Competitor Blog Topic Gap Finder

Convert competitor blog pages into content-gap rows your marketing team can act on quickly.

Who Uses It

SEO consultants, content teams, SaaS marketers, agencies, and founders planning editorial calendars.

Why It Is Useful

This actor does not save a generic web-scrape dump. It saves a compact dataset shaped around one business job, so the output is easier to review, export, and plug into research workflows.

Input

• startUrls - Public pages to inspect.
• logLines - Pasted snippets for fast tests or sources that do not need crawling.
• maxPagesPerStartUrl - Same-domain pages to inspect from each URL.
• maxResults - Maximum clean rows to save, capped at 20.
• proxyConfiguration - Optional Apify proxy settings.

Output Fields

• competitorTopic - The competitor topic or article idea worth reviewing.
• keywordCluster - Topic cluster for grouping ideas into an editorial plan.
• funnelStage - Likely funnel stage for the content opportunity.
• suggestedArticleAngle - A concise angle your team can turn into a better brief.
• gapOpportunity - Why the topic may be useful to cover or compare.
• competitorArticleUrl - Direct article/listing URL when available.
• competitorSite - Competitor domain for grouping results.

Example Output

{
    "competitorTopic": "SEO checklist guide for B2B SaaS teams",
    "keywordCluster": "SEO",
    "funnelStage": "top of funnel",
    "suggestedArticleAngle": "Build a better tactical checklist with SaaS examples and conversion steps.",
    "gapOpportunity": "Competitor covers this topic; compare against your own editorial plan.",
    "competitorArticleUrl": "https://example.com/blog/seo-checklist",
    "competitorSite": "example.com"
}

Use Cases

• Find competitor topics you have not covered yet.
• Sort ideas by cluster and funnel stage.
• Turn competitor article angles into better briefs for your writers.

Limits

The actor works best with public pages or focused pasted snippets. Login walls, CAPTCHA, heavy anti-bot protection, and very noisy pages can reduce result quality.

Pricing

Recommended pricing is paid per saved dataset item, so users pay for useful rows instead of empty page visits.