Website Performance Evaluation Using Customer Segment Agents
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Solution Overview
Problem
Existing methods for evaluating website performance are inefficient and subjective, relying on customer feedback that is time-consuming to gather and skewed towards self-selected opinions, lacking an objective and reliable means to distinguish website quality.
Innovation Solution
A method using an agent that interacts with websites according to behavior models for customer segments, gathering performance data and comparing it to utility functions to assign objective ratings, with a system that includes data mining for customer segmentation and utility function derivation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer feedback is collected and processed to evaluate website performance, then website quality assessment is achieved, but the process is time-consuming and the feedback is skewed towards self-selected opinions
Solution Approach 1:
The patent creates artificial customer agents that copy and simulate real customer behavior patterns. These agents interact with the website according to pre-defined behavior models, generating performance data without requiring actual customer participation. This copying approach eliminates the time-consuming feedback collection process while maintaining evaluation accuracy through statistically meaningful simulations.
Solution Approach 2:
The system performs preliminary actions by pre-defining behavior models and utility functions before actual evaluation. Customer behavior patterns are modeled in advance, and performance metrics are predetermined. When evaluation is needed, the system simply executes these pre-prepared models against the website, avoiding the need for time-consuming real-time feedback collection and processing.
2Loss of information
If customer feedback is used to evaluate website performance, then some assessment information is obtained, but the feedback is subjective and relies on limited individual experiences
Solution Approach 1:
Instead of relying on subjective customer feedback, the system copies objective performance metrics directly from website interactions. Artificial agents measure actual performance data such as page load times, transaction completion rates, and navigation efficiency. This copying of objective data eliminates subjectivity while preserving comprehensive performance information.
Solution Approach 2:
The patent introduces artificial customer agents as intermediaries between the website and the evaluation process. These agents objectively measure performance according to predefined criteria, serving as a neutral mediator that eliminates subjective human bias. The agents collect and report performance data without the emotional or personal factors that influence real customer feedback.
3Reliability
If traditional website evaluation methods are used, then some performance insights are gained, but the evaluation lacks reliability for distinguishing website quality
Solution Approach 1:
The patent segments the evaluation system into distinct modular components: behavior models that define customer actions, utility functions that define performance metrics, artificial agents that execute interactions, and analysis modules that process results. This segmentation allows each component to be independently developed, tested, and maintained, reducing overall system complexity while improving reliability through specialized functionality in each segment.
Solution Approach 2:
The system achieves reliable evaluation by changing key parameters from subjective human feedback to objective automated measurements. Performance metrics are defined as specific measurable parameters such as response time, transaction success rate, and navigation depth. These parameter changes transform the evaluation from unreliable subjective assessment to reliable quantitative measurement.
Data Source
AI summary
A method is disclosed for evaluating the performance of a website. An agent interacts with the website using a behavior model of an exemplary website customer. The agent interacts with the website according to the behavior model and gathers website performance data related to the interaction. The performance data is compared to a utility function for the behavior model. A rating is assigned to the website based on the comparison, and the rating is made available to potential website customers seeking information related to the website's performance.