Two concepts, two logics
Intent data: detecting online interest
Intent data measures the online behavior of prospects: web pages visited, keywords searched, content downloaded, comparisons consulted. The logic is simple enough. If a decision-maker spends time researching cybersecurity solutions, they probably have a cybersecurity need.
Intent data providers aggregate these behavioral signals across networks of partner sites and third-party cookies to identify companies that are “in market” for a given topic.
Intent signals: detecting the real event
Intent signals detect concrete, verifiable events in a company’s lifecycle: funding rounds, hiring, relocations, executive appointments, mergers. These events are factual. They happened, they’re dated, they’re public.
Rodz detects 108 signal types via 350+ scrapers querying 250+ public data sources.
The fundamental difference
| Dimension | Intent data | Intent signals |
|---|---|---|
| What is detected | Online behavior | A real-world event |
| Source | Cookies, site networks | Registries, press releases, jobs |
| Verifiability | Difficult (anonymized) | Easy (public and dated) |
| Granularity | Company level (rarely individual) | Company + individual level |
| Contextualization | ”Interested in X" | "Raised €Y on Z date” |
| Cookie dependency | Yes (threatened by third-party cookie end) | No |
| GDPR compliance | Complex (behavioral data) | Simple (public data) |
| Freshness | Weekly aggregation | Real time (48h) |
The limitations of intent data
The third-party cookie problem
Intent data relies heavily on third-party cookies to track prospects’ online behavior. Safari and Firefox already block them; Chrome is tightening restrictions. That progressive disappearance reduces both the coverage and the reliability of the underlying data.
The attribution problem
Intent data tells you a “company” is interested in a topic, but rarely which individual. The office IP address identifies the company, not the department or the person. The sales rep gets something like “Company X is researching cybersecurity” with no indication of whether it’s the CISO, an intern, or a contractor doing the browsing.
The noise problem
Searching for a topic online doesn’t mean having a buying need. A journalist writing about cybersecurity generates the same behavioral signal as a CISO evaluating vendors. Intent data algorithms try to filter this out, but the false-positive rate stays high.
The timing problem
Intent data is typically aggregated weekly or biweekly. By the time the signal reaches you, the prospect may have already finished their research and picked a vendor. The window closes before you knew it was open.
The strengths of intent signals
Verifiability
An intent signal is a public, verifiable fact. “Company X raised €5 million on March 15” is a dated, sourceable, checkable event. A sales rep can mention it in an email without worrying about being wrong.
Contextualization
The signal gives a precise approach context. Instead of “this company is interested in HR topics,” you know “this company hired 12 people in three weeks and just appointed a new HR Director.” The message writes itself from there. A useful way to think about it: the goal is to contact a company when something like this happens, not before and not a month after.
Technology independence
Intent signals don’t depend on third-party cookies, tracking pixels, or ad networks. They come from public sources such as registries, press releases, and job postings. None of that is threatened by changing privacy standards online.
GDPR compliance
Intent signals come from public professional data. Their use for B2B prospecting sits under the legitimate interest basis in GDPR, without the gray areas that behavioral tracking tends to produce. Publishing a job offer or announcing a funding round is itself the legitimate interest, by construction.
How to combine them
Intent data + intent signals = predictive prospecting
The two approaches do complement each other:
- Intent signals identify the right moment. A funding round, mass hiring, or executive appointment reveals an objective need.
- Intent data can confirm the interest. If the same company is actively researching solutions in your space, the signal is doubly validated.
That combination cuts false positives and pushes conversion rates up.
In practice with Rodz
Rodz uses intent signals as the foundation: verifiable events, produced in real time. Intent data can supplement the Balance scoring model as an additional parameter:
- Intent signal alone: standard Balance score, Tier 2 or 3 treatment
- Intent signal + intent data: boosted Balance score, promotion to Tier 1
For companies that don’t have access to intent data, intent signals alone are enough to multiply meeting rates by 4. The 48-hour window is what makes that possible. A signal older than 48 hours decays back toward cold-list performance, so timeliness isn’t optional.
Frequently asked questions
Should I choose between intent data and intent signals?
Not necessarily, but if you have to start with one, start with intent signals. They’re more reliable, more contextual, less expensive, and they don’t depend on cookies. Intent data is an accelerator for teams that have already built their process around signals.
Will intent data disappear with the end of cookies?
Third-party-cookie-based intent data will shrink, yes. Other forms such as first-party and contextual intent data will fill part of that gap. Intent signals aren’t affected by any of this.
What’s the comparative cost?
Intent data means a subscription to a specialized provider that can run several thousand euros per month. Rodz intent signals include detection, enrichment, and scoring in one offering, which tends to be more accessible for SMBs and mid-market companies.
If you’re already using intent data and want to move toward signal-based prospecting, the guide to migrating from intent data to signal-based prospecting walks through the transition step by step.