Website Search Visibility Scoring With Keyword CTR And Search Volume
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Solution Overview
Problem
Existing technologies lack a comprehensive and accurate method for evaluating and improving search engine visibility (SEV) of websites, particularly in terms of organic traffic, keyword rankings, and click-through-rates (CTR), which hinders effective SEO strategies and competitor benchmarking.
Innovation Solution
A system and method for determining SEV scores using algorithms that combine search volume data, keyword rankings, and CTR data, applying a gamification metric to generate scores understandable by users, and providing graphical user interfaces for visualization and competitor analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data collection (search volume, rankings, CTR) is implemented to improve SEV evaluation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the SEV evaluation process into distinct functional modules: data collection module that gathers search volume, rankings, and CTR data; processing module that applies gamification metrics and algorithms; and output module that generates composite SEV scores. This segmentation allows comprehensive data collection while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components including third-party data analytics providers that supply raw data, and a processing layer that transforms this data into meaningful SEV metrics. These intermediaries simplify the overall system by handling complex data processing tasks and providing standardized interfaces between data sources and the evaluation algorithm.
2Measurement precision
If detailed algorithms combining multiple weighted factors are used to improve SEV score accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system employs parameter-based algorithms that dynamically adjust weights for different factors (search volume, rankings, CTR) based on predefined criteria. The gamification metric applies logarithmic relationships to transform raw data into scaled scores, and the composite SEV calculation uses configurable weight parameters. This approach enables precise measurements while maintaining algorithmic flexibility and manageability.
3Reliability
If real-time polling of third party data analytics providers is performed to improve data freshness, then reliability improves, but use of energy increases
Solution Approach 1:
The system implements periodic polling of third-party data analytics providers at scheduled intervals rather than continuous real-time queries. This periodic action maintains data reliability by regularly updating SEV metrics while significantly reducing computational resource consumption compared to continuous monitoring. The system can adjust polling frequencies based on data volatility and user needs.
4Loss of information
If comprehensive competitor analysis and historical analysis are provided to improve strategic insights, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The patent combines multiple analysis functions into a unified SEV evaluation system: competitor analysis, historical analysis, and real-time performance tracking are integrated into a single platform that processes data through the same gamification metrics and algorithmic framework. This merging reduces overall system complexity by using common processing logic across different analytical functions while providing comprehensive strategic insights.
Data Source
AI summary
Disclosed are techniques for determining search engine visibility (SEV) metrics for a target website. A computer system can: obtain a group of keywords for the website, for each keyword: poll a third party data analytics provider for keyword information, receive the information including keyword position data and search volume data, identify a click-through-rate (CTR) for the keyword based on processing the keyword position data and the search volume data, determine a SEV score for the keyword based on: determining an estimated number of clicks for the keyword based on the keyword position data, the search volume data, and the CTR, determining a gamification value based on a logarithmic relationship between the keyword position data and the search volume data, and determining the SEV score based on the estimated number of clicks and the gamification value, then combine the SEV scores to generate a composite SEV score for the website.