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SEO keyword clustering methods

Analyzed across ChatGPT, Perplexity & Google AIO
Analyzed 11/15/2025

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Brand Performance Across AI Platforms
All 13 brands referenced across AI platforms for this prompt
Clearscope
6
2
Sentiment:
Score:95
Semrush
2
2
Sentiment:
Score:77
Keyword Insights
2
1
Sentiment:
Score:73
4Ahrefs
3
0
Sentiment:
Score:72
5Positional
2
1
Sentiment:
Score:66
Referenced Domains Analysis
All 21 domains referenced across AI platforms for this prompt
ChatGPT
Perplexity
Google AIO
#1semrush.com faviconsemrush.com
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2
#2clearscope.io faviconclearscope.io
ChatGPT:
1
Perplexity:
0
Google AIO:
1
2
#3dashclicks.com favicondashclicks.com
ChatGPT:
0
Perplexity:
1
Google AIO:
1
2
#4aemorph.com faviconaemorph.com
ChatGPT:
0
Perplexity:
1
Google AIO:
0
1
#5ossisto.com faviconossisto.com
ChatGPT:
1
Perplexity:
0
Google AIO:
0
1

AI Search Engine Responses

Compare how different AI search engines respond to this query

ChatGPT

4110 Characters

BRAND (14)

Semrush
精选行业Query
Google
Ahrefs
SE Ranking
Keyword Insights
Clearscope
DashClicks
SurferSEO
IBM Watson
Positional
thruuu
Keyword Planner
Rank Tracker

SUMMARY

ChatGPT provides a structured educational overview of keyword clustering fundamentals, explaining it as an essential SEO strategy for grouping related search terms. The response details specific methods like exact match clustering and semantic clustering, using clear examples such as "cheap flights to Paris" variations. It emphasizes how clustering enhances content relevance and improves search rankings by addressing user intent, though the response appears to be cut off mid-explanation of semantic clustering methods.

Perplexity

3016 Characters

BRAND (14)

Semrush
精选行业Query
Google
Ahrefs
SE Ranking
Keyword Insights
Clearscope
DashClicks
SurferSEO
IBM Watson
Positional
thruuu
Keyword Planner
Rank Tracker

SUMMARY

Perplexity delivers a comprehensive analysis of SEO keyword clustering methods, focusing on grouping keywords by semantic similarity, search intent, and topical relevance. The response covers three main approaches: manual grouping for precision but time-intensive analysis, automated clustering using AI-driven tools for efficiency and accuracy, and clustering by search intent categories including informational, navigational, commercial, and transactional purposes. The platform emphasizes both methodology and practical implementation considerations.

Google AIO

509 Characters

BRAND (14)

Semrush
精选行业Query
Google
Ahrefs
SE Ranking
Keyword Insights
Clearscope
DashClicks
SurferSEO
IBM Watson
Positional
thruuu
Keyword Planner
Rank Tracker

SUMMARY

Google AIO provides a concise analytical summary of keyword clustering, defining it as grouping related keywords by search intent to create comprehensive content that ranks for multiple queries. The response outlines key methodological categories including manual analysis based on semantic relevance, automated clustering using SERP analysis and machine learning, and emphasizes the importance of starting with broad keyword lists and utilizing appropriate tools for implementation.

Strategic Insights & Recommendations

Dominant Brand

Clearscope dominates the brand mentions with 6 references, followed by Ahrefs and Positional with 2 mentions each, indicating these are the most recognized tools in keyword clustering discussions.

Platform Gap

ChatGPT provides the most detailed educational content with specific examples, while Perplexity offers comprehensive methodology coverage, and Google AIO delivers the most concise analytical overview.

Link Opportunity

All platforms provide substantial linking opportunities with ChatGPT offering 6 links, and both Perplexity and Google AIO providing 9 links each, indicating strong potential for reference-based content strategies.

Key Takeaways for This Prompt

Keyword clustering is universally recognized as a fundamental SEO strategy across all platforms for improving content relevance and search rankings.

Manual clustering methods offer precision but are time-intensive, while automated approaches using AI and machine learning provide efficiency and scalability.

Search intent categorization (informational, navigational, commercial, transactional) is a critical component of effective keyword clustering strategies.

The combination of semantic analysis, SERP data, and machine learning algorithms represents the current best practice for automated keyword clustering implementation.

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