Clickstream Analytics for Automated Website UX Diagnosis
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Solution Overview
Problem
Current systems for identifying and remedying problems with websites are inefficient and require many hours of human labor and require costly and time-consuming manual intervention, and existing systems are inefficient and require many hours of human labor. For example, in some current website diagnostic techniques, after a user interacts with a website, the user may be sent a survey in which the customer identifies a problem with the website. However, the customer may not be able to accurately describe the problem with the website, or may not take the time to fill out the survey to identify the problem with the website. Indeed, the survey data is collected, but, the customer may never even bother to fill out the survey to identify the problem with the website. The, the survey data is not sent a list of potential user experience problems; extracting click data from the website; processing the extracted click data; training an analytics engine based on the processed click data; and using the trained analytics engine to diagnose the problem of the website with a potential user experience problem of the received list of potential user experience problems.
Innovation Solution
A computer-implemented method for diagnosing a problem of a website, comprising receiving a list of potential user experience problems, extracting click data from the website, processing the extracted click data, and training an analytics engine to diagnose the problem using the processed click data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual survey methods are used to collect user feedback, then customer input can be obtained, but the data collection process is slow and incomplete
Solution Approach 1:
The patent replaces manual survey collection and analysis with an automated machine learning system that processes clickstream data. The analytics engine automatically identifies user experience problems by analyzing behavioral patterns, eliminating the need for manual survey administration and data analysis while providing more accurate and timely results.
Solution Approach 2:
The system enables self-service by allowing the analytics engine to autonomously diagnose user experience problems without human intervention. The machine learning model automatically processes raw clickstream data, identifies patterns indicating UX problems, and generates diagnoses, freeing researchers from manual data collection and analysis tasks.
2Quantity of substance
If comprehensive survey data collection is implemented, then more problem information can be gathered, but customer participation decreases
Solution Approach 1:
The patent replaces the mechanical system of manual survey completion with automated passive data collection. The system collects clickstream data automatically as users interact with the website, eliminating the need for customers to actively fill out surveys. This passive collection method increases both the volume of data and customer participation simultaneously.
3Measurement precision
If manual analysis of survey data is performed, then detailed problem identification is possible, but the process requires many hours of human labor
Solution Approach 1:
The analytics engine performs self-service analysis by automatically processing clickstream data and identifying user experience problems without human intervention. The machine learning model extracts meaningful patterns from raw data, diagnoses specific UX issues, and prioritizes them based on impact, replacing manual analysis entirely while maintaining diagnostic detail and significantly improving research efficiency.
Solution Approach 2:
The patent substitutes manual data analysis with an automated machine learning system. The analytics engine processes large volumes of clickstream data rapidly, identifying patterns and diagnosing UX problems that would take researchers hours to analyze manually. This automation maintains diagnostic precision while dramatically increasing productivity.
4Loss of time
If automated click data monitoring is implemented, then research turnaround time is reduced, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the analytics system into distinct functional modules: data collection components that capture clickstream information, processing components that clean and structure the data, and analysis components that apply machine learning models to diagnose UX problems. This modular architecture manages system complexity while enabling rapid automated diagnosis.
Data Source
AI summary
The following relates generally to diagnosing problems with websites. In some embodiments, a webpage interaction processor receives a list of potential user experience problems. The webpage interaction processor then extracts click data from the website, and processes the extracted click data into grams. Subsequently, an analytics engine is trained based on the processed click data. The trained analytics engine may then diagnose the problem of the website with a potential user experience problem from the received list of potential user experience problems. In some embodiments, the process is entirely automated.