Competitors reveal strategy in the categories they occupy, the tech they deploy and the buyers their content courts. Read it all systematically: discovery across 102M classified domains, stack comparisons and audience segmentation that decodes whose ICP a rival targets.
Product marketing keeps a battlecard for the three competitors sales mentions most. Meanwhile the category fills with unknowns, a rival repositions toward your best segment, and the evidence arrives as a lost deal.
Tomorrow's threats are rarely on today's battlecard. Keyword search finds the best SEO, not the closest category match.
Build-vs-buy choices, vendor dependencies and GTM tooling are visible in a site's tech fingerprint — if anyone checks systematically.
A rival moving upmarket telegraphs it in content months before the pricing page changes. Classified snapshots catch that drift.
Ad-hoc reviews produce slide decks, not datasets. Without a consistent schema you cannot diff this quarter against last.
Manual monitoring — someone skimming blogs on Fridays — does not scale and leaves no structured record to compare quarter over quarter.
Each layer produces structured, repeatable output — so competitive intelligence becomes a dataset you query and diff, not a deck you rebuild.
The competitor finder surfaces domains in your categories across the 102M-domain database.
The technology detector fingerprints each competitor's commerce platform, analytics, marketing automation and A/B tooling.
POST /api/audience/segment.php on a competitor's pages returns b2b_signals: target roles, company size and industry focus.
content_context.brand_mentions reveals which vendors a competitor names — partners, integrations, or you.
A trimmed audience-segmentation response for a security vendor's blog. One call returns target roles, company-size band, priority industries and brand mentions.
import requests
resp = requests.post(
"https://www.websitecategorizationapi.com/api/audience/segment.php",
data={
"query": "https://www.crowdstrike.com/en-us/blog/",
"api_key": API_KEY,
},
timeout=120,
)
profile = resp.json()["audience_segmentation"]
battlecard.update(
competitor="crowdstrike.com",
icp_roles=profile["b2b_signals"]["target_role"],
icp_size=profile["b2b_signals"]["target_company_size"],
mentions=profile["content_context"]["brand_mentions"],
)
{
"url": "https://www.crowdstrike.com/en-us/blog/",
"audience_segmentation": {
"b2b_signals": {
"is_b2b_content": true,
"confidence": 0.9,
"target_role": ["IT Manager", "Security Analyst", "CISO"],
"target_company_size": "mid-market",
"industry_focus": ["Technology", "Finance", "Healthcare"]
},
"purchase_intent": [
{"category": "cybersecurity solutions", "confidence": 0.8}
],
"content_context": {
"content_type": "blog_article",
"writing_style": "professional",
"reading_level": "advanced",
"brand_mentions": ["CrowdStrike"],
"sentiment": "neutral"
},
"iab_categories": [
"Technology > Cybersecurity",
"Business > IT Services"
]
},
"status": 200
}
When target_company_size shifts from "mid-market" to "enterprise", you have spotted an upmarket move before it hits their pricing page.
Combining the layers yields a comparison grid that regenerates itself (figures illustrative).
| Signal | You | Competitor A | Competitor B |
|---|---|---|---|
| Primary category | Tech > DevOps Tooling | Tech > DevOps Tooling | Tech > Enterprise Software |
| ICP roles targeted | Platform engineer, SRE | Platform engineer, CTO | CIO, IT Manager |
| Company size courted | Mid-market | Mid-market → enterprise | Enterprise |
| Traffic tier | Tier 2 | Tier 2 | Tier 1 |
| Marketing stack | HubSpot, GA4 | Marketo, Drift, 6sense | Adobe stack |
| Mentions you? | — | Yes, comparison pages | No |
Competitor A's content now courts enterprise buyers and their stack added an ABM vendor.
Two independent signals of the same move — each captured automatically with a timestamp.
Candidates share your category assignments in the domain database, ranked by overlap depth. Filter by traffic tier, company size or business model to tighten the net.
The platform reads publicly served pages — the same material any analyst or prospect sees in a browser. It does not access gated content or private systems.
Schedule the API calls. A monthly job re-segments each rival's key pages, re-fingerprints their stack and diffs against the stored profile. Changes land in Slack or your BI tool. The real-time engine answers breaking questions in seconds.
Name a rival and we send back their profile — categories, technology stack, audience segmentation and discovered lookalikes — in the same format the API produces.
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