Tl;dr - Are you working from all the intent data you require to understand buying teams, deals, account activity and individual buying journeys? Or are you simply looking at the same tiny slice as each of your competitors? Understanding the breadth of intent data sources is important to selecting the right data tool for your use case and goals.
2 Billion Websites
You know what happens on your site. That's why you've invested in marketing automation and other tools.
The purpose of third-party intent data is to understand what's happening everywhere else on the web. To collect the sales and marketing intelligence that will help craft the right prospect and customer experiences...and ultimately to drive revenue.
Are activities on all of the other 1.99999999 billion sites relevant? No. Of course not.
But have you ever asked, or wondered, what sites your buyer intent data source observes?
If you're like most buyers, the answer is simply no. Most people take it on faith that their data is delivering everything that's relevant.
That's not a safe assumption.
Data Models and Scope of Reach
Let's dig deeper into how 3rd party intent data models work, and what it means for the reach and scope of the data they deliver.
Publishing Data Exchange
The publishers' coop which informs a leading source of "surge" data describes having "4,000+" participating sites which share anonymous (no contact-level information) data with identifying account information.
4,000 is approximately .0002% of active sites. Maybe it includes the most active for your industry and buyers. But maybe not. How do you know? And even if the list looks solid, how do you know what you're missing? An intuitive and superficially complete list is not dispositive that it is indeed a complete list.
Bidstream Data
A well known source of bidstream data promotes its solution as having broad coverage with a "reach across the web of billions of web pages."
That's clearly a very different approach. One, it's important to note, that has raised questions regarding propriety of the data, and accuracy is also a topic of hot debate.
Registered Users
While technically second-party intent data (not third,) another model is deliberately narrow in focus. Publishers which have relationships with opted-in and registered users often monetize the user interactions with the content they own.
By design, therefore, the breadth of the reach of their data is quite limited - to their own properties only. G2 and TechTarget are among the better known examples. The former focuses on a single review site; the latter on its network of 140+ sites.
Neither promote their data based on reach. The value proposition is in the depth of insight into contacts and activities which are owned by the publisher, and which terms of use and registration allow them to monetize.
From the publisher's perspective this is a beautiful model. They can invest once in creating content and areas of expertise and then sell data and campaigns to all the competitors in a space. Yes, that's correct. It probably means that your competitors are buying the same leads you are. 😬
All of the Structured and Unstructured Data
IntentData.io's model is different. It covers the entire web like bidstream, but in a critically important contrast, does it by observing actions taken publicly for all to see.
That means signals include insights observed on many sites beyond the publishing coop model.
Of course it also means that there's no reliance on third-party cookies or IP address resolution which ensures that while other sources have seen severe declines in signal volume during a surge in work from home, our data streams remain robust - even increasing as web activity increases.
An Actual Intent Data Sample
Let's look at a specific example. In a randomly selected IT security related space we recently observed the following over the course of a couple months:
- Number of signals: ≅360,000
- Number of companies: ≅74,740
- Number of sources: ≅10,640
Let's unpack these a bit.
First of all, they are based on public actions taken. These aren't search engine searches, site searches, page scrolls or other hints of possible interest. They are clear, demonstrable instances of public engagement.
Second, in each case the specific related key term, competitor name or other granular detail was provided. So marketers and sales pros have the intelligence they need to segment and infer where people and buying teams are individually and collectively in their buying journey, the problems they are trying to solve, the outcomes they want to achieve, the composition of the buying team, etc. The signals are of varying value - and that's determined by the client/user, not the person constructing the opaque taxonomy behind a "topic. (More on topics vs. key terms here.)
Third, you're probably not paying for intent data to breath the same exhaust as all your competitors. (Wondering how much purchase intent data costs? Learn more here.) You want creative insights, broader signals and potentially more valuable intelligence from more esoteric sources. Sites like Reddit for instance are particularly intriguing for many users because they're perceived as providing unvarnished insight.
That's why we believe that the number of sources is critically important. If only 10% of a 4,000 publisher coop is actually relevant to your market, that means only 400 sites.
In our example the 10,640 is actually understated because there were a number of bit.ly links which were consolidated and considered as a single source even thought that's obviously not accurate.
Of course data typically reflects a Pareto distribution like everything else. So many of the sources had relatively few signals compared to the ones which were most common.
It's important to note that this example was prepared from internal raw data. Every signal we provide includes specific detail on the type of engagement, and the specifics (exact key term, competitor, etc.) This is substantially more granular detail than most vendors provide, and in our experience is the optimal combination of actionable insight. Less and people just guess. More and the analysis becomes paralyzing. Therefore we do not provide source links.
Aggregate Power of Intent Data Insights
Intent data is typically used tactically (engaging the right accounts and people with the right message at the right time) and strategically (understanding market trends, personas, competitor activity, etc.)
The breadth of sources - the entire internet - is important for both.
Tactically, understanding buying teams, problems to solve, outcomes to achieve, competitor engagements and stage in buying journey all provide important sales and marketing intelligence to ensure that messaging is optimally segmented. Even small volumes of very specific signal from important key terms (not volume from opaque topics) can provide critical insight to savvy SDRs and AEs.
Strategically, imagine the power of understanding what job functions/seniorities engage at which stage in the buying journey? Or what competitors engage most often with which seniority? Of what TOFU "problem" key terms are most often associated with opportunities that advance through the pipeline to the deal stage (or those that consistently fail to!) That's incredibly powerful information for product and demand gen marketers, and the entire sales team.
But....they're only powerful if they provide a complete picture. An artificially constrained view may actually be counterproductive as it misinforms decisions.
Bottom Line - Do Your Research
Too many people buy purchase intent data because it's integrated into a contact database, ABM software platform or some other tool.
Of course simplicity and convenience are appealing.
But often, as people start to understand the implications of how data is collected, they quickly develop a deeper appreciation for the power and/or limitations of various models.
It's a big internet. You're banking on your third-party intent data helping you to understand what's happening out there.
Maybe .0002% isn't enough.
