Finding Content Gaps from Google SERP

Analyzes Google SERP results to identify where existing content is weak, outdated, or missing key information — these are the gaps where new content can rank quickly.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Workflow

Progress:
- [ ] Step 1: Run google-search-scraper for all keywords
- [ ] Step 2: Analyze SERP result quality signals
- [ ] Step 3: Score content gap opportunity per keyword
- [ ] Step 4: Cluster gaps by theme
- [ ] Step 5: Deliver prioritized content gap report

Step 1: Scrape SERPs

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~google-search-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~google-search-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~google-search-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~google-search-scraper"
Input:
{
  "queries": ["[KEYWORD_1]", "[KEYWORD_2]", "..."],
  "maxPagesPerQuery": 1,
  "countryCode": "US",
  "includePeopleAlsoAsk": true
}

REST API fallback:

curl -X POST   "https://api.apify.com/v2/acts/apidojo~google-search-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"queries": ["[KEYWORD_1]", "[KEYWORD_2]"], "maxPagesPerQuery": 1}'

Step 2: Score SERP Quality

For each keyword, assess the top 10 results:

Weakness signals (from title + URL):

stale_content = title or URL contains year < (current_year - 2)
low_authority_domain = not in [known authority list] (e.g. Wikipedia, Forbes, HubSpot, Moz, etc.)
thin_content_signal = page title is vague or generic (no specific angle)
forum_or_QA = result is from Reddit, Quora, AnswerThePublic (not editorial)

gap_score = (stale_content_count / 10) * 0.30
          + (low_authority_count / 10) * 0.25
          + (forum_qa_count / 10) * 0.20
          + (no_featured_snippet ? 1 : 0) * 0.25

Tier: HIGH OPPORTUNITY (score ≥ 0.60) | MODERATE (0.35–0.59) | LOW (< 0.35)

Step 3: Edge Cases

• All top 10 results are from major authorities: Gap score will be low — this is accurate; don't recommend targeting this keyword without a unique angle
• SERP dominated by one domain: Note monopoly; this keyword may be unfairly dominated; consider long-tail variants
• Keyword returns mixed intent (informational + transactional): Separate the analysis — informational content has different gap criteria than product pages

Output Format

# Content Gap Analysis: [KEYWORD_SET]
Keywords analyzed: [N] | High opportunity: [N] | Moderate: [N] | Low: [N] | Date: [DATE]

## High-Opportunity Keywords (Best Investment)
| Keyword | Gap Score | Stale Results | Low Auth | No Snippet | Recommended Angle |
|---------|----------|--------------|---------|-----------|------------------|
| [keyword] | [0.XX] | [N]/10 | [N]/10 | Yes | "[content angle]" |

## Content Brief Recommendations

### 1. "[Keyword]" (Gap Score: [X])
Current top result: [title] ([year if stale]) from [domain]
Gap: [stale/thin/no snippet/forum-dominated]
Recommended content: [format + angle + target length]

Troubleshooting

All gaps score low: Target keywords may be well-served by existing content — pivot to long-tail variations or sub-topics. Stale content from authority domains still ranks well: Google may not penalize age heavily for that topic; assess based on whether the content actually answers the user's question. Gap analysis is subjective: The weakness signals are proxies; manually review the top 3 results for your top 5 keywords to validate before committing to content production.