Contextual Intent
Web-crawled signals of research activity by topic
What is it
Contextual Intent captures WHAT TOPICS COMPANIES ARE ACTIVELY RESEARCHING based on web activity signals. HG tracks ~50 million intent signals across thousands of topics — from broad categories ("cloud migration") to specific products ("Snowflake alternatives").
Unlike cookie-based intent that tracks individuals, contextual intent resolves to the COMPANY level, showing organizational research patterns.
What problem it solves
Knowing what a company HAS (technographics) isn't the same as knowing what they're EVALUATING. Contextual intent bridges that gap — it tells you when a company is in-market for a solution, researching a problem, or exploring alternatives to their current stack.
What you can do with it
- In-market targeting: Find companies actively researching your category
- Timing outreach: Reach out when research spikes, not when the decision is already made
- Competitive alerts: Get notified when accounts research competitors
- Content strategy: Understand what topics your ICP is researching
- ABM campaigns: Layer intent on top of firmographic and technographic targeting
Real-world example
ABM Campaign Prioritization
A marketing automation vendor has 500 target accounts. Using contextual intent, they identify 47 accounts showing elevated research activity around "marketing attribution" in the past 30 days. These accounts get prioritized for SDR outreach and retargeting ads. Result: 4x higher meeting rate vs. accounts without intent signals.
What the data looks like
Salesforce Intent Signals (Last 30 Days)
TOP INTENT TOPICS
CONTEXT TYPE BREAKDOWN
| Context Type | Count | Meaning |
|---|---|---|
| Expansion | 23 | Researching adjacent/complementary products |
| Complementary | 14 | Products that integrate with existing stack |
| Displacement | 2 | Researching alternatives to current tools |
| Whitespace | 0 | New category, no current solution |
Key fields you get
- Topic metadata: Topic name, topic ID, vendor/product associations
- Intent score: 0-100 scale indicating signal strength
- Intent level: Low / Medium / High (urgency indicator)
- Buyer's journey stage: Researching / Evaluating / Deciding
- Context type: Whitespace, expansion, displacement, complementary
- Recency: Last seen date, trend direction
- Source: HG (web) vs. TrustRadius (first-party)
“Salesforce is showing HIGH intent on 287 topics in the past 30 days. They're researching 23 expansion opportunities (adjacent products) and 14 complementary tools (integrations). Only 2 displacement signals (evaluating alternatives). If your product is complementary to Salesforce's stack or an adjacent category, they're actively researching. The HIGH intent + Evaluating stage signals mean they're past awareness — timing is good for outreach.”
Business impact
Increases: Higher meeting rates when outreach aligns with active research
Decreases: Shorter sales cycles by engaging during active evaluation
Increases: Higher campaign click-through when paired with intent
Increases: Better pipeline quality from intent-qualified accounts
Directional outcomes HG customers commonly report. Actual results vary by program and data application.
Under the hood
How it's unique to the market
HG's contextual intent is topic-level AND product-level — we can show research on specific products, not just categories. And because it's integrated with technographics, you can filter for intent signals that are RELEVANT (e.g., "researching Salesforce alternatives" at companies that actually use Salesforce).
How it's derived
Contextual intent is derived from web-crawled content signals — job postings, tech documentation, blog posts, and web mentions that indicate research activity. Topics are classified and resolved to companies via domain/IP matching. Signals are aggregated and scored by recency and volume to identify companies showing elevated research interest.