/seo-cluster

SERP Overlap Methodology

Core Principle

Two keywords that return the same Google results should be targeted by the same page. Two keywords that return completely different results need separate pages. This is the foundation of SERP-based clustering -- using Google's own ranking decisions to determine content architecture rather than relying on keyword text similarity or stemming.

Scoring Algorithm

Step 1: Collect SERP Data

For each keyword in the candidate set, retrieve the top 10 organic results:

• Use WebSearch or DataForSEO serp_organic_live_advanced
• Extract only organic result URLs (ignore ads, featured snippets, PAA, knowledge panels)
• Normalize URLs: strip protocol, trailing slash, and query parameters (except meaningful ones)
• Store as a set of 10 URLs per keyword

Step 2: Pairwise Comparison

For each pair of keywords (A, B):

1. Retrieve the URL sets: urls_A and urls_B
2. Compute overlap: shared = urls_A intersection urls_B
3. Score: overlap_score = len(shared)

Step 3: Apply Thresholds

Step 4: Handle Ambiguous Scores (3-4 Range)

Scores in the 3-4 range require tiebreaking:

1. Check domain overlap (same domains but different pages = closer relationship)
2. Check intent alignment (same intent category = lean toward same cluster)
3. Check volume ratio (if one keyword has 10x+ more volume, it likely deserves its own post)
4. When in doubt, keep in same cluster with separate posts (err toward cohesion)

Optimization Strategy

Full pairwise comparison of N keywords requires N*(N-1)/2 SERP fetches. For 40 keywords, that is 780 comparisons. Optimize by reducing unnecessary checks:

Pre-Grouping

1. Classify all keywords by intent (Informational, Commercial, Transactional)
2. Group keywords that share the same head term (e.g., "CRM software" variants)
3. Only run pairwise SERP comparison within pre-groups
4. Cross-check boundary keywords (highest volume in each group) across groups

Skip Rules

• If keywords A and B are both long-tail variants of the same head term AND share the same intent, assume overlap 4-6 (same cluster) without checking SERP
• If keywords are in different intent categories, assume overlap 0-2 unless they share a head term
• Verify assumptions with spot-check SERP comparisons (sample 20% of skipped pairs)

Scoring Matrix Format

Store the overlap data as a symmetric matrix in cluster-plan.json:

{
  "serp_matrix": {
    "keywords": ["keyword-a", "keyword-b", "keyword-c"],
    "scores": [
      [10, 5, 1],
      [5, 10, 3],
      [1, 3, 10]
    ]
  }
}

Diagonal is always 10 (a keyword overlaps perfectly with itself).

Anti-Patterns

1. Never cluster by text similarity alone. "Dog training tips" and "dog training classes" may have completely different SERPs despite similar text.
2. Never use stemming-only grouping. "Run" and "running" may target different intents entirely.
3. Never assume related searches belong in the same cluster. Verify with SERP data.
4. Never ignore SERP feature differences. If keyword A triggers a local pack and keyword B triggers a featured snippet, they likely need different content types even with moderate URL overlap.
5. Never treat all domains equally. Wikipedia and Reddit appear in many SERPs. Consider filtering out ubiquitous domains (top 5 most common) before scoring, or weighting domain-specific results higher.

Data Source Priority

1. DataForSEO (if available): Most reliable, consistent SERP data. Use serp_organic_live_advanced with location_code: 2840 (US) and language_code: "en".
2. WebSearch (fallback): Adequate for clustering but results may vary by session. Run multiple searches for the same keyword and use the most common result set.

Caching

Within a single clustering session, cache all SERP results. If keyword A's results are fetched for the A-B comparison, reuse them for the A-C comparison. This halves the number of actual SERP fetches needed.