combined event dataset. The system causes display, via the analysis interface, of a visualization of the obtained information, wherein the visualization is based on event data from the combined event dataset in chronological order.

Abstract: This disclosure involves performing event analytics on-the-fly based on user input to an analysis interface. An event analytics system correlates a plurality of event datasets to include a common visitor identifier. The system causes display, via the analysis interface, of information about the plurality of event datasets. The system receives, via the analysis interface, user selection of one or more event datasets, of the plurality of event datasets. Based on the selected one or more event datasets, the system generates a combined event dataset. The event analytics system receives, via the analysis interface, user input specifying information requested about the combined event dataset. The system obtains the requested information about the combined event dataset. The system causes display, via the analysis interface, of a visualization of the obtained information, wherein the visualization is based on event data from the combined event dataset in chronological order.