Visitor Session Classification via Clickstream Distance Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current market segmentation strategies for online commercial entities, based on static customer characteristics, often fail to significantly increase conversion rates, resulting in limited economic return due to their inefficiency in identifying potential customers likely to engage in transactions.
Innovation Solution
A visitor session classification system that organizes web pages into categories, divides clickstreams into sessions, calculates mathematical distances between sessions using visitation metrics like click counts and durations, and employs algorithms like LMNN and kNN to classify sessions into target and non-target groups for targeted marketing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If market segmentation is based on static customer characteristics, then the segmentation process is simple to implement, but the conversion rates do not significantly increase
Solution Approach 1:
The patent transitions from static customer characteristics to dynamic clickstream-based segmentation. By analyzing real-time visitor behavior, page views, and interaction patterns, the system creates dynamically updating customer segments that adapt to changing visitor intentions and preferences, thereby significantly improving conversion rates while maintaining operational simplicity through automated processing.
Solution Approach 2:
The invention changes the segmentation parameters from fixed demographic attributes to variable behavioral metrics including clickstream data, time spent on pages, navigation patterns, and interaction frequency. This parameter transformation enables the system to identify high-value customer segments with precision, directly addressing the conversion rate problem while keeping the implementation straightforward through algorithmic processing.
2Quantity of substance
If resources are invested in market segmentation using static characteristics, then the segmentation process can be performed, but the economic return is limited
Solution Approach 1:
The system implements feedback loops where clickstream data from customer interactions continuously informs and refines segmentation categories. This feedback mechanism ensures that marketing resources are dynamically reallocated to the most responsive segments, maximizing economic return on investment by focusing efforts on high-conversion groups identified through real-time behavioral analysis rather than static demographics.
Solution Approach 2:
The automated clickstream analysis system performs segmentation self-service without requiring extensive manual intervention. The algorithm automatically processes visitor data, identifies patterns, and creates segments, reducing the labor resource investment needed while significantly improving the economic return through more effective target identification and marketing campaign optimization.
3Productivity
If dynamic clickstream analysis is used for market segmentation, then conversion rates increase significantly, but the system complexity increases
Solution Approach 1:
The patent replaces complex manual segmentation processes with automated computational algorithms that process clickstream data. This substitution of mechanical human analysis with algorithmic processing maintains system simplicity despite the sophistication of the analysis, enabling significant conversion rate improvements through automated pattern recognition and segment classification without proportionally increasing operational complexity.
4Measurement precision
If detailed clickstream data is collected and analyzed, then customer segmentation accuracy improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system extracts only the most relevant behavioral features from extensive clickstream data, such as key navigation patterns, time-on-page metrics, and interaction frequencies. This selective extraction approach maintains high segmentation accuracy by focusing on discriminative features while reducing the complexity of data processing by eliminating redundant information, thereby balancing measurement precision with analytical feasibility.
Data Source
AI summary
Example systems and methods of classifying web visitor sessions based on clickstreams are presented. In one example, a plurality of web pages of a website is organized into a plurality of web page categories. A clickstream of each visitor to visit the plurality of web page categories of the website are divided into a plurality of visitor sessions. A mathematical distance between each of the plurality of visitor sessions is determined using a visitation metric based on the web page categories. Each of the visitor sessions is classified into a target group or a non-target group based on the mathematical distance between each of the visitor sessions and on an identification of at least one of the visitor sessions with an event corresponding to the target group.