Turbocharge B2B Marketing Attribution with AI

I started my morning by downloading a research report from a software company’s website. Now, I’m their prospect. How will they know if I am a good or bad lead? Importantly, how will they know whether that marketing content is a useful tactic or not? That’s where B2B marketing attribution comes in. 

B2B marketing attribution is the process of assigning value to each marketing touch in a buying journey. Most companies are doing this already. Most are also using some method of weighting those touches. First touch. Last touch. Key tactic touches. And so on. 

The interesting thing is that once you open the door of weighted attribution, it closes behind you. There’s no turning back. You start with simplistic attribution models. After initial analysis, you realize simplistic models are, well, too simplistic. And so the model is improved. Touches are weighted. Then marketing budget decisions may be informed by attribution. Which tactics are paying off – let’s invest in those! But after initial executive questioning, the attribution method doesn’t hold up and more complexity is required. Once you start down the path of making attribution accurately reflect the buying journey, there’s no shortcut – you have to get into the details. 

One of the latest trends in b2b marketing analytics is using engagement in marketing attribution. Simply put, the longer a prospect engages with a touch, the more weight it receives for engagement. Makes sense, and the math is simple. Right?

Intellectually, every marketer agrees engagement weightings make sense. Practically, every marketer is struggling. What are the right engagement weightings? If I have an ebook with a read time of 4 minutes, and a prospect engages for 2 minutes, is the weighting 0.5? If that prospect reads the ebook as their first step vs. their 7th, does the engagement weighting change? It isn’t just a factor of time. It is meant to be a weighting of how much that engagement matters to making a purchase. It’s complex, to say the least. 

Most companies that pursue weighted engagement multi-touch attribution use their team to decide the weightings. But there’s a flaw. Human bias. You can eliminate that bias, and get far more accurate results, with machine learning and artificial intelligence. Specifically, using it to derive the weightings for engagement in a b2b multi-touch attribution model. Machine learning AI is made for this use case. And you have all the data you need. Prospect journeys. Tactic engagements. Sales outcomes. It just needs to be combined and curated so it can be used to train a machine learning model to derive engagement weightings. Once you have those weightings, your B2B marketing attribution model will become much more accurate. How much? We’ve seen some customers improve accuracy by over 120%. Their budgeting decisions improve. They identify the real top-performing tactics and invest in them. They understand the point of diminishing returns and pull back investments. Crucially, they cull ineffective tactics. In short, they reduce their marketing spend and at the same time improve marketing assisted revenue. 

Marketing leaders are very excited about the potential of AI to transform marketing. Most are looking at generative AI and its applicability to content creation. But there’s a bread and butter use case that is much, much easier to implement, will deliver ROI this calendar year, and has a direct impact on marketing ROI. B2B marketing attribution. 

So as I finish my morning coffee, I wonder if the B2B software company I just downloaded a paper from is going to attribute my engagement correctly. And you should wonder whether your b2b marketing attribution models are accurately reflecting engagement. One thing you can do is look into B2B marketing analytics case studies to learn what others are doing. Another idea would be to do a self-assessment of your current marketing capabilities. It’s worth looking into, and it will impact your marketing budget and ROI in 2024.