Welcome back to another episode of Funnel Lab Fridays! Funnel Lab Fridays is our weekly LinkedIn Live series where we tackle marketing challenges and share use cases for data-savvy marketers. I’m your host, Eric Westerkamp, CEO at CaliberMind. Today, I’m joined by Doug Bell, CMO, and Misha Salkinder, Director of Customer Data Strategy. We’re discussing the concept of Marketing Decision Engines (MDEs) and how they can supercharge B2B multi-channel models.
Breaking Down the B2B Multi-Channel Growth Model
A Marketing Decision Engine is a set of data pulled together in a single environment, typically a combination of attribution data, funnel data, and engagement data. This allows marketers to gain insights into what’s performing well and what’s not, enabling them to make informed decisions.
The reason marketers are shifting our focus to multi-channel models, and moving away from the classic funnel, is that large enterprises have already made this transition. They’re dealing with more complexity, and the traditional funnel doesn’t capture that.
Doug illustrated this point with an example, “Take Facebook. You might see a lot of activity driven by Facebook, but in B2B, especially with complex products, Facebook isn’t usually where the purchase happens.” However, it plays a crucial role in creating awareness. In a multi-channel model, each channel plays a specific role in the engagement funnel—not just the lead or account funnel.
The Evolution from Funnels to Engagement Models
Misha adds that “a model that looks through the entire funnel might not be granular enough for decision-making.” Marketers are getting more sophisticated with the buyer journey and seeking more nuanced insights.
Doug agreed. “We marketers are simple folks; we don’t want complexity. We wish one channel could produce pipeline and bookings, but that’s not reality.” Buyers today are savvy. They navigate around the process until they absolutely have to engage with a company. Managing that complex, multi-stage journey is where the Marketing Decision Engine plays a vital role.
At CaliberMind, we see buyer journeys that can last up to a year. We might see surges in engagement, and then suddenly, the buyer is ready to talk. Each channel—be it G2, LinkedIn, or Facebook—plays a different role in that journey. The MDE helps us manage and optimize this complexity.
Attribution vs. Engagement: Levels of Nuance
We often see that high-frequency events get a lot of attribution, which can skew the data, like a recommendation to send more emails. But that’s not necessarily the most effective strategy. Companies are starting to recognize this and are looking beyond attribution.
They might look at engagement scoring or even models that assess intent versus engagement with their organization. Looking at attribution in a vacuum isn’t enough because there’s more nuance involved.
Doug adds that “Attribution is a trailing indicator—it tells us what allowed a successful event like an opportunity or booking to happen. Engagement data, on the other hand, lays out the buyer journey in real-time, allowing marketers to react and adjust.”
The Complexities of Large Enterprises
In many ways, our buyer journey at a smaller company like CaliberMind is less complex. Larger organizations have multiple products, business units, and buyer journeys. Managing a multi-channel model becomes exponentially more challenging at that scale.
Misha added, “For large enterprises, the importance of a clean, large, and wide data set becomes crucial. You need to be able to slice and segment the data to get insights for specific subsets. For instance, commercial accounts don’t act the same as enterprise accounts.” Being able to identify those differences is vital.
From Leads to Accounts to Engagement
Doug brought up a critical shift in the industry: “A couple of years ago, Forrester declared the MQL dead. It’s a bold statement, but what they were getting at is that the classic lead funnel doesn’t work anymore.” Marketing is moving towards an account-based funnel because buying units involve multiple buyers.
Eventually, Doug sees the industry moving towards full engagement models where we’ll care about how much engagement we’re creating over time, recognizing that we have limited control over the progression, but encouraging more engagement.
Misha agrees that there’s going to be a pendulum swing towards focusing on buying committees. In enterprise accounts, you’re selling multiple solutions to different stakeholders. The master data set becomes essential to identify patterns and behaviors among these groups.
The Role of Machine Learning and AI
At CaliberMind, we’re applying machine learning to help our customers navigate their buyer journeys better. Doug elaborates, saying “If I could add a new stage, it’s engagement plus machine learning. And I don’t think that’s years away; it’s months.”
Misha adds that we’re seeing a trend where companies are using scoring models in parallel with attribution. These organizations see value in a unified view that combines different data sources and analytical models.
The Importance of a Marketing Data Warehouse
Since marketers are inundated with data from all these channels, the first step is to consolidate it all in one place. You need a marketing data warehouse. If you’re not moving towards one, you need to start now. This allows you to create a consolidated data model that can leverage different types of analytics to pull out insights about where your customers are in their journey.
Turning Insights into Action
Doug points out that there is still a big gap in how data is actioned. “There’s so much value in helping marketers understand their data, but there’s a yawning gap between that understanding and the execution piece. The way marketers have filled that gap is by relying on what worked in the past,” he elaborates. MDEs should ultimately feed activation or orchestration models.
Misha added that you need to be able to answer behavioral questions: Where is the buyer in their engagement with my brand? What have they already seen? What’s the next best action? You need to see all this across different channels, be it paid ads or emails, in the same view.
If you can manage a multi-channel model well, your aggregate ROI should be a lot higher. The MDE is going to increase your ROI over time because you’re able to understand and manage those subtleties in a multi-channel model to become more efficient and increase conversion rates at the end of the day.
Final Thoughts and Looking Ahead
The classic funnel is giving way to more complex, engagement-driven models. Marketing Decision Engines are not just a luxury but a necessity for navigating this complexity, especially as organizations scale.
Thank you for joining us on this week’s Funnel Lab Fridays! Next week, we’ll have Jordan Crawford, an advisor to a company called Clay, to talk about the potential of AI to disrupt the funnel.
