Why is it so difficult to gather the data needed for intent signals?
Collecting the data required to generate intent signals is far from a simple task. Several factors make this process complex and challenging, including the multiplicity of sources, increasingly strict regulations, and the variable quality of available data.
The multiplicity of data sources
To build relevant buying signals, you need to collect data from multiple types of sources: first-party, second-party, and third-party data. Each of these sources presents its own challenges.
- First-party data is the most direct to collect, since it comes from interactions on your website, emails, or social media. However, it only represents the tip of the iceberg: you can only capture information from prospects who are already interacting with you.
- Second-party data requires solid partnerships with other companies to exchange information. This involves building trust and respecting formal agreements. Such partnerships are not built overnight, and data availability is not guaranteed long-term.
- Third-party data comes from external platforms that collect information across the web, such as visits to specialized sites or interactions on professional forums. This data is valuable, but it is often expensive to acquire and raises important questions about privacy and legal compliance.
Data compliance and privacy
The regulatory framework around data protection has become increasingly rigorous, with laws like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations impose restrictions on the collection, storage, and use of personal data, which further complicates the process.
Complying with these regulations often means restricting the sources and types of data you can collect. This can limit the quality and quantity of available data, making the generation of intent signals more complex and requiring a delicate balance between commercial opportunities and legal compliance.
Data quality and accuracy
Even when data is available, its quality and accuracy are major concerns. Poor-quality data can lead to incorrect buying signals, which results in misguided prospect prioritization, wasted prospecting efforts, and loss of confidence from your sales team.
For example, outdated data, such as information about a company that has relocated or an employee who has left their role, can lead sales teams to contact the wrong people or miss a real opportunity. It is therefore essential to implement mechanisms to continuously verify and update collected information.
Historical challenges and current evolution
The idea of detecting the right moments to sell, what we now call intent signals, is not new. Just after World War II, researchers would clip newspaper articles to gather information on economic events and detect opportunities. It was a manual process that demanded enormous effort, but it was the first version of intent data.
Today, thanks to the growing digital footprint of companies, the amount of data available to capture these key moments is unprecedented. Every click, every interaction, and every major event leaves a digital trace, creating incredible opportunities for prospecting specialists. However, this abundance of data also poses challenges in terms of processing and relevance: the more data there is, the more complex sorting and analysis become to produce accurate signals.
Solutions to overcome these difficulties
To overcome these challenges and maximize the use of intent signals, it is essential to adopt a rigorous data strategy and invest in the right tools and partnerships.
Leveraging advanced data processing technologies
Using technologies such as artificial intelligence (AI) and machine learning is crucial for processing the growing volume of data, cleaning it, and identifying the most relevant information. These technologies automate data analysis and extract actionable intent signals without requiring human intervention at every step.
Choosing reliable partners
Working with reliable third-party data providers who comply with privacy standards is fundamental to ensuring data quality and compliance. At Rodz, we only work with partners who meet high standards in data protection and transparency.
Automating data collection and verification
Automating data collection and verification is an essential step to ensure data freshness and accuracy. By ensuring that data is continuously updated, you can avoid errors caused by outdated information and maximize the relevance of generated signals.
The positive impact of accurate intent signals
When the data needed for intent signals is collected and processed optimally, the impact on prospecting is significant. Clients using these signals see their meeting count multiplied by 4x, a +74% increase in closing rate, and a time savings of 15 hours per week for their sales teams. These results clearly show that, despite the challenges, collecting the right data at the right time makes the difference between random prospecting and a truly effective strategy.
Collecting the data needed to produce buying signals is a complex challenge that requires overcoming obstacles related to source multiplicity, regulatory compliance, and information quality. But by investing in the right technologies, choosing reliable partners, and automating collection and verification processes, it is possible to turn these challenges into opportunities.
Intent signals allow you to stop relying on luck in prospecting and start acting strategically, targeting the right prospects at the right time with the right message. A true revolution compared to the era when researchers had to clip newspapers by hand, and a major growth lever for those who know how to leverage these signals.
To automate the delivery of these signals to your own tools, learn how to configure webhooks to receive signals in real time and how to secure these 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 three pillars: 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 very expensive in terms of time and technical resources. The alternative is to use a specialized platform like Rodz that pools this infrastructure across its clients, making access to intent signals affordable for companies of any size.