Tl;dr - Success in complex sales depends on your ability to understand and reach many of the approx 10.2 members of the buying team. Who are they? What are each thinking? How are they acting individually and collectively? Those are important questions that purchase intent data can help answer - as long as it includes contact level insights. Here's how.
10.2 Cranky, Overworked, Biased Individuals
That's the size of the typical complex buying team according to Challenger research.
OK, they provided the number - I've added the color commentary.
But you know what I mean. Each of those folks has their own responsibilities, priorities, pet projects, personalities, preferences, departmental requirements, and more. And they're thrust together into a multi-disciplined buying team to understand a complex requirement, find potential solutions, and select the best one.
Many of the people on the team may not even care about solving the problem because it doesn't affect them - while other problems that do are going unanswered in the meantime.
It's a difficult situation for sure. And it's one that's made even more complicated by remote work and the ease with which folks can "hide" behind digital communications.
Salespeople have an enormous challenge managing large buying teams, and therefore their biggest competitor is often the status quo. More and more deals end in "no decision."
Intent data can help.
"But" you might be asking "how? The only thing my data tells me is that there's some activity happening in the account. That might confirm a project but doesn't help to understand the buying team at all. What are you talking about?"
Fair point.
Contact level intent data can help. Here's how.
Identify and Understand Active Members
One of the common complaints about intent data is that it only provides some vague account level signal.
In other words, someone - or maybe a few people - from XYZ company have taken actions that seem to indicate some activity related to some topic. Not only do you have no idea who they are (job title, seniority, location, how many actions they've taken, etc.) but you're simply guessing that the specifics of their activity, which are hidden behind an opaque topic taxonomy, actually indicate some meaningful activity.
Obviously, account level purchase intent data doesn't provide any detail about the individuals. However, contact level data does.
As the name implies, contact level data delivers specific information on the person who is taking action that might indicate purchase intent. That reported signal includes:
- Job title - from which we understand seniority, function, role on buying team, likely priorities, and perspectives
- Account - the contact is naturally associated with an account
- Key-term/Competitor - signals are reported with specific key terms or competitors (provided by the client) around which the contact has taken action
From this array of details sales operations can infer some further detail including the following:
- which roles on the buying team are identified
- the likely thoughts of the contact (stage in buying journey, problem to solve, outcome to achieve, solutions to consider, competitors engaged, etc.)
Sales operations and/or marketing can use those insights to inform sales enablement for the outbound sales team with details including cadence to use, questions to ask, content to share, messaging to use, and more.
In addition to this explicit information (at least inferred), contact level prospect intent data also can help to understand what's missing.
Recognize Gaps in Active Members
Strong sales companies have clear understandings of the buying team and participating personas.
As contact level intent data helps to match active contacts to their likely roles on the buying team, when we overlay those we've identified on the typical team matrix, we can identify which roles are conspicuously absent.
Does that mean that they're not participating? We don't know - this certainly isn't dispositive.
It does, however, remind us of blind spots and suggests questions we should be asking of our deal champion.
For example, if we see no activity from IT, but know that they're often involved, we can inquire whether IT is involved in this project at this company, how they normally participate, and whether there's any internal discussion.
Deals end in no decision most frequently because of what's not clear, what's not said, and what we never see. It's those blind spots that often derail deals. After all, when we are aware of a problem, we typically address it.
In this way, contact level intent data helps to identify the gaps in the buying team to sensitize us to possible issues so that capable marketing, sales operations, and account executives/outbound sales can proactively address lurking issues.
Understand the Team Dynamic
The challenge of large buying teams for complex buying situations is that they are comprised of complicated individuals, and have an additional culture and behavior in aggregate.
We can understand active individuals and identify potentially inactive participants as noted above. And contact level data can also help, in aggregate, to understand the inertia and activity of the entire buying team.
Because you know the typical roles and mindsets of the common members of the buying team, and can therefore identify divergences and gaps, or unusual actions, you can also draw some conclusions on what the buying team is doing in aggregate.
For instance, mid-level marketers might commonly and enthusiastically pursue the latest marketing automation or sales acceleration SaaS solutions, while their IT colleagues might normally resist (reflected in slower progression through the buying journey, involving lots of competitors, engaging with other solutions, etc.) This dynamic is often revealed by the actions observed for each role. But if you observed a situation where IT was actively engaged along with the C-suite, but no mid-level marketers, you'd have some clues to a very different sort of situation.
You might not know exactly what is happening, but you'd have good reason to believe it's not according to your standard playbook or expectation, and therefore would be able to nurture, research and sell accordingly.
Sales and Marketing Intelligence
Of course, we're not sitting in the buyers' conference room or privvy to their slack channel and zoom meetings. So our inferences and conclusions are based on analysis of intent data signals which are a valuable form of sales and marketing intelligence. The process of that analysis is necessarily imperfect, and everyone should have realistic expectations.
Over time, and as analysis is refined though, it improves and provides more insight. This supports predictive lead scoring and propensity to buy models as well as other enterprise sales activities including sales forecasting, target account sales, sales pipeline management, and sales enablement.
Sophisticated Data Orchestration
This is necessarily more complex than most intent data playbooks. Typically companies are using account level data very generally to identify and prioritize accounts, and the most recognized providers supply types of data are account level only which isn't capable of providing this type of insight.
Therefore, while it's a powerful opportunity, it's beyond the scope of common data activation. Success requires more creative approaches to data, powered by a stronger and more capable tech stack.
Every company that aspires to unlock the power of data for better sales results should be experimenting with this approach.
But maybe your sales are consistent, reliable and rapidly growing already.
