Automated Competitor Classification via Product Overlap and Traffic Analysis
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Solution Overview
Problem
Retailers face difficulties in determining relevant competitors for specific product groups due to the vast number of products and fluctuating prices across numerous retailers, making it challenging to maintain competitive pricing strategies.
Innovation Solution
A system that classifies competitors by analyzing product overlap, estimated traffic, average review ratings, and website views to identify relevant competitors for a given product group, using wrapper induction and product matching methodologies to automate data mining across unbounded domains and ensure accurate price comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If retailers monitor pricing across thousands of retailers and products manually, then comprehensive competitive intelligence is obtained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual monitoring processes with an automated web crawler system that uses spiders to navigate and extract pricing information from retailer websites. The system automatically scrapes product data, prices, and inventory information without human intervention, substituting mechanical manual operations with automated computational processes.
Solution Approach 2:
The system performs self-service by automatically maintaining its own operation through scheduled crawling cycles, self-updating product catalogs, and autonomous price monitoring. The web crawler independently navigates retailer websites, extracts data, and updates the competitive intelligence database without requiring continuous manual oversight or intervention.
2Productivity
If retailers track pricing changes in real-time across multiple products, then responsive pricing strategies are enabled, but system complexity and resource requirements increase
Solution Approach 1:
The patent segments the monitoring system into distinct functional modules: a web crawler for data collection, a product matching module for identifying relevant products, a price monitoring module for tracking changes, and a reporting module for analysis. This segmentation allows each component to operate independently and reduces overall system complexity while maintaining high productivity.
Solution Approach 2:
The system performs preliminary actions by pre-crawling and storing product catalogs, price histories, and competitor information in databases before actual pricing decisions are needed. This advance data preparation enables rapid response to pricing changes without requiring complex real-time processing during critical decision moments.
3Adaptability or versatility
If the system crawls and processes data from numerous unbounded domains, then comprehensive competitor coverage is achieved, but data processing complexity and accuracy challenges increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting crawl depth, data extraction fields, and processing thresholds based on the specific retailer domain and product category. The system modifies its behavior parameters when encountering different website structures or data formats, allowing comprehensive coverage while maintaining accuracy through adaptive processing parameters.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor data quality, validation results, and processing errors from various domains. When anomalies are detected in scraped data or when certain domains prove difficult to parse, the system adjusts its crawling strategy and data extraction parameters accordingly, feedback-driven refinement that maintains accuracy across diverse unbounded domains.
Data Source
AI summary
Competitors are classified in terms of products the competitors offer. A product set is generated from product information received from a user. Also, a competitor set is generated, where the competitor set comprises at least one competitor determined to be relevant to one or more products in the product set. A target price rule is generated that is operative to change a price offered by the user for the at least one product. A competitor's relevancy can be determined by considering factors such as: (1) unique visitors to the competitor's website, (2) reviews on the competitor's website (3), ratings on the competitor's website, (4) absolute number of products common to the user's website and the competitor's website, (5) percentage number of products common to the user's website and the competitor's website, and (6) number of products offered by the competitor that comprise the product set.