Note: This article was originally published on Demand Gen Report’s Demanding Views
Three questions encompass everything a C-suite or board wants to know when it checks in with its Marketing organization:
- How are we performing?
- How much is it costing us?
- Is it getting better or worse?
Sounds simple, right? You might be shaking your head right now, having full knowledge of the scramble and stress associated with coming to that table with clear-cut answers to those questions.
But doesn’t the B2B attribution technology out there give us everything we need to do this simply? If multi-touch attribution allows us to calculate the value of our marketing efforts, why are we still scrambling?
The answer is that traditional attribution models aren’t cutting it.
There’s a new way of looking at attribution, and it’s a vital piece to understanding your Sales and Marketing ROI. It’s known as “Chain-Based Attribution” (CBA). While you may have never heard of it yet, you’ll want to now.
First, what it is. Chain-based Attribution utilizes AI, machine learning, and natural language processing — we are amidst the rise of the machines, after all. It works backward from Closed-Won / Closed-Lost opportunities, analyzing the complete chain of events to compute the contribution of each marketing channel to revenue.
That’s the simple explanation. Before I get into the complex one, let’s touch on why we need Attribution 3.0 in the first place.
Multi-Touch Attribution Hasn’t Delivered on it’s Promise
Before you jump all over this, hear me out…
When early B2B attribution vendors introduced the concept of multi-touch attribution (MTA), it was game-changing. It brought about a major shift in how we think about customer engagement, and we haven’t looked back.
So how do we think about it? MTA ushered in a new method that called for assigning value to touch points across the customer journey. Once those values were assigned and you watched a few customers move through the buying journey, you could look across that concerning the efforts you’ve created (calls, events, content, etc) and say “Hey! Nine out of 10 of our customers watched this demo” and conclude that said demo must be super great.
Where MTA fails us in its complexity and its inherent bias. Data culled from MTA quickly becomes siloed and complex to manage. Our tech stacks can’t digest and display the information in a way that is meaningful to our business.
But more detrimental is MTA’s misguided approach and the inherent bias that proves counterproductive to determining the actual truth around which of our marketing efforts are most valuable.
You see, we’ve been a channel-focused bunch — a worldview stemming from the early digital days when we were all just trying to figure out, very broadly, how to use various channels in the most advantageous ways. That evolved into a tech stack of solutions catering specifically to individual channels.
So we divided up a chain of touch points and arbitrarily assigned each a value to determine which channel, program, or campaign should get “credit,” and then shoehorned that multi-channel data into single-channel reports, tools, and execution.
See the bias there? WE decide the value of each thing. Before we even put a thing out there to be found and digested, we say “This is big. We worked hard on this event/white paper/email, and it aligns with our customer journey. So let’s make this count for a lot if someone touches it — especially if it’s the first touch.”
It’s a backward approach that will net you biased information about what your customers care about.
What is a Machine Learning Attribution Model?
A Machine Learning Attribution Model is a sophisticated approach to determining the impact of various marketing channels, aka Chain-Based Attribution. Unlike traditional attribution models, which often rely on preset rules (like first-click or last-click attribution), machine learning models utilize complex algorithms to analyze vast amounts of data and identify patterns in customer behavior. These models assess how different marketing touchpoints contribute to the conversion process, assigning value to each interaction based on its role in driving the desired outcome.
Key Features:
- Data-Driven Insights: Machine learning models process extensive data from various sources, providing more accurate and nuanced insights into customer journeys with data-driven marketing.
- Adaptive Learning: These models continuously learn and adapt as they process new data, improving their accuracy and relevance over time.
- Granular Analysis: Machine learning allows for a more detailed analysis of the interplay between different marketing channels, uncovering subtle patterns and interactions.
Understanding the importance of a Machine Learning Attribution Model is crucial for modern marketing strategies. By accurately identifying the most effective marketing channels, businesses can allocate budgets more efficiently. This enhanced understanding allows companies to tailor their marketing efforts to better meet customer needs and preferences.
Precise attribution enables marketers to fine-tune campaigns in real-time, maximizing impact and minimizing wasted spend, leading to more effective and cost-efficient strategies. Additionally, businesses gain a comprehensive view of their marketing effectiveness, enabling data-driven decision-making that reduces guesswork and increases the likelihood of achieving desired outcomes. Companies leveraging these models can outperform competitors by making more informed and strategic marketing choices.
A New and Improved Approach to Attribution
Chain-based Attribution is built on top of a model (Markov) rooted in probability and statistics. Instead of reporting on “credit,” as in a traditional MTA approach, it reports on contribution to revenue (or pipeline). And instead of being output-based (looking at clicks and downloads), CBA is outcome-based (looking at engagements tied to revenue). The model observes what your accounts do before converting, and what they do when they don’t convert, to measure what’s important.
Another cool aspect of CBA and the Markov model is The Removal Effect. If you were to remove a channel or campaign, what would that effect be on your revenue outcome? Imagine being able to understand the revenue impact of a large event with a built-in report that takes one minute to generate.
CBA also attributes both Marketing and Sales touch points for full-funnel visibility, from Anonymous to Closed-Won and beyond.
Oh, and it’s smart. Because CBA utilizes machine learning, with more data, your models continue to refine and improve over time.
CBA lets you predict your sales opportunity conversion with a much higher level of accuracy than any other attribution model. It’s the stuff marketing dreams are made of.
And now that you’ve heard it, you can’t go back. So keep on going. See if a Chain-Based Attribution approach is what you need to bring in more revenue and make better marketing decisions.
