Correlation Analysis for KPI Metrics and Community Data
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Solution Overview
Problem
Existing methods for reviewing interaction data in online communities are cumbersome and ineffective, overwhelming businesses with abundant data that is difficult to analyze for success and health metrics.
Innovation Solution
A method and system that receive key performance indicator values and time range information, determine correlations between metrics and these values, and provide results to users without revealing sensitive data, using a computing device with processors and memory to simplify data review and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If abundant interaction data is tracked to measure online community success, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent extracts and isolates only the most relevant metrics from the abundant interaction data through automated analysis. The system identifies key performance indicators (KPIs) and correlates them with specific community activities, pulling out meaningful insights from the data ocean without requiring users to process all raw data themselves.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that sits between the raw interaction data and the user. This intermediary performs correlation analysis, identifies patterns, and presents simplified results, mediating between the complexity of data collection and the simplicity of user interpretation.
2Measurement precision
If comprehensive interaction data is collected to analyze community health, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent enables the system to perform self-service automated analysis of interaction data. The computational system automatically correlates KPIs with community activities, generates insights, and presents results without requiring manual data processing by users. The system serves itself in analyzing its own collected data.
Solution Approach 2:
The patent replaces manual mechanical analysis of interaction data with automated computational analysis. Instead of users manually examining and correlating data points, the system uses algorithms and processing power to automatically identify patterns and relationships between KPIs and community activities.
3Loss of information
If detailed interaction data is tracked to determine community success, then information completeness is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary automated analysis and correlation of interaction data as it is collected or at scheduled intervals. By pre-processing and pre-analyzing the data, the system prepares insights in advance, so when users need information about community health, the analysis is already complete or near-complete, reducing their time investment.
Data Source
AI summary
A device with one or more processors and memory receives, from a first party, an input including a sequence of numbers corresponding to a plurality of key performance indicator values associated with an entity and time range information for the sequence of numbers. In response to receiving the input, the device obtains, from a data set associated with the entity, data that corresponds to a plurality of metrics in accordance with the time range information, wherein the data set is accessible to a second party that does not have direct knowledge of the key performance indicators and determines correlations between the plurality of metrics and the sequence of numbers. The device provides, to the first party, a result indicative of a respective correlation between one or more of the metrics and the sequence of numbers.