Why is it so difficult to gather the data needed for intent signals?
Collecting the data required to generate intent signals is genuinely hard. Three problems show up every time: too many sources pulling in different directions, regulations that keep tightening, and data that degrades faster than most teams expect.
The multiplicity of data sources
Useful intent signals draw on first-party, second-party, and third-party data simultaneously. Each layer carries its own friction.
First-party data is the easiest to handle because it comes from your own website, email flows, and social activity. The catch is that it only covers companies already interacting with you, which is a small slice of the market you’re actually trying to reach.
Second-party data means formal partnerships with other companies who agree to share what they know. Those agreements take time to negotiate, and availability isn’t guaranteed once the relationship changes.
Third-party data comes from external platforms tracking activity across the web, things like visits to trade publications or engagement on professional forums. It’s valuable, it’s often expensive, and it raises the most questions about privacy compliance.
Data compliance and privacy
The General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States both constrain what you can collect, store, and act on. That’s not going away.
The useful framing here is “legitimate interest by design.” When a company publishes a job offer, announces a funding round, or appoints a new executive publicly, that publication is the legitimate interest. The event itself creates the legal basis for outreach. Rodz operates entirely within that logic: every signal it produces is anchored to a publicly declared action, not scraped personal data.
Still, compliance adds real overhead. It narrows the sources you can use and forces trade-offs between data breadth and legal safety.
Data quality and accuracy
Even when data is accessible and compliant, quality and accuracy are constant concerns. Stale data sends sales teams after companies that have moved, employees who’ve left, or situations that resolved three weeks ago. An intent signal is only valuable for 48 hours. After that window closes, the context that made the signal actionable has likely shifted, and you’re back to cold-list efficacy.
That’s why verification can’t be a quarterly cleanup. It has to run continuously.
Historical challenges and current evolution
The idea of catching the right moment to sell, what we now call intent signals, isn’t new. Just after World War II, researchers would clip newspaper articles to track economic events and spot opportunities. Slow, manual, expensive, but structurally the same idea: find context before making contact.
Today, the digital footprint every company leaves behind makes that kind of research possible at scale and in real time. Every job posting, every public appointment, every registered subsidiary is a traceable event. The opportunity has grown considerably. So has the complexity: more data means harder sorting, more noise to cut through, and faster decay on anything you don’t act on quickly.
Solutions to overcome these difficulties
Leveraging advanced data processing technologies
Artificial intelligence and machine learning are what make it practical to process the volume involved. Rodz runs roughly 350 scrapers across 250-plus sites, each fully rebuilt four to five times a year to track how sources change their structure. Without automation at that scale, the signal-to-noise problem becomes unmanageable.
Choosing reliable partners
Third-party data providers have to meet clear standards on both data protection and transparency. If a partner can’t demonstrate how their data was collected and what consent framework it sits under, the signals built on top of it carry legal risk. That’s a non-starter for any outbound program that’s meant to scale.
Automating data collection and verification
Freshness is the metric that matters most. A signal that was accurate yesterday but hasn’t been checked since may already be wrong. Continuous verification, rather than batch updates, is what keeps the 48-hour window meaningful. Without it, you’re shipping stale context dressed up as real-time intelligence.
The positive impact of accurate intent signals
When the data is right and the process is tight, the numbers are concrete. Clients using intent signals from Rodz see their meeting count multiplied by 4x, a 74% higher close rate on meetings sourced from signals versus cold prospecting, and around 15 hours saved per week for their sales teams.
Those results don’t come from contacting more people. They come from contacting the right company at the moment its context makes it receptive. The canonical use case is simple: “I want to contact a company when [signal].” That when is doing all the work.
Signal stacking makes it stronger. A freshly incorporated company is a signal. A newly appointed sales director at that company is a second signal. A recruitment campaign for five or more salespeople in the same 30-day window is a third. Three signals overlapping means one clear move to make, and very little doubt about timing.
On average, a single contact crosses about four intent signals per year. That’s four distinct moments to send a fresh, contextually grounded message, never a follow-up to an ignored email.
Collecting the data behind those signals isn’t easy. The source problem, the compliance problem, and the quality problem are real. But the alternative is cold outbound: no context, four to seven follow-up attempts to compensate for missing timing, and close rates that don’t compare. Rodz has been building this infrastructure since 2018, before the category had a name in France, because the decay problem was obvious from the start.
To automate delivery of these signals into your own tools, here’s how to configure webhooks to receive signals in real time and how to secure those webhooks with HMAC-SHA256 verification.
Frequently Asked Questions
Why is collecting intent signals so complex?
The difficulty comes from the multiplicity of sources (over 250 for Rodz), the variability of data formats, and the need to process information in real time. Each signal requires a specific configuration among 222 possible configurations to ensure its relevance.
How do you ensure the quality of collected intent signals?
Quality relies on source diversity (to cross-reference information), data freshness (a signal only has value for 48 hours), and human verification on critical signals. Rodz uses over 350 scrapers with automated consistency checks.
What is the cost of setting up intent signal collection?
Developing an in-house collection infrastructure is expensive in time and technical resources. The alternative is a specialized platform like Rodz that pools this infrastructure across its clients, making access to intent signals practical for companies of any size.