Viral Effectiveness Index for SaaS Interaction Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in effectively measuring and analyzing user interactions with software products, particularly for B2B and B2C SaaS, due to the complexity of graph terminology and the difficulty in understanding relative trade-offs between different graph metrics.
Innovation Solution
A system that measures user interaction data from multiple user devices, models this data as a graph, and calculates a viral effectiveness index (VEI) as a single metric summarizing core graph attributes. This system uses a graphing application and an analytics application with AI integration to generate natural language insights and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple graph metrics are used to measure user interactions, then measurement precision is improved, but ease of operation deteriorates due to difficulty in understanding relative trade-offs
Solution Approach 1:
The patent combines multiple graph metrics (degree, betweenness, closeness, eigenvector centrality) into a single unified graph metric that captures overall user influence and engagement. This consolidation maintains measurement precision by incorporating all metric dimensions while improving ease of operation by presenting a single interpretable value instead of multiple complex metrics requiring trade-off analysis
2Measurement precision
If graph terminology and intricacies are explained to the target audience, then measurement precision is improved, but loss of time increases due to continuous explanation requirements
Solution Approach 1:
The patent introduces an intermediary layer that automatically translates complex graph metrics into business-relevant insights about user influence, engagement, and virality. This intermediary processing layer maintains measurement precision by accurately computing graph metrics while eliminating the need for continuous time-consuming explanations to the target audience
3Device complexity
If in-depth measurement of user interaction is limited to a single product, then device complexity is reduced, but measurement precision deteriorates due to inability to measure multi-product interactions
Solution Approach 1:
The patent creates a universal graph-based measurement system that can analyze user interactions across multiple products and platforms simultaneously. This multi-functional approach maintains manageable device complexity by using a unified graph modeling framework while dramatically improving measurement precision by capturing cross-product interaction patterns that single-product analysis would miss
Data Source
AI summary
A system and method measuring data, from user devices, regarding user interactions with a Software-as-a-Service (SaaS) product installed in each of the user devices, using a measurement module in the processor to generate measured data. The measured data is then modeled as a graph, using a graphing application in the processor, wherein the graph includes a plurality of varying metrics, each representing different attributes of a structure of the graph. A viral effectiveness index (VEI) as a single metric summarizing core graph attributes of the graph is determined from the plurality of the varying metrics of the graph using a viral effectiveness index (VEI) module in an analytics application in the processor.