Analytics systems for measuring virality and network effects in multi-domain interactions

A system, comprising:

• a processor;
• and
• a machine-readable medium storing executable instructions that, when executed, cause the processor, alone or in combination with other processors, to perform operations comprising:
• measuring user interaction data, via a measurement module in the processor, to provide measured user interaction data from a plurality of user devices regarding user interactions with a software product accessible to each of the user devices;
• partitioning the measured user interaction data into n partitions based on an extrinsic key or property to generate partitioned measured user interaction data, using a measured interactions partitioning and graphing module in an analytics application in the processor;
• generating n graphs, one graph for each partition of the partitioned measured user interaction data, using the measured interactions partitioning and graphing module, which includes a graphing application, in the analytics application;
• determining, from the n graphs of the partitioned measured user interaction data, n viral effectiveness indexes (VEIs), one for each of the n graphs, wherein each VEI is a single metric summarizing core graph attributes and graph connectedness of a corresponding one of the n graphs using a viral effectiveness index (VEI) module in an analytics application in the processor to provide a simplified presentation of the n graphs with regard to user interactions with the software product;
• providing the VEIs generated by the analytics application to an artificial intelligence (AI) system coupled to the analytics application to provide an AI output;
• and
• generating natural language insights and recommendations regarding the software product in response to the AI output.

A method comprising:

• measuring data, via a measurement module in a processor, to provide measured data from a plurality of user devices regarding user interactions with a software product accessible to each of the user devices;
• creating a graph model generated based on the measured data, using a graphing application, wherein the graph model includes a plurality of varying sub-metrics, each representing different attributes of a structure of the graph model;
• determining, from the plurality of the varying sub-metrics of the graph model, a viral effectiveness index (VEI) as a single metric summarizing core graph attributes of the graph model using a viral effectiveness index (VEI) module in an analytics application in the processor, wherein the VEI is a summary of graph connectedness of the graph model and provides a simplified presentation of the graph model with regard to user interactions with the software product;
• providing the VEI generated by the analytics application to an artificial intelligence (AI) system coupled to the analytics application to provide an AI output;
• and
• generating natural language insights and recommendations regarding the software product in response to the AI output.