This article was originally posted on MarTech Advisor.
Artificial Intelligence is transforming B2B marketing, much as it is impacting so many other industries. But with the application of AI technology still relatively young, and the potential for advances so huge, the best may be yet to come.
Yet as the recent spat between Facebook founder Mark Zuckerman and fellow tech entrepreneur Elon Musk illustrates, some of the leading visionaries driving the next generation of AI applications can’t even agree on whether it’s a force for good, or a mortal danger to mankind. So predicting where we’ll be with AI in 10 years’ time — or even two or three — is a risky business.
Fortunately, the applications of AI to B2B marketing are somewhat more down-to-earth. We’re not saving mankind here, but we aren’t dancing with the apocalypse either.
And, while there really is no telling what incredible possibilities may emerge in the coming years, there are a few nascent developments, advances and trends already taking place, which B2B businesses of all kinds would do well to look out for.
From making sense of the noise, to shutting it out altogether
The central benefit of AI for B2B marketers is that it gives them the power to see through their impenetrable sea of data, by being able to process volumes of data that a human couldn’t get through in their lifetime — in a matter of minutes. This empowers marketers to gain an intimate understanding of who their customers and prospects are, and how to engage with them.
Currently, we’re at the beginning of the AI adoption phase. But as these technologies become more widely used — and more importantly, once customers become educated as to the true potential of true AI modeling — their expectations will become much more sophisticated.
And rightly so. The current mystique surrounding AI has allowed some vendors to hide behind partial solutions or “black box” platforms which don’t enable customers to unlock the full potential within their data. Such solutions certainly provide some benefits, giving glimpses into the intelligence locked within your marketing database, but not the full picture.
Once the market realizes that these AI applications only take them half way, and that instead of simply being able to make some sense of the noise, they could potentially shut out the noise altogether and gain full control of their data — well, they won’t want to stop half-way anymore.
As Leadspace CEO Doug Bewsher noted in a recent interview, this is the direction AI is going more generally:
“I think that the change in user interactions is going to fundamentally change our way of interacting with one another – both in a personal and business sense. We will find it weird in 10 years’ time that people walked along the street looking at their phones all the time, not letting an assistant talk to you or read the news, or have it overlaid in context.
“At the same time, we’ll also find it weird that we spent so much time looking at Gmail, Salesforce, or Hubspot interfaces on a big screen. Instead, we’ll be presented with the pertinent information at the right time – with a recommendation on what to do.
“I think this is why you see such emphasis on large technology platforms trying to build out the “AI’ side of their platforms, as the specific form of interface will increasingly matter less – whether it is Alexa or Slack that you get your CRM data from – and it will need to be everywhere.”
Deep Learning technology already exists, but it is still relatively new and given its greater complexity than other forms of “machine learning” hasn’t seen nearly as much practical implementation.
You can read a very useful article explaining what Deep Learning is here. Essentially, it’s a form of AI which mimics the human thought process, and enables machines to learn “rules” independently by analyzing the data themselves, instead of being fed rules by humans.
While Musk thinks “Terminator” and Zuckerberg thinks… uh… “Robocop” (is that technically AI? Never mind…), for B2B marketers — in fact for anyone in demand generation — AI simply presents an incredible opportunity to use their data in ever more effective ways.
Take the example of predictive scoring — an application of AI that’s already being widely used by B2B marketers.
Let’s say that a common thread among a company’s big-deal accounts is that they’re all using a particular technology or set of technologies, or that they all have a certain sized sales team. No human marketer has the capacity to pick through all the data to connect the dots and recognize that trend — it’s hard enough keeping track of all the “top-level” segments we actually know to look out for, like industry, company size, geographic location, etc.
Even a more basic machine-learning AI model would similarly miss this subtle pattern, as no one would know to instruct it to look for that particular pattern/rule. But a Deep Learning predictive model could pick this pattern up by itself. This would provide a highly valuable insight to inform your lead or account scoring, as well as future marketing strategies, which otherwise would have been missed.
There are other, more ambitious potential applications of Deep Learning as well, beyond enhancing existing AI technologies like predictive modeling. One such application is what we call “Look-alike Modeling” — using Deep Learning to not only understand who your ideal customers are, but to then actively delve into your white space and find more just like them.
Using advanced AI to prospect for net-new customers in this way is yet another step towards the ultimate goal of AI, which is…
Automation: The future of data management
This is one of the central, strategic goals marketing organizations should be aiming for with AI: automating the extremely tedious, humanly-impossible task of data management from beginning to end.
In fact, a recent survey of senior B2B marketers revealed that “data management” was the aspect of their job they most disliked. Of course, this makes sense: on the one hand it’s a task that can never be completed optimally by a human being (or even more than one). But on the other hand, it needs to be done — so it ends up sucking enormous amounts of time from the average marketer’s day which could be used creating original campaigns, or executing other important tasks. And still the results are often unsatisfactory!
AI holds the potential to free Marketing (and Sales) from the constraints of data management and enable them to focus on doing the central aspects of their job better.
This, incidentally, relates back to the Zuckerberg vs. Musk debate — one that has actually been raging over AI for many years. There have always been prophets of doom predicting that Artificial Intelligence will eventually take all of our jobs and trigger mass unemployment. What these naysayers don’t realize is that on the ground AI isn’t really replacing that many human jobs; rather, it’s helping automate tasks that humans just aren’t as well suited for, and thereby helping us to actually excel more at our jobs.
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