Introduction

Go-to-market systems run on data but most teams don’t use that data in real time.

Signals flow in from CRMs, product analytics, websites, and external tools, yet they often remain disconnected instead of driving action.

Signals form the base layer of intent-driven GTM. They reveal who’s paying attention, what they’re doing, and when they’re ready to engage.

The goal of this playbook is to show how to capture, classify, and activate those signals so your team can move from raw activity to measurable intent.

We’ll break signal data into three categories: first-party, second-party, and third-party and show how each type supports scoring, automation, and prioritization within a modern GTM system.

This is a practical framework for teams that want to make intent data operational, consistent, and useful across marketing, sales, and revenue operations.

The 5 core uses of intent signals

Intent signals give visibility into activity that indicates interest, curiosity, or readiness to buy.

When used correctly, they make engagement more timely, targeted, and coordinated across your GTM motion.

Here are five practical ways to activate intent signals inside your system:

1. Lead and awareness scoring

Signals help quantify awareness and engagement.

Website visits, email clicks, or in-product activity can be scored to reflect how close a lead or account is to conversion.

When multiple signal types are combined. For example, product usage + content engagement + firmographic fit, the score becomes far more predictive than any single metric alone.

2. Trigger automated outreach

Certain signals are strong enough to justify immediate action.

Examples:

• A demo request
• A spike in product usage
• A new funding round

When these appear, they can automatically start workflows: outbound sequences, nurture emails, or retargeting ads.

Automation ensures engagement happens at the right time, without manual effort.

3. Deliver CRM or slack notifications

Some signals require a human response.

By routing key events into CRM alerts or Slack notifications, sales teams can act in real time when:

• A target account visits pricing or demo pages
• A known contact starts a new role
• A champion reactivates or engages again

This turns signal data into an immediate trigger for action, not just another metric in a dashboard.

4. Monitor customer expansion and retention

Intent signals don’t stop at acquisition, they’re equally valuable post-sale.

Tracking product usage, engagement frequency, and stakeholder changes helps identify both churn risk and upsell opportunities early.

A customer showing declining activity may need re-engagement.A new stakeholder joining the account could signal an expansion opportunity.

5. Build targeted lists for ABM

Signals strengthen targeting.

When combined with fit data, they show which accounts are actively exploring your category or related products.

This allows teams to build high-intent account lists for ads, outbound campaigns, or ABM programs, reducing waste and focusing effort where momentum already exists.

The 3 Signal Categories

Intent data comes from multiple sources.

But to make it actionable, it needs to be organised and grouped by where it originates and how reliable it is.

In a modern go-to-market system, signal data is divided into three core categories:

first-party, second-party, and third-party.

Each category plays a specific role in identifying, validating, and activating buyer intent.

Understanding how they connect and when to use each, is what separates teams that simply collect data from those that turn it into pipeline.

1. First-party signals

First-party signals are the most valuable because they come from your own ecosystem: data you control, verify, and observe directly.

They represent how prospects interact with your brand across owned channels, from your website and emails to your product and CRM.

This layer forms the foundation of your signal architecture.

It captures high-intent behaviour: actions people take inside your environment that show real engagement and curiosity.

2. Second-Party Signals

Second-party signals extend your visibility beyond what you own but within a trusted ecosystem.

They come from partners, platforms, and integrations that share intent data across networks.

This data bridges the gap between your owned signals and the broader market by showing how your prospects behave in related environments.

Think of it as intent data that exists in proximity to your brand: data from partners, peers, or platforms where your ICP already operates.

Second-party data bridges the gap between owned and public intent sources.It often highlights shared customers, mutual relationships, or overlapping audience segments that indicate stronger buying potential.

When integrated with first-party data, it improves scoring accuracy and helps prioritize accounts that are active within your partner network.

3. Third-Party Signals

Third-party signals widen your field of vision.

They represent intent and activity happening outside your direct network or partner ecosystem, usually through external or public data sources.

These signals are essential for discovering net-new accounts, understanding market trends, and identifying buying behaviour early, even before engagement starts.

Building Your Signal Architecture

Collecting intent signals is easy.

Turning those signals into a coordinated, usable system that actually drives pipeline, that’s the hard part.

Most teams stop at collection. Data is flowing in from CRMs, analytics tools, and platforms, but it’s scattered across dashboards that no one checks daily.

The real value of signal data emerges only when it becomes operational: connected, normalised, scored, and routed into actions across the GTM motion.

A strong signal architecture does exactly that.

It acts as the central nervous system of a go-to-market engine: capturing inputs from first-, second-, and third-party sources, processing them into intelligence, and distributing that intelligence to the systems and people who can act on it.

The goal is not to collect more data.

The goal is to make every captured signal have a destination, a purpose, and an owner.

This is the structure that turns intent into action.

1. Ingest signals

The first layer of any architecture is data ingestion, getting all your signals into one place.

Every source, whether it’s CRM, website, product usage, or third-party data, should feed into a single source of truth, typically your CRM, CDP, or warehouse.

Ingesting signals manually is unsustainable.

Instead, use integrations or ETL tools (like Tray.io, Zapier, Hightouch, or Fivetran) to automate data flow between systems.

Each signal should carry a consistent metadata structure that includes:

• Source (where it originated)
• Timestamp (when it occurred)
• Account or contact ID (who it relates to)
• Signal category (first-, second-, or third-party)
• Event type and weight (the nature of the activity)

Standardising this metadata ensures traceability and prevents overlap or double counting when signals merge from multiple tools.

2. Normalize and score

Once signals are flowing in, the next challenge is consistency.

Different tools define engagement differently, one may count a page view, another a form submission, another a click.

Without normalisation, you can’t compare or prioritise meaningfully.

Normalisation creates a shared language for all activity types.

It categorises signals by intensity (low, medium, high) and recency, then applies a weighted scoring model.

For example:

• Visiting a blog post might score +2
• Viewing a pricing page might score +8
• Submitting a demo form might score +15
• Product activity spike might score +20

Each signal adds to an account’s total “intent score,” which can then be segmented into tiers:

• Hot (ready for outreach)
• Warm (active but not sales-ready)
• Cold (early research stage)

Weighted scoring ensures your system stays realistic, recognising that not all activity is equal.

3. Enrich accounts

Signals on their own don’t give full context.

To be actionable, they must be combined with existing data: company information, firmographics, contact details, and past interactions.

Enrichment merges intent signals with your CRM data, building a living profile for every account and contact.

This profile shows both static data (like company size and role) and dynamic data (like engagement and activity trends).

For example:

• A company might be “100–500 employees, Series B, North America” (firmographic data).
• But if it’s also had “8 product logins, 2 pricing page visits, and 1 G2 comparison” this week, that’s live, behavioural intent.

Merged together, the system now knows not just who the company is, but what they’re doing right now.

4. Trigger automations

Once your signals are enriched and scored, they should start driving action automatically.

The purpose of intent data is not to observe activity, it’s to activate workflows when meaningful events occur.

Each automation should follow a clear “if → then” logic tied to signal thresholds.

Examples include:

• CRM automation: Create a new deal when a qualified account crosses a score of 50.
• Slack alerts: Notify the sales channel when a target account views the pricing page twice in 48 hours.
• Nurture flow: Enrol leads who download a playbook into a personalised email sequence.
• Retargeting sync: Add all “warm” leads from the past 14 days into a LinkedIn audience.
• Outbound trigger: Automatically queue an SDR sequence for contacts showing product-level intent.

5. Close the Feedback Loop

The most advanced signal systems are self-correcting.

Once signals start driving action, results should feed back into the model.

If a particular signal pattern consistently leads to booked meetings or closed deals, its weight should increase.

If a signal frequently triggers workflows that go nowhere, it should be deprioritised or removed.

This is how the system improves itself over time.

The feedback loop also helps GTM teams align around reality, not opinion.

Instead of debating which signals matter, the data shows it, empirically, through outcomes.

The cycle becomes:

1. Capture
2. Score
3. Act
4. Measure
5. Adjust

Each turn of that loop sharpens the model and makes the flywheel spin faster.

Conclusion

Signal data is no longer optional, it’s the foundation of a modern go-to-market system.

When captured, classified, and activated inside one architecture, it stops being scattered information and becomes real-time GTM intelligence.

Signals align every motion:

• Marketing knows which accounts are showing interest
• Sales knows when to engage
• RevOps knows what’s actually driving pipeline

It replaces guesswork with visibility and visibility with predictability.

Teams stop reacting to activity and start anticipating it.

For companies building signal-driven GTM systems, Workflows.io helps design, connect, and automate the entire stack, from signal capture to scoring, routing, and activation across CRM, Slack, and outbound.

If the goal is to turn intent data into a connected GTM engine Workflows.io builds the system end-to-end.