AI-Driven Predictive Analytics in B2B

In Part 1 of this two-part series, we talked about how mission-critical intelligence is often right under our noses but we don’t know how to find it. We went through the types of signals contained in such intent data, and how B2B sellers and marketers can work to collect them.

But like the Allied codebreakers in World War II, we understand that collecting those signals is only the first step. To actually do anything with them, we need to decode them.

In World War II, the critical break came in the form of technology—the Colossus computer. And in B2B, it’s technology to the rescue again.

B2B’s Colossus: Decoding Intent

Intent data without predictive analytics is like those mountains of signals the Allied forces gathered but couldn’t understand without a computer to decode them—to convert them into actionable information.

For B2B sellers and marketers, the decoding machine comes in the form of predictive analytics driven by artificial intelligence (AI).

Robust data paired with predictive analytics is essential for modern sales and marketing leaders. It makes it possible to do things we could never do otherwise—even with rooms full of intel-gatherers and decoders trying to interpret data.

Here are just a few examples of what I mean.

  • Go-to-market teams can plan where to focus their resources. AI tells us what our total addressable market (TAM) is. It also tells us our ideal customer profile (ICP). And it even tells us who in our ICP is ready to buy right now.

    That allows us to know whether we have enough headcount to cover all the in-market ICP accounts we’ve surfaced. Or, conversely, if we want to expand our ICP or TAM, it pinpoints the smartest places to put our energy and dollars for the most marketing and selling success.

  • We can determine the best roles to market and sell to. We now know who makes up a typical buying team, which takes all the guesswork and random persona exercises out of our planning.
  • We stop missing out on deals. Have you ever seen a competitor put a logo on its site, yet you hadn’t even realized that their new customer was in-market?

    With data and AI, you get a complete view of engagement at the contact level, including what keywords they’re researching (and, therefore, what’s most important to them right now). You can even determine contacts’ relationships to each other so you can put together an overall picture of buying-team engagement.

  • We get our timing right. Is this a time when Marketing should engage? Or is it a job for Sales? Maybe Customer Success? You can put those questions to bed with data and AI that determines where an account is in the journey and then orchestrates the right action.
  • It provides a complete view of engagement—and tells you how and when to act on it. AI-based predictive models compare how a contact’s engagement with that of previous buyers, including information on which contacts are engaged, how and when they engaged, and where you have no activity. That information allows you to engage the right contacts at the right time, and also to fill any gaps in your database with new contacts.

    It’s a vast improvement over traditional point-based scoring in marketing automation. Rather than humans’ deciding on the importance of individual activities, AI continually analyzes patterns in the data and determines what’s most relevant.

  • It gives us a picture of the entire buying team. In B2B, we’re not dealing with a single contact. We’re dealing with a team of 10 or so people. So, seeing engagement from one contact on a buying team is much less meaningful than seeing multiple contacts on that same team showing intent.

    What’s most meaningful is seeing people in roles with whom you’ve had success in the past showing intent. And intent data plus AI can match roles and profiles to previous purchases so you know what activity is most relevant to your company.

  • It shows us where accounts are in their buying journey—and whether they’re in-market. Knowing whether a buyer is just getting started on their buying journey or getting ready to issue an RFP puts sellers and marketers in a position to provide relevant information at each stage that helps buyers move forward.

    Bigger picture: it gives an overview of all the commercial opportunity available to a company right now.

Turning Intent Data Into Intelligence

When we have deep insights into our current and future customers’ behaviors, we can deliver incredible experiences that put them first, and that in turn help us improve revenue success. Companies that have cracked the code on intent data see impressive results—like a 35% better average deal value, 20% higher win rates, and 20% reduction in average days to close.

We are in business, not at war—thank goodness. But we are competing every day. And every bit of actionable intelligence we can amass gives us an advantage.

A full-picture approach to intent, paired with AI-based predictive modeling and orchestration technologies, shines a light on previously hidden signals.

For B2B sellers and marketers, uncovering and deciphering encoded signals is essential for competing, remaining relevant, and providing the best user experience possible. But it’s only possible with the right data—and the technology to decode it.

This code-breaking approach is a total game changer. It’s what takes us from humans’ making their best guesses to really knowing what potential buyers are doing so we’re in the best position to win.

This article series is written by Latané Conant, CMO of 6sense, a leading AI-powered account engagement platform.

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