Content Consolidation Analyser

Use cases

Identifying keyword cannibalisation issues Planning content consolidation projects Improving rankings by merging competing pages Reducing content debt on large sites

Identifies pages competing for the same keywords by analysing which URLs appear together in search results.

Uses connected component detection and clique analysis to group competing pages, then scores each cluster 0-100 with actionable recommendations from "Strong consolidation candidate" to "Keep separate".

Platform

Python script (requires Python 3.x)

Input

SERP or GSC data CSV

Output

CSV with cluster IDs and consolidation recommendations

Features

SERP overlap analysis with configurable thresholds
Connected component and clique detection algorithms
Consolidation scoring 0-100 with actionable recommendations
Connectivity metrics (actual vs possible query connections)
Supports overlapping clusters for multi-topic pages

How to use

1 Export SERP data or GSC rankings showing which pages rank for which queries
2 Upload the CSV to the analyser
3 Set minimum shared URLs threshold for clustering sensitivity
4 Run the clustering algorithm
5 Review clusters with consolidation scores and recommendations
6 Export results with cluster IDs and actionable next steps

Want me to run this for you?

I offer this as a managed service. You get the insights without touching the tool.

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