Search Query Lifetime Value Analysis for Content Title Selection
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Solution Overview
Problem
Identifying content with the greatest potential for generating revenue is challenging due to the sheer volume of data in query logs, which precludes manual processing, and the lack of subject matter experts for certain topics.
Innovation Solution
Analyzing query logs to estimate the lifetime value (LTV) of search queries by matching them to search terms with known LTVs, selecting queries with high LTV as potential titles for online content, and using a matching score to determine the revenue-generating potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of query logs is attempted, then analysis accuracy may be maintained, but processing capability becomes insufficient due to the sheer volume of data
Solution Approach 1:
The patent replaces manual mechanical processing of query logs with automated computer-based processing. The system uses algorithms to automatically analyze query logs, extract search terms, and estimate lifetime values without human intervention, thereby handling large volumes of data efficiently while maintaining consistent analysis standards.
Solution Approach 2:
The system enables self-service processing where the computer automatically performs data collection, analysis, and title identification without requiring manual operation. The automated pipeline processes query logs, matches search terms against databases, calculates lifetime values, and generates content titles autonomously, freeing operators from manual tasks.
2Measurement precision
If subject matter experts are used to identify valuable topics, then topic expertise is available, but their availability is limited and they cannot provide quantitative revenue estimates
Solution Approach 1:
The patent introduces an intermediary automated system that bridges the gap between limited expert knowledge and the need for comprehensive quantitative analysis. The system uses databases of known lifetime values for search terms as intermediaries to translate expert-identified topics into precise revenue estimates, allowing quantitative measurement without requiring continuous expert involvement.
Solution Approach 2:
The system transforms qualitative expert judgments about topic value into quantitative lifetime value estimates by matching search terms against a database with known LTV parameters. This parameter transformation allows precise measurement of revenue potential for any search term, extending beyond what individual experts can estimate.
3Measurement precision
If all search queries are analyzed in detail, then comprehensive revenue identification is achieved, but processing time and resources become excessive
Solution Approach 1:
The patent extracts only the essential elements from query logs - specifically search terms and their matching against the LTV database - rather than analyzing all aspects of each query in detail. This extraction approach identifies the key revenue-related information needed while discarding unnecessary data, enabling efficient processing of large query volumes.
Solution Approach 2:
The system performs partial analysis by focusing only on matching search terms against the lifetime value database, rather than conducting exhaustive analysis of each query's full context. This partial action approach processes queries faster by applying only the necessary analysis steps to identify revenue potential, accepting that not all query dimensions are examined in depth.
Data Source
AI summary
Systems and methods are provided to select potential titles for online content using search query logs from web search service providers. A plurality of search queries are collected from one or more web search service providers. A lifetime value is determined for each of the search queries. Potential titles are then selected from the plurality of search queries using selection criteria including the lifetime value of the search queries. The potential titles can then be provided to content developers who develop online content based on the potential titles.