2017 Projections: Moving Beyond the ABM Discussion
After a long weekend of celebrations with my loved ones, I finally sat down and got the chance to reflect on the year that’s passed. 2016 was my first year as a CMO, and I was absorbing everything and anything that I could about what people were talking about.
Even before I became CMO, I noticed it: the conversation in 2016 was so dominated by ABM. Countless blog posts, webinars, articles, and talks at major conferences popped up throughout the year – every single marketer was chiming in with their take.
At the end of 2016, two things have become crystal clear: ABM is here to stay, because ABM has always been here. All of the discussion, commentary, insight, and planning made everybody realize that marketing departments may have strayed away from ABM, but sales teams never did. What really happened over the past year was that marketers re-affirmed their commitment to alignment with sales, and ABM became a relevant term again. So now, that conversation is over – in 2017, I personally don’t want to beat the dead horse anymore.
What I want to talk about in 2017 is machine learning and predictive marketing.
Predictive analytics was a natural side-topic to ABM over the course of the past year because in the effort to tighten up their ABM strategies, companies across so many different verticals invested in various types of predictive tools. Predictive analytics uses various internal and third party data to identify when a target account is researching topics related to your business. Predictive marketing allows you to more accurately market to your accounts by identifying who is already in market, what they are searching for, and where the research is taking place. They wanted to know how to get the best ROI on their predictive investments – and this brought about a whole new set of challenges for 2017.
The first challenge is: how do you continuously make smarter and smarter predictions about who your next customer will be? The second is: how do you ensure that your sales and marketing teams are responding to those predictions in a way that enhances the customer’s experience and accelerates the capture of revenue?
So as for the B2B conversation of 2017, my predictions are twofold. To address the first question, the key to machine learning is the inputs that feed the algorithm. Sales and marketing departments will recognize the need to increase the number of inputs beyond “intent data” and CRM data. The smartest, most advanced sales and marketing leaders will recognize that machine learning and predictive analytics are not just a part of their marketing mix – they are the North Star of the entire operation. And those departments will begin selecting predictive vendors that not only offer solutions designed to be ever-adjusting their algorithms for additional inputs, but who can also execute on their behalf when needed knowing that this closed system of prediction and execution provides the natural feedback loop necessary to consistently make smarter predictions about that next customer.
Our early experiences with our clients show that the immediacy of predictive’s output does not resonate through the organization as it should. Clients treat their predictive dashboards as just another data source and not their road maps – they haven’t re-organized their departments and their processes and their systems to be able to jump on an opportunity from both a sales and a marketing perspective when an alert is triggered that says a prospect is ready to buy. To fully capture the benefit that this powerful technology provides requires a complete re-thinking of the structure of the sales and marketing organization, and this is the other prong of what I think will be the discussion in 2017.
If you’re interested in discussing this with me further, you can contact me at firstname.lastname@example.org