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 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: The present disclosure relates to performing attribution modeling in real time using touchpoint data that correspond to arbitrary analytics parameters (e.g., a user-specified dimension) and are retrieved from a database using an attribution model. For example, in one or more embodiments, a system stores raw data in an analytics database that comprises an aggregator and a plurality of nodes. In particular, each node stores touchpoint data associated with a different user. Upon receiving a query, the system can, in real time, retrieve subsets of the touchpoint data that correspond to a user-specified dimension in accordance with an attribution model. The system then combines the subsets of touchpoint data using the aggregator and generates the digital attribution report using the combined data.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing stacked attribution distributions within a stacked attribution user interface. For example, the disclosed systems can utilize attribution models to generate stacked attribution distributions as breakdowns of other attribution distributions. The disclosed systems can further provide a stacked attribution user interface that includes selectable elements for identifying event categories, dimensions, and attribution models for generating stacked attribution distributions. Based on user interaction with these selectable elements, the disclosed systems can dynamically generate, provide, and modify attribution breakdowns via the stacked attribution interface.

Abstract: In some embodiments, a real-time and interactive preview of alerts is provided in a user interface. A computer system parses a set of rules that specifies an alert definition. Each rule identifies a set of observations and an alert trigger criterion based on user input in the user interface. For a rule, the computer system accesses historical data corresponding to the set of observations identified by the rule and determines, based on an analysis of the historical data, time points that trigger alerts over a time period according to the rule. The analysis is based on the alert trigger criterion identified by the rule. The computer system aggregates, based on the alert definition, the time points determined for the rule with time points determined for another rule from the set of rules. Further, the computer system generates an alert preview over the time period for presentation at the user interface.

Abstract: The present disclosure relates to performing attribution modeling in real time using touchpoint data that correspond to arbitrary analytics parameters (e.g., a user-specified dimension) and are retrieved from a database using an attribution model. For example, in one or more embodiments, a system stores raw data in an analytics database that comprises an aggregator and a plurality of nodes. In particular, each node stores touchpoint data associated with a different user. Upon receiving a query, the system can, in real time, retrieve subsets of the touchpoint data that correspond to a user-specified dimension in accordance with an attribution model. The system then combines the subsets of touchpoint data using the aggregator and generates the digital attribution report using the combined data.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing stacked attribution distributions within a stacked attribution user interface. For example, the disclosed systems can utilize attribution models to generate stacked attribution distributions as breakdowns of other attribution distributions. The disclosed systems can further provide a stacked attribution user interface that includes selectable elements for identifying event categories, dimensions, and attribution models for generating stacked attribution distributions. Based on user interaction with these selectable elements, the disclosed systems can dynamically generate, provide, and modify attribution breakdowns via the stacked attribution interface.

Abstract: In some embodiments, a real-time and interactive preview of alerts is provided in a user interface. A computer system parses a set of rules that specifies an alert definition. Each rule identifies a set of observations and an alert trigger criterion based on user input in the user interface. For a rule, the computer system accesses historical data corresponding to the set of observations identified by the rule and determines, based on an analysis of the historical data, time points that trigger alerts over a time period according to the rule. The analysis is based on the alert trigger criterion identified by the rule. The computer system aggregates, based on the alert definition, the time points determined for the rule with time points determined for another rule from the set of rules. Further, the computer system generates an alert preview over the time period for presentation at the user interface.

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: The likelihood of consumption of a linear content stream may be determined. Information that includes data indicative of user interaction with a client device may be received. The information may correspond to a period during which the client device received linear content. The likelihood of consumption may be determined for that period based on the received information.

Abstract: The likelihood of consumption of a linear content stream may be determined. Information that includes data indicative of user interaction with a client device may be received. The information may correspond to a period during which the client device received linear content. The likelihood of consumption may be determined for that period based on the received information.

Abstract: The likelihood of consumption of a linear content stream may be determined. Information that includes data indicative of user interaction with a client device may be received. The information may correspond to a period during which the client device received linear content. The likelihood of consumption may be determined for that period based on the received information.

Abstract: The likelihood of consumption of a linear content stream may be determined. Information that includes data indicative of user interaction with a client device may be received. The information may correspond to a period during which the client device received linear content. The likelihood of consumption may be determined for that period based on the received information.