Web Analytics Pricing Optimization via Competitor Response Detection
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Solution Overview
Problem
Current price optimization and sensitivity analysis techniques in retail pricing rely primarily on historical data and consumer website click streams, failing to account for competitors' reactions to price changes, leading to sub-optimal pricing that can result in low sales or poor margins due to the complexity of analyzing massive competitive intelligence data.
Innovation Solution
A system that automates data mining across unbounded internet domains to gather competitive pricing data, uses wrapper induction and matching methodologies to identify equivalent products, and performs price sensitivity analysis to derive optimized pricing strategies by analyzing consumer navigation behavior and competitor responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retailers analyze massive competitive intelligence data to determine optimal pricing, then pricing accuracy improves, but the complexity of data analysis increases significantly
Solution Approach 1:
The patent introduces web analytics as an intermediary measurement tool that indirectly captures consumer behavior patterns and competitor responses. Instead of directly analyzing complex competitive intelligence data, the system uses web analytics proxies (click streams, conversion rates, time on page) to infer pricing sensitivity and competitive dynamics, thereby reducing direct analysis complexity while maintaining pricing accuracy
Solution Approach 2:
The patent replaces complex mechanical data analysis systems with automated web analytics tracking. Rather than manually analyzing massive datasets of competitive pricing information, the system automatically captures and analyzes web interaction data to derive pricing insights, substituting complex manual analysis mechanisms with automated digital tracking and processing
2Ease of operation
If retailers use historical pricing data and consumer click stream data for price optimization, then implementation ease improves, but the ability to predict competitor reactions deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring web analytics data to detect real-time consumer responses to price changes and competitor actions. The system uses this feedback loop to dynamically adjust pricing strategies and predict competitor reactions, moving beyond static historical data analysis to dynamic, real-time adaptive pricing that accounts for competitor behavior patterns
3Speed
If retailers monitor competitor prices in real-time across multiple domains, then pricing responsiveness improves, but data collection complexity increases
Solution Approach 1:
The patent creates a universal web analytics framework that serves multiple functions: tracking consumer behavior, monitoring competitor pricing, analyzing conversion rates, and measuring time on page. By consolidating these functions into a single multi-functional analytics system, the patent reduces the complexity of collecting and processing multiple separate data streams while maintaining real-time pricing responsiveness across all monitored domains
Data Source
AI summary
A method includes generating a control set, based on input received via a user interface, the control set comprising at least a first product of a product category type. The method further includes generating a test set, based on input received via the user interface, the test set comprising at least a second product of the product category type other than the first product in the control set. The method further includes changing a feature of the second product in the test set, the feature being visible on a web page via the internet, while maintaining the feature of the first product in the control set. The method further includes measuring competitor or consumer responses to the changing. The method further includes generating a recommendation based on the measured competitor or consumer responses.