Vertical Content Demand Measurement via Keyword Segmentation
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Solution Overview
Problem
Current web analytics tools and services are unable to effectively measure consumer content demand across multiple websites within a vertical category, focusing either on demographics, website traffic statistics, or consumer sentiment, without providing detailed insights into product-level trends or consumer visits to specific web pages.
Innovation Solution
A system and method that utilize natural language processing and JavaScript tags to identify and measure trends in consumer content demand by aggregating data on keyword occurrences, consumer traffic, and engagement metrics across vertically associated websites, providing metrics such as occurrence, geographics, velocity, engagement, reach, and location for specific products or brands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional web analytics tools focus on demographics and website traffic statistics, then they can provide site-level rankings and traffic patterns, but they cannot measure product-level trends or consumer visits to specific web pages across multiple websites
Solution Approach 1:
The system segments the measurement task into two distinct components: (1) text mining component that identifies keyword occurrences in web page content using natural language processing, and (2) traffic measurement component that tracks consumer visits to web pages containing those keywords. This segmentation allows each component to specialize and achieve high precision in its respective function while working together to solve the overall problem of product-level trend measurement across multiple websites.
Solution Approach 2:
The system introduces keyword occurrences in web page content as an intermediary element that bridges content analysis and traffic measurement. By first identifying which web pages contain relevant product keywords through text mining, the system can then measure traffic to those specific pages, thereby indirectly measuring product-level demand without requiring direct product tracking on each website.
2Quantity of substance
If companies aggregate web traffic data from multiple ISPs and panel data, then they can provide industry-level traffic patterns, but they cannot determine actual consumer visits to specific blog or social network pages containing keywords
Solution Approach 1:
The system establishes a feedback loop where JavaScript tags embedded in web pages continuously report back consumer visit data to the analytics server. When a consumer visits a web page containing a tracked keyword, the JavaScript tag detects this visit and sends feedback information including the URL, timestamp, and consumer identifiers. This feedback mechanism enables accurate tracking of actual consumer visits to specific pages across multiple websites, transforming passive data collection into active verification.
Solution Approach 2:
The system replaces traditional mechanical data collection methods (such as server-side log analysis or panel surveys) with electronic JavaScript-based tracking. The JavaScript tags embedded in web pages automatically capture and transmit visit information, eliminating the need for manual data collection or complex server log parsing. This substitution enables scalable, automated tracking of consumer visits across numerous websites and page types including blogs and social networks.
3Loss of information
If web analytics tools use JavaScript tags to monitor consumer behavior, then they can collect detailed click and navigation data, but they cannot effectively measure content demand trends across vertically associated websites
Solution Approach 1:
The system creates a universal measurement framework that can simultaneously track multiple types of content demand information across different vertical categories. The JavaScript tagging system is designed to work across diverse website types (e-commerce, content, social media, blogs) and can measure various metrics including page views, time on page, and navigation patterns. This universal approach allows the system to aggregate data across vertically associated websites and derive comprehensive content demand trends that apply across the entire vertical category.
Solution Approach 2:
The system adds a new dimension of analysis by combining traditional traffic metrics with content-specific keyword information. Instead of merely measuring overall site traffic, the system enriches the data by identifying which specific keywords appear on visited pages and how consumers interact with content containing those keywords. This additional dimensional information (keyword-context-traffic combination) enables the system to measure content demand trends rather than just aggregate traffic patterns, providing deeper insights into consumer preferences within vertical categories.
Data Source
AI summary
Embodiments of the invention can provide systems and methods for identifying and measuring trends in consumer content demand within a vertical network of websites and related content. In one embodiment, a method can include receiving content from webpages in at least one vertical; receiving data associated with a plurality of selected keywords associated with the at least one vertical, wherein one or more associations between at least a portion of the plurality of selected keywords can be generated; receiving session data associated with a plurality of consumers accessing content in webpages in the at least one vertical; determining third party data associated with the plurality of consumers accessing at least a portion of webpages comprising at least one of the selected keywords; and aggregating, based at least in part on the third party data, session data associated with visits to the webpages comprising at least one of the selected keywords.