Predictive Web Analytics Model for Revenue Optimization
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Solution Overview
Problem
Traditional web analytics methods rely on observed results and require multiple test cases, limiting accuracy and reliability due to time constraints and reliance on intuition for feature development, which restricts the number of tests that can be conducted.
Innovation Solution
A system and method using predictive web analytics that builds an initial website effectiveness model through Bayesian networks or combinations with structural equation models, prunes the model to ensure business sense, and implements a time series approach to predict the impact of functional levers on revenue and website effectiveness, allowing for 'what if' scenario projections to identify areas for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional web analytics methods use multiple test cases with clear test case protocols, then measurement accuracy can be improved, but the time required and complexity of the testing process increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple test cases with clear test case protocols before actual testing begins. This allows the analytics system to have measurement frameworks ready in advance, reducing the time required during actual execution while maintaining measurement accuracy through pre-planned test structures.
Solution Approach 2:
The system changes parameters by dynamically adjusting test case configurations, sample sizes, and measurement parameters based on available time resources and priority levels. This allows the system to optimize between measurement accuracy and time consumption by adapting parameters to current constraints.
2Reliability
If traditional web analytics conduct more tests to improve reliability, then measurement reliability can be improved, but the number of tests that can be conducted is limited by time constraints
Solution Approach 1:
The system implements continuous useful action by running multiple tests in parallel and continuously collecting analytics data across different test cases simultaneously. This approach increases the total number of tests that can be conducted within available time while maintaining reliability through continuous data collection and analysis.
Solution Approach 2:
The system segments the testing process into independent, parallelizable test cases that can be executed concurrently. By dividing the overall analytics measurement into multiple independent segments, the system can increase productivity by running more tests simultaneously while maintaining reliability through comprehensive coverage of different test scenarios.
3Ease of operation
If traditional web analytics rely on intuition for feature development, then ease of operation is improved, but the accuracy and reliability of results deteriorates
Solution Approach 1:
The system implements feedback mechanisms that provide data-driven insights to guide feature development decisions. By incorporating analytics feedback loops, the system maintains ease of operation through automated recommendations while improving accuracy by replacing pure intuition with evidence-based decision making.
Solution Approach 2:
The system enables self-service by automatically generating test case recommendations and analytics insights without requiring deep expert intuition. This maintains ease of operation for users while improving accuracy through automated analytical processes that objectively evaluate feature priorities based on data.
Data Source
AI summary
A system and method are disclosed for optimizing website effectiveness. Original input data associated with a plurality of website effectiveness variables is processed using a website effectiveness model to generate a first website effectiveness value, which in turn is processed to generate a dependent variable. Input data corresponding to an individual website effectiveness variable is then processed to generate changed input data, which in turn is processed by the website effectiveness model with the original input data and the dependent variable to generate a second website effectiveness value. The first and second website effectiveness values are then processed to determine the effect of the changed data on the first website effectiveness value.