Search Query Analysis
Definition
Search query analysis is the systematic study of the search terms users enter into a knowledge base's internal search to extract insights about user needs, vocabulary, and content gaps. Analyzing search queries reveals: what users are looking for (validating or challenging assumptions about support needs), how users phrase questions (informing content titles and keywords), which searches return no results (identifying content gaps), which searches return results users do not click (indicating result quality issues), and search volume patterns over time (revealing emerging support trends). This analysis is foundational for knowledge base strategy and content planning.
Why It Matters
Search query analysis provides the most direct signal available about what help users need that the knowledge base is not providing. When users type a query and find no relevant results, they must either contact support, search externally, or abandon their task. Identifying these zero-result queries and high-effort queries (multiple searches before resolution) enables content teams to target their creation efforts precisely where user need is highest. For AI chatbots, search query data also helps identify which types of questions the AI is struggling to answer, guiding both content improvements and AI configuration adjustments.
How It Works
Search query analysis is conducted using the analytics features of knowledge base platforms, which log all search queries along with result counts, click-through data, and session outcomes. Content managers export query data and analyze it for patterns: grouping similar queries, identifying queries with high volume but low result click-through (indicating poor search relevance), isolating zero-result queries, and mapping queries to existing articles to find coverage gaps. Many teams establish a weekly or monthly ritual of reviewing the top zero-result queries and assigning new article creation tasks for the most common ones.
Search Query Analysis Report
| Query | Volume | Click Rate | Zero Results | Avg Position |
|---|---|---|---|---|
| reset password | 1,240 | 68% | No | 1.2 |
| export data csv | 890 | 41% | No | 3.8 |
| saml sso setup | 430 | 22% | Yes | — |
| webhook retry limit | 310 | 17% | Yes | — |
| billing invoice pdf | 270 | 55% | No | 2.1 |
Insights
High-volume zero-result queries
"saml sso setup" (430 searches) — create a new article
High-volume zero-result queries
"webhook retry limit" (310 searches) — create a new article
Low click rate — ranking or snippet issue
"webhook retry limit" and "saml sso setup" — improve title & description
Real-World Example
A 99helpers customer runs a monthly search query analysis on their knowledge base and discovers that the query 'how to export my data' receives 150 searches per month but has a zero-results rate of 100%. They have three articles about data, none of which mention 'export' prominently. They create a dedicated 'How to Export Your Data' article and update their existing data articles with 'export' keywords. The following month, the zero-results rate for this query drops to 0% and 45 support tickets about data exports are prevented.
Common Mistakes
- ✕Analyzing only zero-result queries while ignoring high-effort multi-step searches — users who search multiple times before finding an answer are also experiencing a knowledge base problem
- ✕Treating query analysis as a one-time audit instead of an ongoing practice — user search behavior evolves as the product and user base change
- ✕Optimizing article titles for search queries without ensuring the article content actually answers those queries
Related Terms
Knowledge Base Analytics
Knowledge base analytics tracks how users and AI systems interact with knowledge base content — measuring article views, search queries, resolution rates, feedback ratings, and content gaps. These insights drive continuous improvement of both the content and the AI chatbot powered by it.
Zero-Results Rate
Zero-results rate is the percentage of knowledge base searches or AI retrieval queries that return no relevant results. It is a direct measure of knowledge gaps — every zero-results query represents a user question that the knowledge base cannot answer and a specific, actionable opportunity to create new content.
Content Gap Analysis
Content gap analysis is a systematic review of what topics a knowledge base covers versus what users are actually asking — identifying areas where content is missing, insufficient, or outdated. It combines analytics data, chatbot logs, and user feedback to prioritize new content creation.
Knowledge Base Optimization
Knowledge base optimization is the ongoing process of improving a knowledge base's content quality, structure, and coverage to maximize AI chatbot accuracy and user self-service success rates. It involves analyzing search failures, filling content gaps, improving article clarity, and retiring outdated content.
Knowledge Base Search
Knowledge base search is the capability that enables users to find relevant articles, and enables AI systems to retrieve relevant content to answer questions. Effective search combines full-text keyword matching with semantic understanding — finding relevant content even when users use different words than those in the articles.
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