Recap
- Intent data is any signal that a person or company is moving toward a buying decision. It is a proxy for timing, not proof.
- First-party intent comes from your own properties and is precise. Third-party intent is inferred by a vendor and is broad but fuzzy.
- Public signals (hiring, funding, tech-stack changes, reviews, filings) are the most actionable because they tie to a specific event you can write to.
- The real skill is turning a signal into a trigger, then filtering out the noise so you do not pitch on a false positive.
Intent data is any observable signal that a person or company is getting closer to buying something. A visit to your pricing page, a job posting for a role that uses the tool you replace, a Series A that just landed, a one-star review of your competitor. None of these prove a purchase. Each one tells you the odds and the timing just shifted in your favor. Good outbound is mostly the discipline of acting on the right signals at the right moment.
What is intent data, exactly?
Intent data is a signal that stands in for a buying decision you cannot see directly. You will never watch a prospect decide. You can watch them hire for a problem, raise money, swap a vendor, or read your docs at 11pm. Each of those correlates with a need surfacing. The data is a bet on timing. It is never a confession.
A signal does not say email this person now. It says the chance this account has a live need just went up. What you do with that probability is where the work lives.
What is the difference between first-party and third-party intent?
First-party intent is anything you observe on your own properties. Someone replies to a cold email, books a demo, or hits your pricing page three times in a day. You know the identity, the action, and the timestamp. It is the highest quality intent you will ever get, and most teams underuse it because it is not packaged as a product.
Third-party intent is purchased from a vendor that watches activity across a large network of sites and content, then infers which companies are researching a topic. It is wider in reach and almost always coarser. It usually resolves to the company, not the person, and it is often modeled rather than observed. Useful as a first filter, weak as a final answer.
| Dimension | First-party intent | Third-party intent |
|---|---|---|
| Source | Your site, email, product | A vendor network across the web |
| Resolution | The person | Usually the company |
| Precision | High, observed directly | Lower, often modeled |
| Freshness | Real time | Daily or weekly batches |
| Best use | Trigger a send now | Prioritize which accounts to look at |
What public signals actually predict a need?
Public signals are the most actionable kind of intent because each one ties to a specific event you can name in the first line of an email. They are observable, they are timestamped, and you do not need a vendor login to start using most of them. The trick is knowing which event maps to which need.
- Hiring. A job post is a public statement of a problem someone got budget to solve. A listing that names a tool, a workflow, or a missing function tells you what they are about to buy.
- Funding. A round creates budget and pressure to grow fast. The weeks after an announcement are when new tools get evaluated and bought.
- Tech-stack changes. A new tag on a website, a CNAME pointing at a new platform, a swapped vendor. These mark a team in motion, often shopping for the next piece.
- Reviews and complaints. A public one-star review of a competitor is a prospect telling you exactly what they hate about the thing they already pay for.
- Filings and registry data. New entity registrations, permits, SEC filings, grant awards. Slower signals, but unambiguous, and great for verticals the consumer-internet signals never touch.
How do you turn a signal into an outreach trigger?
A trigger is a signal plus a rule that says when this happens, do this. The signal alone is inert. You make it useful by deciding the threshold, the message, and the window. Without those three, you just have a feed nobody acts on, which is the fate of most intent purchases.
Start by writing the message to the signal. If the trigger is a funding round, the email should sound like it was sent because of the funding round, not like a template that happened to land that day. The signal is your permission to be specific. Waste it and you are back to spray and pray with extra steps.
Then set a freshness window. A hiring post is strongest in the first week and stale by the fourth. Most buying signals decay in days. If your pipeline only refreshes monthly, you are arriving after the door closed. Real-time beats comprehensive almost every time here.
Where does intent data go wrong?
Intent data fails in two predictable ways: noise and false positives. Noise is volume without meaning, a thousand accounts that tripped some keyword threshold with no real need behind it. A false positive is a signal that looked like intent but was not, the analyst doing research with no budget, the competitor scoping your pricing, the bot inflating a page view.
Third-party data is especially prone to this because it is modeled and anonymized. A surge in topic activity at a company might be one curious intern. I have real questions about how some intent vendors source and resolve their data, so treat any single third-party signal as a hint, never a fact.
The fix is requiring corroboration, not a better vendor. Make two or three signals line up before you act: funding plus a relevant hire, a tech change plus a matching role, a pricing-page visit plus a known fit on company shape. One signal is a guess. Three that agree is a reason to write. Tools that watch these signals continuously and only fire when several agree, which is the approach we take at LaunchSurface, turn a noisy feed into a short list worth acting on.
Apple Mail Privacy Protection is a useful reminder of how fragile a single signal is. It pre-loads images, which inflates open rates and makes opens close to worthless as intent on their own. Any signal that can be faked, batched, or triggered by a machine needs a second source before you trust it. Intent data is an edge when you respect its limits and a liability when you pretend it is certainty.