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 idea is straightforward: if a decision-maker spends time researching cybersecurity solutions, they probably have a cybersecurity need.
Intent data providers (Bombora, G2, TechTarget) 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 are dated, they are 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. However, the progressive disappearance of third-party cookies (Safari and Firefox already block them, Chrome is restricting them) reduces the coverage and reliability of this data.
The attribution problem
Intent data detects that a “company” is interested in a topic, but rarely identifies which individual. The office IP address identifies the company, but not the department or the person. The sales rep receives a signal like “Company X is interested in cybersecurity” without knowing if it is the CISO, an intern, or a contractor.
The noise problem
Searching for a topic online does not mean having a need. A journalist writing an article about cybersecurity generates the same intent signal as a CISO searching for a solution. Intent data algorithms attempt to filter out noise, but the false positive rate remains high.
The timing problem
Intent data is generally aggregated on a weekly or biweekly basis. By the time you receive an intent signal, the prospect may have already finished their research and selected a vendor.
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, indisputable event. The sales rep can mention it in their email without risk of being wrong.
Contextualization
The signal provides a precise approach context. Instead of “this company is interested in HR topics,” the signal says “this company hired 12 people in 3 weeks and just appointed a new HR Director.” The sales message writes itself naturally from this context.
Technology independence
Intent signals do not depend on third-party cookies, tracking pixels, or ad networks. They come from public sources (registries, press releases, job postings) that are not threatened by evolving online privacy standards.
GDPR compliance
Intent signals are derived from public and professional data. Their use for B2B prospecting is covered by the legitimate interest basis under GDPR, without the gray areas associated with online behavioral tracking.
How to Combine Them
Intent data + intent signals = predictive prospecting
The two approaches complement each other:
- Intent signals identify the right moment: a funding round, mass hiring, or executive appointment reveals an objective need
- Intent data confirms the interest: if the same company is actively researching solutions in your domain, the signal is doubly validated
This combination significantly reduces false positives and increases conversion rates.
In practice with Rodz
Rodz primarily uses intent signals (verifiable events) as the foundation of its detection. 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 the majority of companies that do not have access to intent data, intent signals alone are sufficient to multiply meeting rates by 4.
Frequently Asked Questions
Should I choose between intent data and intent signals?
No, but if you must start with one, start with intent signals. They are more reliable, more contextual, less expensive, and independent of third-party cookies. Intent data is an accelerator for companies that have already mastered intent signals.
Will intent data disappear with the end of cookies?
Intent data based on third-party cookies will indeed shrink. But other forms of intent data (first-party, contextual) will take over. Intent signals, on the other hand, are not affected by this evolution at all.
What is the comparative cost of both approaches?
Intent data requires a subscription to a specialized provider (Bombora, G2) that can cost several thousand euros per month. Rodz intent signals include detection, enrichment, and scoring in an integrated offering, generally more accessible for SMBs and mid-market companies.
If you already use intent data and want to evolve toward intent signals, our guide to migrating from intent data to signal-based prospecting details the transition step by step.