Sequential Data Segmentation for Web Analytics Precision
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Solution Overview
Problem
Current methods for generating segments in web analytics are inefficient and lack specificity, making it difficult for webpage administrators to create precise segments based on user behavior patterns, leading to unwieldy data management and ineffective marketing strategies.
Innovation Solution
An analytics engine that allows for advanced sequential segmentation, enabling users to define segments based on sequences of hits, events, and visits, with user-defined dimension item values, and repeated events at specific frequencies, providing greater granularity and specificity in segment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to generate segments, then the process is simple, but the specificity and granularity of segments are insufficient
Solution Approach 1:
The patent applies segmentation by breaking down user behavior data into discrete sequential events (hits, events, visits) that can be individually analyzed and combined. The system segments users based on specific sequences of these events, allowing for granular segmentation without requiring complex programming. Each sequential pattern represents a distinct segment criterion that can be independently defined and applied.
Solution Approach 2:
The system dynamically generates segment definitions based on user-selected criteria rather than requiring pre-programmed rules. Administrators can easily change or revise segment criteria by selecting different sequential patterns and dimension item values through a user interface, making the segmentation system adaptable and flexible without technical complexity.
2Measurement precision
If customized segmenting is implemented with specific criteria, then segment precision improves, but the time required to create and revise segments increases
Solution Approach 1:
The system performs preliminary action by pre-defining common sequential patterns (e.g., specific sequences of hits, events, visits) and dimension item values that can be directly selected by administrators. Rather than requiring administrators to program custom rules from scratch, the system provides a library of pre-configured segmentation criteria that can be quickly applied or modified through simple selection, dramatically reducing segment creation time while maintaining precision.
Solution Approach 2:
The system enables self-service segmentation by allowing administrators to independently define and modify segment criteria through an intuitive user interface. Administrators can select from predefined sequential patterns and dimension item values without requiring programmer intervention or complex configuration, making the segment creation process self-sufficient and time-efficient.
3Measurement precision
If advanced sequential segmentation with user-defined dimension item values is implemented, then segmentation granularity increases, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer between the complex analytics engine and the user interface. This intermediary translates user-friendly selections of sequential patterns and dimension item values into the complex queries and processing required by the analytics engine. Administrators interact with simple, standardized options while the intermediary handles the complexity of generating and processing sophisticated segment definitions, shielding users from system complexity while enabling fine-grained segmentation.
Data Source
AI summary
The present disclosure is directed toward systems and methods that allow users to efficiently and effectively create and identify segments of usage patterns. For example, systems and methods described herein allow marketers to query and return sequential segments including sequence conditions based on user-defined dimension item values. Furthermore, systems and methods described allow marketers to query and return sequential segments including sequential events based on user-defined dimension variables. In addition to the foregoing, systems and methods described herein allow marketers to query and return sequential segments defined by repeated events performed at given regularity or frequency.