How To Accelerate GTM Strategies With Intent & Behavioral Signals

Published: April 13, 2022

Marketers are no longer using their mother’s intent data — as with most B2B staples, intent data has rapidly evolved and now has a range of uses that includes everything from proactively identifying in-market accounts to flagging churn signals in existing customers.

Intent data is all about increasing go-to-market (GTM) productivity and identifying how marketers can make their GTM more agile and responsive to dynamic market inputs. When a GTM organization can use its actions to create behaviors in a targeted segment or account, it creates new information about the target’s intentions.

“What forms the core of the intent-based dimension within the overall data-driven revolution is that it is about current behavior in actual markets,” said John Steinert, CMO of TechTarget. “What we’re seeing is that the observed behaviors of real buyers make a huge difference to what a GTM organization will actually do. For example, when we can show a portfolio marketing team the actual content that’s resonating with their target buying centers, they can see where they need to take their messaging and positioning.”

With almost a third of marketers investing in intent data throughout the year and an additional 40% planning to collect data on prospective buyer behavior in order to boost engagement and messaging, the Demand Gen Report team sat down with Steinert to dive deeper into how intent is working as a catalyst of change for the better.

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Demand Gen Report: What role does behavioral data play in the intent landscape? Do you think it’s an essential part of marketers’ arsenals?

John Steinert: For us, behavioral data defines the difference between the intent landscape and the technographic, firmographic and demographic areas. While the popularity of the term “intent” may have been helpful to spur innovation in the data industry as a whole, my opinion is that it may now have reached the point of confusing practitioners.

Take technographics, for example. Really good install information can even tell you when a SaaS contract will renew, but it can’t tell you much at all about the sentiment around the particular installation. So while you know that the account will either renew or replace at a specific time, and you may even have information about replacement rates that give you an idea of propensity, without behavior data, you know nothing about a particular account or its people.

DGR: How can marketers use intent data to identify and understand the behaviors of active buyers to target them more effectively?

Steinert: There’s something a bit subtle embedded in this question that needs some teasing out. In the industry, intent is now being applied to a wide range of behavioral signals. That can be pretty confusing to a busy GTM team trying to get the most out of its tech and data sourcing. For many of the sources out there, the answer to this question is around how data can help companies identify, understand and target active buyers.

Most sources of “intent” are quite weak in their ability to connect their signals to actual in-motion buyer’s journeys. In B2B, buying decisions are pursued by groups of individuals. Within a given buying team, the different functional members will care most about their own functionally specific considerations.

However, there are solutions that help you monitor at the opt-in person level, providing two pieces critical to determining what actions to take. First is that you can see the group forming, so you can distinguish between single leads and real opportunities. Second is that you can understand the needs of each member based on the very specific content they are consuming. This informs whether there’s an opportunity or not and how to engage each member of the team. With hundreds of client teams, we’ve learned that those two elements are far and away the most powerful drivers of increased yield — real GTM productivity gains.

DGR: Why is it so important to have structured analytics in place to analyze intent data? What are the benefits?

Steinert: As buyers, marketers need to be very clear about the difference between modeled (probabilistic) structure and observed (deterministic) structure. Probabilistic structure is a suggestion of what might be going on, whereas deterministic structure is a view into what is actually happening. As rollout project leaders, you’ll want to assess how comfortable your user community is likely to be with a modeled view as a guide.

While on the B2C side (given the huge, relatively simple transactions involved), such modeling can be a huge lift to productivity, in B2B, modeling is super complex and therefore a potential drag on implementation, acceptance and outcomes.

DGR: How can marketers use intent data and behavioral signals to close funnel leaks and help eliminate churn?

Steinert: By funnel leaks, I usually mean things that get by us that shouldn’t have. High-quality intent data should be used to enrich our understanding of any given account, any specific lead and every opportunity. Account leaks happen when a salesperson is unable to properly prioritize and action real demand present in the account. High-quality intent focuses a rep’s attention where yield is likely to be highest.

By illuminating other active members and activity as a buying team coalesces, quality intent data can uncover when a lead is actually a leading indicator of an actual buyer’s journey. The last common funnel leak we’re all familiar with is stalled opportunities. Not only can intent tell you the people involved in the opp (that usually have not been associated to it in CRM), but it can also expose what the core players are currently thinking about. This gives sales and sales enablement the material it needs to reinvigorate a potential deal.

For more expert insights into the world of intent data, be sure to check out DGR’s “What’s Working In Intent-Based Strategies” report.

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