Intent signals flare up constantly across the internet — billions every day — and behind many of those signals are in-market buyers. Somewhere, someone has clicked on an article, downloaded a guide or attended a webinar that relates to your business.
The goal is to capture that data about buyer behavior and turn it into intent intelligence you can use to find your next customer. So many stages of B2B purchase decisions happen online, long before a sales team becomes aware of the individual or account. Unless you can look widely and not just in defined environments, you may limit the audiences where you can hunt for leads.
Data is often where digital agencies and B2B organizations hit campaign roadblocks. So many requirements get put on demand gen data before you can even think about activation for campaigns. It must be fresh, accurate, compliant, holistic and actionable.
One of the most important steps agencies and brands can take to get data right, and therefore get better results, is to use data that’s widely and organically sourced. Before that demand gen data got to you, what happened to it? How was it gathered to ensure compliance and accuracy?
Some demand gen partners depend on publisher web sites to capture intent data. The problem comes from assuming this actually represents buyer intent. Intent activity from some of these platforms may have little to do with what prospects are really interested in. The site’s content actually reflects the publisher’s view according to their own editorial calendar, more so than organic buyer search behavior.
This type of lead isn’t sourced from the entire internet (but that’s where today’s buyers are.) It’s just from that publisher’s content. So when their data says cloud or network security or whatever the topic was way up last month, that’s all because of the publisher’s choices. They create their own noise, and then attribute interest to the noise they create. It’s a reflection of what readers have been offered that month around limited topics from an editorial calendar. This is fine for some brands, but not for many others.
Are you aware of all the potential audiences and buying communities relevant and available to you for campaigns? Do you use a marketing cloud to build out custom segments by drawing on an internet-wide pool of intent data? This matters, because you can identify and target “boutique” audiences, rather than tired, overused off-the-shelf audiences.
A better approach is to use intent signals curated from the entire internet, not just one walled garden or editorial site. Some data providers use artificial intelligence (AI) methods to source and scale organic intent data from billions of signals across the internet. Natural language processing (NLP) “reads” actual content and determines its topic, rather than relying on a publisher tag.
When you hunt intent signals across the entire internet, you look at the interest of everyone, as opposed to the interest of what publishers want to talk about. Internet-wide access contributes to finding in-market contacts B2B marketers want. Those are the signals that indicate intent behavior and fresh leads for your campaigns.
Look for a source or partner who triangulates data to identify actual individuals expressing intent. This affects the accuracy of contact data for leads. Intent data reveals facts about what’s truly going on when you need to know it. Clicks, page reads, downloads, event registrations, bid stream data from AdTech – it all triangulates to reveal user intent. (We call it Identity Graph Triangulation®.)
It’s even better if audiences are updated weekly by your data partner. Custom segments that refresh every week further assure access to the most current data available. It’s also more convenient when these audiences are available via a DSP for use in programmatic programs run by an agency or enterprise team.
Whether you’re on the agency side or the brand itself, do you know the average deal size won with help from the leads your team provides? What’s the cost per opportunity? It’s a good conversation to have, especially when you can confidently point to the quality of your leads. The average deal size from one data source might be $100,000, while that of another might be half that.
The quality of your data matters in the pipeline, and the proof is the revenue your leads generate. So consider the source.