Clickstream Analytics for Application Design Accuracy
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Solution Overview
Problem
There is a disconnect between how applications are developed and how end users interact with them, as developers often lack accurate information about user interactions, leading to inefficient and inaccurate application design.
Innovation Solution
The implementation of an intelligent application process development system that uses clickstream data and predictive analytics to generate recommendations for modifying applications based on user behavior, suggesting improvements such as simplifying processes, reducing repetitive actions, and optimizing feature presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If developers design applications based on prescriptive guidance and assumptions, then application structure and completeness are improved, but accuracy of user interaction modeling deteriorates
Solution Approach 1:
The system implements feedback by capturing actual user clickstream data from the application and using it to generate recommendations for developers. This closed-loop feedback mechanism allows the system to continuously improve application design accuracy by incorporating real user interaction patterns, thereby resolving the contradiction between maintaining structured design and achieving accurate user interaction modeling.
Solution Approach 2:
The system enables self-service by automatically analyzing user clickstream data and generating design recommendations without requiring manual researcher intervention. This automated analysis recovers lost user interaction information and presents actionable insights to developers, improving design accuracy while eliminating the information loss that occurs when relying solely on developer assumptions.
2Ease of manufacture
If developers include all prescribed fields and features in applications, then application completeness is improved, but ease of operation deteriorates
Solution Approach 1:
The system applies partial action by recommending that developers implement only the most frequently used fields and features based on clickstream analysis, rather than all prescribed elements. This allows the application to maintain sufficient completeness for core functionality while removing unnecessary complexity that hinders ease of operation, directly resolving the contradiction between completeness and simplicity.
Solution Approach 2:
The system enables local quality by allowing different parts of the application to have different levels of detail and complexity based on actual user usage patterns. Frequently accessed features receive full implementation while less-used features can be simplified or omitted, resolving the contradiction by applying completeness selectively where it matters most for user operation.
3Loss of time
If developers rely on prescriptive guidance without user behavior data, then development time is reduced, but productivity deteriorates
Solution Approach 1:
The system implements preliminary action by capturing and analyzing user clickstream data in real-time as users interact with the application. This preliminary data collection and analysis occurs during normal usage, allowing developers to receive actionable recommendations without extending development time, thereby resolving the contradiction between quick development and effective application outcomes.
Solution Approach 2:
The system substitutes mechanical user research methods (interviews, surveys, observation) with automated digital analysis of clickstream data. This substitution eliminates time-consuming manual research while providing more accurate and actionable insights, resolving the contradiction by replacing traditional time-intensive productivity measurement methods with efficient automated analytics.
Data Source
AI summary
Methods and systems are provided for modifying an application provided by a cloud-based computing system. The application is used by end users of an organization that is part of the cloud-based computing system. A clickstream monitoring module monitors a clickstream generated by each end user as that end user interacts with the application to generate a set of clickstream data for that particular end user. Each set of clickstream data indicates a path of interaction with features of the application by a particular end user. The sets of clickstream data can then be processed at an analytics engine to extract usage patterns that indicate how end users interact with different features of the application during usage of the application. The extracted usage patterns indicate which features the end users interact with and in what order. An artificial intelligence engine can then generate, based on the extracted usage patterns, at least one recommendation for modifying one or more features of the application to tailor the application for use by the end users in view of the extracted usage patterns.