TL;DR
- Intent data is useful for identifying and prioritizing accounts showing interest in topics you care about. LinkedIn engagement signals add person-level context by showing who engaged, what they engaged with and when.
- Intent data can help shape account priorities. Engagement signals can help determine where there's a timely reason to engage with a specific person.
- When both appear at the same account, the combination gives your team stronger context than either signal alone.
Intent data and engagement signals answer different questions. Picture a Monday intent report flagging a 600-person travel software company (fictional) for a surge on contract management. The report says something may be happening at the account. It doesn't say who. A LinkedIn engagement signal from the same company names a legal operations director who reacted to a post about contract approval bottlenecks. Neither replaces the other. Together, the team has an account worth prioritizing and a person worth a closer look.
Intent data and engagement signals, defined
Intent data is behavioral data that suggests a company is researching a topic, product or problem.
Third-party intent data is research activity that a provider tracks on websites you don't own, matches to companies and scores by topic. A topic surge is a rise in an account's research on a topic above that account's own recent baseline.
First-party intent data is activity on your own website, emails and product.
A LinkedIn engagement signal is a public reaction or comment on a specific LinkedIn post by a named person who fits your ideal customer profile (ICP). Where there's no ambiguity, this article calls it a signal.
Intent surges, engagement signals, job changes and funding news are all examples of signals: observable activity or events that can provide useful context about a person or account.
Intent data vs. engagement signals: side by side
The right-hand column describes a SignalRaven signal. Other engagement-signal products include different things.
| Typical third-party intent data | SignalRaven engagement signal | |
|---|---|---|
| What it tells you | An account is showing increased interest in a topic | A named person engaged with a specific LinkedIn conversation and qualified against your ICP |
| Unit | An account (a company domain) and a topic | A named person and the post they engaged with |
| Source | Reading activity on B2B publisher and research sites, matched to companies by the provider | Public reactions and comments on monitored LinkedIn posts from company pages, people, keyword searches, company mentions and conferences |
| Freshness | A topic score measured against the account's own baseline and refreshed on a schedule (often weekly) | Event-based: each qualified engagement becomes its own signal with the post link and the time SignalRaven captured it |
| Identity | Account name; some providers add role or persona detail | Name, title, company and LinkedIn URL |
| Context | Topics and an intent score | The post and a summary, comment text when there's one, five ICP dimension scores, urgency from 1 to 10, why it matters, previous signals from the person or company, and talking points at urgency 5 or higher |
| Blind spot | Often doesn't identify the individual behind the research | Doesn't capture relevant people who never publicly engage with monitored LinkedIn content |
What SignalRaven adds to an engagement signal
A reaction or comment on its own is an observation. SignalRaven turns it into a signal in stages: it identifies the person and researches their company, evaluates ICP fit across five dimensions, sets aside staff of the companies you track and people at competing vendors, scores urgency with the person's and company's recent signal history, and attaches the context a human needs to decide what to do. In SignalRaven data, about 3% of people who engage with a monitored post become a qualified signal, roughly 1 in 30. The 14-stage qualification process walks through each step.
It helps to think in three levels:
| Level | What it says |
|---|---|
| Account intent | Something may be happening at this company |
| Person signal | This person did something relevant |
| Signal intelligence | Here's who they are, whether they fit your ICP, what they engaged with, what else has happened at the account and how much attention the signal deserves |
Intent data works at the first level. A raw reaction is the second. SignalRaven's job is the third.
In SignalRaven data, about 85% of qualified signals begin with a reaction rather than a comment. That makes the original post especially important: when the person hasn't written anything, the conversation they engaged with provides the context.
Fictional example: one account, two kinds of evidence
- Intent data alone: the team knows the travel software company is showing increased interest in contract management but still needs to identify the relevant people and the angle.
- Engagement signal: a legal operations director at the same company reacted to a post about contract-approval bottlenecks (urgency 7). Now the team has a person, a role and a specific topic to research before deciding whether and how to engage.
How to use intent data and SignalRaven together
Signal in a flagged account. A delivered signal carries the company's website and LinkedIn URL when known, so you can match it against your intent-flagged accounts in the CRM. Treat the overlap as additional context. Review the person's ICP fit, urgency and engagement before deciding where it belongs in the team's priorities.
Flagged account, no signal yet. Run Account Intelligence on the company's LinkedIn URL (trial, Pro and Scale plans). It lists up to 15 stakeholders at Director level and above by default and shows each person's signals from the last 90 days. Use it to identify relevant stakeholders and review any recent SignalRaven activity from people at the account. Then decide what to do.
Signal outside the flagged list. A SignalRaven signal doesn't become irrelevant because the account isn't surging in an intent platform. The two systems observe different behaviors. Evaluate the signal on its own ICP fit, urgency and context.
Accounts heating up. Multiple signals from the same person or company can show a pattern that a single engagement can't. SignalRaven incorporates recent signal history into urgency on every plan and groups activity by company in the Companies view, so you can see when engagement is accumulating across an account. Pro and Scale add the signal stack view on each signal.
Where each signal is most useful
Intent data is particularly useful for account prioritization. It can help teams identify accounts showing increased research activity, tier territories and focus account-based programs.
Engagement signals are particularly useful when person-level context matters. An account-level intent signal may still leave the team to determine which person to engage. A person-level signal narrows that decision because it identifies the individual and the LinkedIn conversation they engaged with, and SignalRaven's qualification tells you whether that person fits before anyone spends time on them.
Neither captures everything. Intent data may not identify the individual behind the research. Engagement signals won't identify relevant people who never publicly engage with monitored LinkedIn content.
FAQ
What's an engagement signal?
A LinkedIn engagement signal is a public reaction or comment on a specific LinkedIn post by a named person who fits your ideal customer profile. SignalRaven finds them on posts from company pages, people, keyword searches, posts that mention a company and conferences. Each one arrives with the post, the person's name and title, their company and an urgency score from 1 to 10.
What's the difference between intent data and engagement signals?
Third-party intent data scores accounts on the topics people at those companies research across websites you don't own. An engagement signal names one person who reacted to or commented on a specific LinkedIn post and fits your ideal customer profile. Intent data helps shape account priorities. Engagement signals add person-level context and a timely reason to look closer.
Is intent data account-level or person-level?
Third-party intent data is mostly account-level. It's resolved to a company domain and scored by topic. Some providers add role, seniority or persona detail, so check what a given product delivers. Engagement signals are person-level by design because each one comes from a named person's public activity on a post.
What's the difference between first-party and third-party intent data?
First-party intent data is activity on properties you control: your website, your emails and your product. Third-party intent data is research activity on websites you don't own. A provider resolves it to companies and scores it by topic. First-party data shows interest in your company, while third-party data shows interest in a topic on sites outside your control.
How often is third-party intent data updated?
It depends on the provider. Many refresh topic scores weekly, so ask for the refresh cadence when you evaluate one. An engagement signal is event-based: each qualified engagement on a monitored post becomes its own signal with a link to the post and the time SignalRaven captured it.
What's a topic surge in intent data?
A topic surge is a rise in an account's research on a topic above that account's own recent baseline. Because each account is measured against its own history, a surge shows a change in that account's behavior. Each provider sets its own measurement windows and thresholds.
Is LinkedIn engagement a type of intent data?
It can be treated as a form of behavioral signal, but it's different from what the B2B market typically calls third-party intent data. Third-party intent usually aggregates research activity and resolves it to an account. SignalRaven engagement signals come from public LinkedIn activity tied to a named person and a specific post.
Do engagement signals replace intent data?
No. They do different jobs. Intent data can help identify accounts showing increased interest in relevant topics. Engagement signals provide person-level context around observable LinkedIn activity. When both point to the same account, you have two different forms of evidence to work with.
Does an engagement signal include the person's email address?
A SignalRaven signal doesn't include an email address or phone number. It identifies the person by name, title, company and LinkedIn URL, and carries the company's website and LinkedIn URL when known. If your sequence needs an email, add it through your own enrichment step before the message goes out.
Sources
- SignalRaven data, aggregated across customer workspaces
- SignalRaven product page: urgency and the signal stack