Guide
What Is Signal Data? The New Standard Replacing Intent
Signal data is discrete, verifiable business events that reveal buying windows. Not inferred from browsing patterns. Not aggregated into opaque topic scores. Specific events, with timestamps, sources, and context. 700+ signal types from 35+ sources, one API.
Signal Data, Defined
Signal data is a category of b2b data composed of discrete, observable, verifiable business events that indicate a company is entering a buying window. Each signal has three properties: a timestamp (when it happened), a source (where it was observed), and business context (why it matters to your pipeline).
A signal is not a probabilistic guess. It is a fact. FlowAI raised $45M Series B led by Sequoia. TechStart hired a new VP of Sales from Salesforce. Acme Corp posted 8 SDR roles in the past 14 days. LegacyCorp is posting 8 AWS architect roles. These are observable events, sourced from primary data, with clear commercial implications.
The distinction matters because revenue teams have spent the past decade acting on inferred data. Topic scores. Aggregated browsing patterns. "Surge" alerts based on IP-matched content consumption across publisher co-ops. These mechanisms worked when cookie tracking was reliable, when IP-to-company matching was accurate, and when fewer teams were competing for the same data. None of those conditions hold anymore.
Signal data operates differently. Instead of asking "who might be interested based on their browsing?", it asks "what just happened at this company that creates a buying window?" The answer is specific, verifiable, and immediately actionable. A seller can reference the signal directly in outreach. A scoring model can weight it precisely. An AI agent can trigger a workflow based on it. No black box. No interpretation required.
The Autobound signal catalog currently tracks 700+ signal types across 35+ sources, organized into 6 categories. Each signal type is sourced from primary data (SEC filings, job boards, LinkedIn, patent offices, government databases, Glassdoor, Reddit, Product Hunt, conference schedules, and more), normalized into a consistent schema, and delivered via API or flat file.
Signals vs. Intent vs. Firmographic Data
Three fundamentally different data types. Revenue teams often conflate them, but the distinctions determine what you can do with the data and how quickly you can act on it.
| Dimension | Firmographic | Intent | Signal |
|---|---|---|---|
| Definition | Static company attributes (revenue, headcount, industry, location) | Inferred interest based on content consumption patterns | Discrete, verifiable business events with timestamps and source attribution |
| Freshness | Quarterly or annual updates | 7-30 day aggregation windows | Real-time to daily (event-driven) |
| Personalization Value | Low (generic segmentation only) | Medium (topic-level targeting) | High (specific event context enables hyper-relevant messaging) |
| Actionability | Low (build lists, no timing) | Medium (know they're researching, not why) | High (know exactly what happened, when, and why it matters) |
| Accuracy | High (but quickly outdated) | Variable (IP matching, cookie deprecation) | High (verifiable from primary sources) |
| Attribution Level | Company-level only | Mostly company-level (some contact) | Both company and contact-level |
| Cost Efficiency | $0.01-0.05 per record | $0.15-0.50 per signal (annual contracts) | $0.004-0.0095 per credit (pay-as-you-go, credits never expire) |
Firmographic data tells you who a company is. Intent data tells you they might be interested. Signal data tells you what just happened and why it creates an opening. These are not interchangeable, and they serve different functions in a revenue stack.
The practical impact: a seller using firmographic data alone sends the same message to every mid-market SaaS company in North America. A seller using intent data knows the company is "researching CRM solutions" but can't say why or reference anything specific. A seller using signal data writes: "Saw you just hired Sarah Chen as CRO from Salesforce. When we've seen that pattern before, the new leader typically evaluates outbound tools within 60 days. Worth a conversation?"
That last message converts at 3-5x the rate of the first two. The signal gives you specificity, timing, and relevance. The other two give you a starting point at best.
The 6 Signal Categories
Autobound organizes 700+ signal types into 6 categories. Each category captures a different dimension of business change. Together, they form a complete view of buying readiness.
Hiring & Growth
12 signal typesHiring patterns reveal budget allocation, strategic priorities, and growth trajectory faster than any earnings call. A company posting 8 SDR roles in 14 days is building an outbound engine. A company hiring 4 data engineers is investing in infrastructure. These are not ambiguous.
SDR/BDR Team Expansion
"Acme Corp posted 8 SDR roles in the past 14 days"
They're scaling pipeline generation and likely evaluating outbound tools.
Engineering Hiring Surge
"DataFlow posted 15 engineering roles this month, up 300%"
Product investment is accelerating. Budget exists. Decisions are being made.
VP of Sales Hire
"TechStart hired a new VP of Sales from Salesforce"
New leaders bring new vendors. The first 90 days are the buying window.
Hiring Velocity Change
"MetaTech hiring velocity increased 85% month-over-month"
Rapid acceleration signals funding deployment or new strategic initiative.
Financial & Funding
10 signal typesMoney changes everything. A Series B announcement means budget just materialized. An earnings beat means confidence to invest. A government contract award means procurement is active. Financial signals are the strongest predictor of near-term purchasing behavior.
Series A/B/C Funding
"FlowAI raised $45M Series B led by Sequoia"
Post-funding companies spend 3-5x more on tools in the following 6 months.
Government Contract Award
"DefenseTech won $25M DoD contract"
Contract awards unlock earmarked budget that must be deployed.
M&A Activity (Acquirer)
"EnterpriseCo acquired DataSync for $150M"
Acquirers consolidate tech stacks. New evaluation cycles begin immediately.
Revenue Milestone
"CloudMetrics announced crossing $50M ARR"
Growth milestones correlate with infrastructure upgrades and team expansion.
Technology & Product
10 signal typesTechnology adoption and migration signals reveal active evaluation cycles. A company migrating from HubSpot to Salesforce is making purchasing decisions right now. A company posting 8 AWS architect roles is mid-cloud-migration. These aren't inferences. They're observable facts.
CRM Implementation or Switch
"RapidGrow migrating from HubSpot to Salesforce"
CRM migrations trigger cascading tool purchases across the entire stack.
Cloud Migration Initiative
"LegacyCorp posting 8 AWS architect roles"
Cloud migration budgets are large and adjacent tool purchases follow.
Product Launch
"AutomateIt launched AI workflow builder on Product Hunt"
New products require new distribution channels, tools, and infrastructure.
Tech Stack Downgrade
"ScaleDown replaced Salesforce with Pipedrive"
Cost restructuring creates openings for value-positioned alternatives.
Leadership & People
9 signal typesPeople changes drive vendor changes. A new CRO brings their preferred stack. A departing VP leaves behind a vacuum that a competitor can fill. A champion changing jobs opens a door at their new company. Leadership signals are deeply personal and deeply actionable.
New CRO Appointment
"TechCorp appointed Sarah Chen as CRO"
CROs evaluate and replace tools in their first 90 days at a rate 4x higher than baseline.
Contact Job Change
"Your champion at Acme just joined BigCorp as VP Sales"
Champions who change jobs bring their vendors. Warm intro at a new account.
VP Marketing Hire
"GrowthEngine hired VP Marketing from Drift"
Marketing leaders bring playbooks, and the tools that support those playbooks.
Glassdoor Sentiment Shift
"Rating dropped from 4.2 to 3.1 in 3 months"
Declining sentiment signals internal dysfunction, leadership change, or restructuring.
Intent & Engagement
9 signal typesIntent signals capture active research behavior. Pricing page visits, G2 activity, competitor research, content downloads. These are closest to traditional intent data, but delivered as discrete, timestamped events rather than aggregated topic scores.
Pricing Page Visits
"3 contacts from Acme visited your pricing page this week"
Pricing page visits indicate evaluation-stage activity with near-term purchase intent.
G2 Review Activity
"BigCorp left a negative review of your competitor on G2"
Negative competitor reviews signal active dissatisfaction and evaluation.
Competitor Research Activity
"FinCorp downloaded 4 competitor comparison guides in 7 days"
Multi-vendor research indicates an open RFP or evaluation cycle.
Social Engagement Spike
"CTO of TargetCo liked 5 posts about data orchestration this week"
Topic-concentrated engagement reveals what decision-makers are thinking about.
Company & Market
10 signal typesMarket-level events reshape competitive landscapes overnight. A competitor acquisition creates urgency. A regulatory change forces compliance spending. A layoff announcement signals restructuring that opens budget elsewhere. These macro signals contextualize every other signal in your pipeline.
Competitive Displacement
"TargetCo's primary vendor acquired by their competitor"
Vendor acquisitions by competitors create immediate switching urgency.
New Market Expansion
"EuroTech announced US market entry with New York office"
Market entry requires new infrastructure, tools, and local vendor relationships.
Layoff Announcement
"MegaCorp announced 15% workforce reduction"
Layoffs signal cost-optimization. Budget-friendly alternatives gain appeal.
Patent Filing Activity
"InnovateCo filed 12 patents in AI orchestration this quarter"
Patent activity reveals R&D direction before product announcements.
Explore the full signal catalog → 700+ types across 35+ sources.
Browse Signal TypesThe Compound Signal Effect
One signal is a data point. Three signals from different categories at the same company tell a story. This is the compound signal effect, and it is what intent data structurally cannot replicate.
Consider this scenario: CloudBase raised a $45M Series B (Financial & Funding) → hired a new VP of Sales from Salesforce (Leadership & People) → posted 8 SDR roles in the past 14 days (Hiring & Growth). Each signal alone has value. Together, they paint a specific picture: this company is building an outbound engine, has budget to invest in tools, and a new leader who will make vendor decisions within 90 days.
An intent score would tell you "CloudBase is researching sales engagement tools." The compound signal tells you exactly why they're researching, who will make the decision, and that they have $45M in fresh capital to spend. The seller who has compound signal context writes a fundamentally different (and more effective) message than the seller who only knows a topic score.
The Autobound Signal API returns all active signals for a given company in a single response, making compound signal detection trivial. When you query a company, you see every signal across all 6 categories, timestamped and sourced, ready to be scored, prioritized, or referenced in outreach.
This is also what enables signal-based selling workflows. Reps don't just act on one trigger. They identify accounts where multiple signals converge, creating high-confidence buying windows. Our data shows accounts with 3+ signals from different categories convert to meetings at 3-5x the rate of single-signal accounts.
Example: Compound Signal Story → CloudBase
Financial & Funding
Raised $45M Series B led by Sequoia (12 days ago)
Leadership & People
Hired VP of Sales from Salesforce (8 days ago)
Hiring & Growth
Posted 8 SDR roles in the past 14 days
Technology & Product
Migrating from HubSpot to Salesforce (3 days ago)
→ Compound confidence: Very High. Building outbound team, has budget, new decision-maker, evaluating sales tools now.
How Teams Use Signal Data
Signal data powers workflows across sales, marketing, RevOps, and platform teams. The common thread: replacing probabilistic targeting with event-driven precision.
Sales Teams
Signal-based prioritization + personalization
Reps open their day to a ranked list of accounts exhibiting buying signals. Instead of working alphabetically through a static list, they call the company that just raised a Series B, hired a new CRO, and posted 6 SDR roles. The first email references the exact signal.
3-5x higher meeting conversion vs. baseline outbound (measured across 100,000+ signaling events).
AI SDR Platforms
Automated signal-to-send workflows
AI SDRs ingest signals via API, generate hyper-personalized sequences triggered by specific events. When a target account hires a VP of Sales from a competitor, the AI SDR sends a contextual message within hours, not days.
Platforms like TechTarget saved $400k building IntentMail on the Autobound API instead of in-house.
Data & Enrichment Platforms
Signal enrichment layer via API or flat file
Platforms integrate signal data to enrich their existing records with real-time event context. Instead of showing a static company profile, they show a living timeline of business events that inform outreach timing.
OEM partners layer 700+ signal types into their products without building signal infrastructure.
Marketing (ABM)
Signal-triggered campaign enrollment
Marketing teams build audiences based on compound signals. Companies showing 3+ signals across different categories get enrolled in high-touch ABM campaigns. Budget is allocated dynamically based on signal density.
Ad spend concentrates on accounts with verified buying windows, not probabilistic intent scores.
RevOps & Lead Scoring
Signal-weighted scoring models
RevOps teams weight signals by category and recency in their lead scoring models. A funding signal from this week scores higher than a job posting from 30 days ago. Compound signals from multiple categories multiply the score.
Scoring models built on verified events outperform those built on inferred intent by 2-4x in pipeline prediction accuracy.
How to Get Signal Data
Three paths exist. They differ dramatically in cost, reliability, and time-to-value.
Path 1: Build it yourself
Scrape LinkedIn for job changes. Monitor SEC filings for funding events. Track job boards for hiring velocity. Parse Glassdoor for sentiment shifts. Build NLP pipelines to extract meaning from earnings calls.
Realistic cost: 2-4 engineers full-time, $500K-$1M/year, 6+ months to reach coverage parity with a commercial provider. Ongoing maintenance is brutal. Sources change schemas, rate-limit, or shut off access. Most teams who try this path abandon it within 12 months.
Path 2: Buy from multiple point solutions
G2 for review signals. Crunchbase for funding. LinkedIn Sales Navigator for people changes. Bombora for intent. BuiltWith for technographics. Each vendor covers one category, charges separately, delivers data in a different schema, and refreshes on a different cadence.
Realistic cost: $150K-$500K/year across 4-6 vendors. No normalization. No compound signal detection. Integration burden falls on your RevOps team. And you still have gaps.
Path 3: Unified Signal API
One API, 700+ signal types, 35+ sources, normalized schema, real-time to daily delivery. Pay per credit ($0.004-$0.0095 per credit depending on plan). Credits never expire. Zero-result queries are free. Start with 1,000 free credits, no credit card.
The Autobound Signal API provides REST endpoints for search (find companies/contacts matching signal criteria), enrich (get all signals for a given company), and buyer intent (contacts actively researching your category). An MCP server is also available for AI agent integration. For a hands-on code tutorial, see the company enrichment API guide.
Example: Fetch signals for a company
curl -X GET "https://api.autobound.ai/v1/signals/company?domain=cloudbase.io" \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json"
Response
{
"company": "CloudBase",
"domain": "cloudbase.io",
"signals": [
{
"category": "Financial & Funding",
"type": "Series B Funding",
"summary": "CloudBase raised $45M Series B led by Sequoia",
"timestamp": "2026-06-03T14:22:00Z",
"source": "SEC Form D Filing",
"confidence": 0.98
},
{
"category": "Leadership & People",
"type": "VP of Sales Hire",
"summary": "CloudBase hired Marcus Rivera as VP of Sales (prev. Salesforce)",
"timestamp": "2026-06-07T09:15:00Z",
"source": "LinkedIn",
"confidence": 0.95
},
{
"category": "Hiring & Growth",
"type": "SDR/BDR Team Expansion",
"summary": "CloudBase posted 8 SDR roles in the past 14 days",
"timestamp": "2026-06-10T11:30:00Z",
"source": "Job Boards (aggregated)",
"confidence": 0.97
},
{
"category": "Technology & Product",
"type": "CRM Implementation or Switch",
"summary": "CloudBase migrating from HubSpot to Salesforce",
"timestamp": "2026-06-09T16:45:00Z",
"source": "Job Postings + LinkedIn",
"confidence": 0.88
}
],
"compound_score": 0.94,
"buying_window": "high",
"credits_consumed": 2
}Each signal includes category, type, a human-readable summary, timestamp, source, and confidence score. The compound_score field represents the aggregate buying window probability based on all active signals. 2 credits consumed per company enriched. Zero-result queries are free.
For enterprise teams needing flat file delivery (50M+ companies, weekly refresh, GCS/S3 delivery, custom schema), contact our data team directly. We match your schema and seed monitored audiences with your target account lists.
Signal API Pricing
Credit-based. Pay once. Credits never expire. Start free.
Starter
$19
2,000 credits
$0.0095/credit
Growth
$49
5,444 credits
$0.009/credit
Scale
$149
19,867 credits
$0.0075/credit
Pro
$499
83,167 credits
$0.006/credit
Business
$1,299
288,667 credits
$0.0045/credit
Enterprise
$4,999
1,249,750 credits
$0.004/credit
Every plan includes: all 35+ signal sources, REST API + MCP server, buyer intent data, zero-result queries free.
View full pricing details →Getting Started with Signal Data
Every new account receives 1,000 free credits on signup. No credit card required. That's enough to enrich 500 companies with full signal data, or search for companies matching specific signal criteria across your target market.
The fastest path from zero to value:
- Create a free account → receive API key immediately
- Enrich your top 50 target accounts → see which are exhibiting buying signals right now
- Identify compound signals (3+ signals across different categories) → these are your highest-priority accounts
- Reference specific signals in outreach → measure reply rate lift vs. generic messaging
For developer integration, full documentation is available at autobound-api.readme.io. The API returns structured JSON, supports batch operations, and integrates natively with Salesforce, HubSpot, Outreach, Salesloft, Instantly, and Clay. The MCP server enables AI agents to query signal data conversationally. Teams building data enrichment pipelines typically start with the REST API and add MCP for agent workflows.
For platform teams looking to embed signal data into your own product, OEM licensing is available with flat file delivery, custom schema matching, and dedicated audience seeding.
Frequently Asked Questions
Signal data refers to discrete, verifiable business events that indicate buying windows or strategic changes at a company. Unlike intent data (which infers interest from content consumption patterns) or firmographic data (which describes static company attributes), signal data captures specific events like funding rounds, executive hires, technology migrations, and job postings. Each signal has a timestamp, a verifiable source, and business context that explains why it matters for sales and marketing teams.
Intent data infers interest from aggregated content consumption patterns (e.g., 'Company X is researching CRM solutions based on 14 content downloads across publisher sites'). Signal data captures discrete, observable events (e.g., 'Company X posted 8 SDR roles, hired a VP Sales from Salesforce, and raised a $45M Series B in the past 30 days'). Intent tells you someone might be interested. Signals tell you exactly what happened, when, and why it creates a buying window. Signals are also verifiable, meaning you can trace each one to a primary source.
Autobound's Signal API uses a credit-based pricing model. Credits start at $0.0095 each on the Starter plan ($19 for 2,000 credits) and drop to $0.004 each on the Enterprise plan ($4,999 for 1,249,750 credits). Credits never expire. Every new account receives 1,000 free credits with no credit card required. For enterprise flat file licensing (50M+ companies, weekly refresh, GCS delivery), custom pricing is available. There are no annual contracts required for API access.
The Autobound Signal API aggregates signals from 35+ primary sources including LinkedIn (posts, comments, job changes), SEC filings (S-1, 10-K, Form D), job boards (hiring velocity, role-specific tracking), Glassdoor (sentiment analysis), Reddit (pain points, competitor mentions), Product Hunt (launches), government databases (contract awards, RFPs), patent offices (filing activity), podcast directories, Hacker News, conference schedules, and more. All signals are normalized into a consistent schema regardless of source.
Yes. The Autobound Signal API provides REST endpoints and an MCP server for AI agent integration. You can search for companies or contacts matching specific signal criteria, enrich existing records with real-time signals, and receive structured JSON responses with signal type, timestamp, source, and business context. The API supports both real-time lookups and batch processing. Documentation is available at autobound-api.readme.io.
Teams use signal data across the revenue cycle. Sales reps use signals to prioritize outreach (calling companies exhibiting buying signals first) and personalize messaging (referencing the specific event in their email). AI SDR platforms use the API to trigger automated sequences when accounts match signal criteria. Marketing teams use compound signals to build high-intent ABM audiences. RevOps teams weight signals in lead scoring models. Data platforms embed signal data via OEM licensing to enrich their own products.
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1,000 free credits. 700+ signal types. 35+ sources. One API. No credit card required.