Intent Intelligence

Buyer Intent Signals

Company and contact-level buyer intent across 38,000+ B2B topics. Every company-topic pair gets a 0-100 intent score with early and late buying stage labels.

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21M+

Companies Monitored

38,000+

Topics Available

6B+

Daily Behavioral Signals

310M+

Contact IDs

Intent Intelligence38,000+ topics subtypes · Daily refresh

What Is Buyer Intent Data?

Buyer intent data captures the digital research behavior of companies and individuals as they evaluate solutions in your category: reading vendor comparisons, researching implementation topics, consuming category content. That behavior is mapped to a taxonomy of 38,000+ B2B topics spanning products, technologies, and business problems.

Every company-topic pair gets an intent score from 0 to 100 with an Early or Late stage label, so you know both how intense the research is and how far along the evaluation is. Unlike providers that stop at account-level scores, Autobound also delivers contact-level intent, revealing which individuals are driving the evaluation. Intent is strongest layered with other signals: high intent plus a fresh raise and 15 open SDR roles is a different prospect than intent alone.

Example Signal Subtypes

companyIntentcontactIntenttopicSurgestageTransitioncompetitorResearchcategoryEntrybuyingGroupFormationchurnRisk

Data Schema

Intent Signal Data Schema

Every intent signal includes topic classification, composite scoring, stage detection, and temporal trends. Here is a real example from our production database.

web_intent.schema.json

{
  "topic_id": "b2b_35642",
  "topic": {
    "id": "b2b_35642",
    "name": "Sales Engagement Software",
    "category": "Business",
    "subcategory": "Sales",
    "is_product": false
  },
  "intent_score": 87,
  "stage_label": "Late",
  "signal_week": "2026-07-27",
  "company": {
    "domain": "snowflake.com",
    "name": "Snowflake",
    "industry": "Software Development",
    "revenue": "1 Billion and Over",
    "employee_count": "5001 to 10000",
    "linkedin_url": "linkedin.com/company/snowflake-computing"
  }
}

GCS Bucket: gs://autobound-buyer-intent-v1/
Formats: JSONL · Parquet
Refresh: Daily

Full schema docs →Measured coverage →

Applications

What teams build with Buyer Intent Signals

05 documented applications

01
Account Prioritization
Score and rank your total addressable market by intent intensity. Focus your outbound team on the accounts actively researching your category right now, not last quarter’s static list.
02
Buying Group Targeting
With contact-level intent, identify the specific individuals driving vendor evaluation within target accounts. Reach the researchers, not just the org chart.
03
Campaign Timing
Trigger outbound sequences, ad campaigns, and SDR tasks the moment an account enters your intent category. Early-stage signals mean education content; late-stage signals mean direct sales outreach.
04
Competitive Displacement
Monitor when accounts show intent for competitor categories or specific competitor product names. Time your outreach to arrive during the evaluation window.
05
Churn Prevention
Track when your existing customers start researching competitor solutions. Intent spikes for categories that overlap with your product are early churn warnings.

Worked Example

One signal, traced to outcome

One Buyer Intent Signals record followed from detection, through the action a rep took on it, to the measured result. Illustrative composite of observed customer workflows.

01 signal → 02 action → 03 outcome

Signal detected

Snowflake’s composite intent score for ‘Sales Engagement Platforms’ jumps from 42 to 87 over three weeks, with late-stage classification and related topic spikes in ‘Cold Email Software’ and ‘B2B Data Providers.’

Action taken

Your SDR sees the surge, confirms 3 contacts at Acme are individually showing intent, and sends a signal-grounded email to the VP of Sales referencing their evaluation.

Outcome

Meeting booked within 48 hours

The VP confirms they’re actively replacing their current vendor and Autobound was the only outreach that arrived during the evaluation window.

Run this against live data →

FAQ

Frequently Asked Questions

How is buyer intent data collected?

Intent signals are captured from behavioral activity across 500K+ tracked domains, including B2B publisher sites, review platforms, search behavior, and content consumption patterns. Activity is mapped to companies via IP-to-company resolution and to individuals via universal person IDs. All data is collected from consented, privacy-compliant sources.

What’s the difference between company-level and contact-level intent?

Company-level intent tells you that someone at Snowflake is researching sales tools. Contact-level intent tells you that Marcus Johnson, VP of Sales at Snowflake, is personally driving that research. Contact-level enables buying group targeting and personalized outreach to the actual decision-makers.

How many topics can I monitor?

The full topic taxonomy includes 38,000+ topics organized into 26 categories and 364 subcategories. You can also create custom topics for niche categories not covered by the standard taxonomy.

What does the composite score (0-100) represent?

The composite score aggregates multiple behavioral signals: content consumption volume, topic concentration, research velocity, and cross-domain activity into a single intensity measure. Scores above 70 indicate strong active research. The early/late stage label adds buying journey context: early-stage accounts are educating themselves, late-stage accounts are comparing vendors.

How does intent data combine with other Autobound signals?

Intent is one of 35+ signal categories in the Autobound platform. The API returns intent alongside financial signals (funding, SEC filings), workforce signals (hiring, job changes), market signals (tech stack, website changes), and social signals (LinkedIn, Reddit). This multi-signal view is what separates Autobound from intent-only providers.

How It Works

From Raw Data to Your Stack

Autobound ingests from multiple data sources, extracts structured signals with AI, and delivers them however your infrastructure needs.

Autobound Ingests

Raw data from multiple data sources is continuously collected and normalized across millions of sources.

AI Extracts & Scores

ML models extract 38,000+ topics signal subtypes with relevance scoring, confidence levels, and entity resolution.

You Receive

Structured JSONL delivered via your preferred method, updated on a daily cadence.

REST API

Real-time access with subtype filtering

300 req/min

GCS Push

Automated delivery to your bucket

JSONL + Parquet

Enrich API

On-demand LLM-ranked insights

AI relevance scoring

Flat File

Bulk exports for data warehouses

CSV, JSON, Parquet

Ready to License Buyer Intent Signals?

Custom pricing based on signal types, delivery frequency, and volume. Full schema documentation and integration guides included.

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