Tl;dr - Beyond demand generation there are numerous use cases for intent data. Contact level data particularly opens up some interesting insights into personas, competitors and market positioning. It's helpful to understand who's engaged across the market, by job function and seniority, for instance, compared to who's engaged on your site and with your content. If they're aligned, that's reassuring. If they're dissonant, that's scary - but it's a great warning to get things on track!
Lazy Assumptions = Big Risk
We've all seen the casual "buyer persona" documents created according to standard psychographic templates. They often reflect the creators' biases and assumptions. Occasionally they reference an interview or two - typically with folks who are known and fit the expected profile. They often serve more to reassure marketers than to really challenge and shape a company's market approach.
Of course there are substantially different and powerful alternatives. Adele Revella's model is an example. Based on deep qualitative research around five categories that plumb the depths for the real reasons people and teams decide to change, or not, it's often startling substance.
Both approaches, however, often lack quantitative confirmation or insights.
For instance, while we know the profile (industry, company size, job function, seniority, stage in buying journey) of folks who commonly convert on our site, that's a skewed sampling by definition. Is it the same for our competitors? How about early in the research process? How does it change as buying teams coalesce around a project?
Those are questions that often can't be answered. After all, how could they be?
And therefore we proceed merrily along on our way assuming that our understanding is accurate and using that foundational assumption to inform our positioning, messaging and competitive marketing.
That's a big gamble.
There's a better way. Actual observation!
Seeing Around the Internet's Corners
If you believe that director and manager level marketing folks are your target persona, and you create content for them, will you be satisfied with the validation when they come to your site? When sell cycles are slow, will you know that it must be sales' shortcomings, because you're attracting and converting the right buyers? Or is this all one big confirmation bias cycle?
Contact level intent data can provide some really interesting insights. Just be prepared for the fact that the findings might challenge some sacred cows.
Here's an interesting exercise that we take clients through, and use ourselves for reality check and adjustment.
We track actions taken by people within our Ideal Client Profile (Account and Individual fit by size, industry, location, job function and job seniority) across all the structured and unstructured information on the web, by week. This lets us test assumptions and learn about the market - in snapshots and to observe trends. Things we track include:
- Engagement with each competitor
- Ratio by job seniority (does one competitor resonate with the manager level while another rocks the CSuite?)
- Ratio by job function (IT, HR, marketing, sales, finance, etc. - which competitors seem to connect with each function)
- Geographical distribution (where are they particularly engaged? absent?)
- Unique job title interactions (if 50% of activity is with sales, but spread randomly across 400 job titles that tells a very different story than that same 50% being split at 80% with CROs, 10% with VP and 10% distributed )
- Ratio by company size (maybe a different competitor dominates each of the SMB and enterprise size market)
- Ratio by industry
- Engagement with each theme/topic of key terms
- Ratio by job seniority
- Ratio by job function
- Geographical distribution
- Unique job title interactions
- Ratio by company size
- Ratio by industry
- Job title distribution for overall engagement
- Buying team activity (beyond the fact that there's a surge within a company, to the details of it)
But the insights go deeper. For instance, we can generally associate certain key terms with certain stages in the buying journey. So it's possible to understand how the composition of the buying team evolves as projects progress.
The competitive analysis provides insights (a competitor that's strongly exposed to an industry or geographic market experiencing a downturn) and opportunities (want to connect with more CMOs or enterprise prospects? Then reverse engineer the content of your competitors who are strong in those ways.)
When we dig into buying teams we're able to see what roles join the process at what stage in the journey, within each company. We can also see which members are engaging with which competitors, which problems they're trying to solve and what outcomes they hope for...even where each is in the buying journey. It's like x-ray vision into account level surges.
The Data Makes This Possible
This provides a fascinating data journey. While intent data is often thought of as a simple boost for demand generation, these examples illustrate both the strategic value to competitive intelligence (CI) and the tactical sales intelligence application for complex sales managing buying teams.
But how is it possible? Because it's contact level data. In other words, the intent signals are reported with contextual detail and contact information. That includes job titles which can be parsed for seniority and function, key terms which can be associated with topics and stages in buying journey, competitors, and firmographic detail.
It's all publicly observed, and it's all based on actions people take. That means not just a site search on a participating publisher's site, or a programmatic ad flashed before someone. Instead this is data based on observing people taking action publicly.
Most people think of contact level data as simply facilitating personalization and outbound sales - knowing who's active and having contact details to follow up. The same data, though, can be used across the enterprise by people from different departments and with different perspectives from tactical to strategic.
Be Ready to Be Disrupted
Sometimes the persona and market engagement data reinforces what you assumed. But sometimes, once you roll it around in your BI/data visualization tool, something really surprising bubbles up. Think along the lines of those cringeworthy stories of surprise DNA test results or Eureka! moments in R&D.
We've seen companies change strategy with the benefit of actual data on their product/market fit.
We've seen personas overhauled, battle cards rebuilt, and campaigns pivoted.
We've also seen reassuring validation of the go-to-market approach of other companies.
We've even been surprised ourselves. In one recent instance we discovered that engagement around a collection of terms and competitors which we assumed would be very nearly 100% marketing in job function and high level in seniority, was actually more mid-level and 40% sales function. Pretty cool on the one hand, to actually have the data. Pretty alarming on the other, that an area we know really well, and in which we were so confidant, actually confounded us.
Beyond a "Plug-in"
The more companies understand the power of intent data, the more they understand the opportunity beyond the increasingly common simple data plug-in to help prioritize accounts. That increased understanding also often leads to an appreciation for nuance in data methods that make a big difference in what's possible.