Artificial intelligence is changing the rules of account identification
Columnist Peter Isaacson takes a look at how AI-powered intent data is transforming the way B2B marketers approach their Account-Based Marketing programs.
We’re on the verge of a whole new world when it comes to Account-Based Marketing (ABM). Just when it seemed like marketers were getting the hang of ABM, advancements in technology are flipping traditional account-based tactics on their head.
In the past year or so, we’ve seen artificial intelligence (AI) technologies swoop in and really transform specific parts of the strategy, from website personalization and digital advertising to sales enablement. Now, we’re seeing these technologies tackle even more fundamental ABM steps, including account identification.
The current model of account identification
Today’s B2B marketers often build their target account lists in one of three ways:
- using predictive vendors that often combine lookalike modeling and some basic intent data;
- taking a data-based approach by analyzing a current list of customers, vertical penetration, company size and so on; or
- adopting accounts that your sales and marketing teams are already targeting through named accounts or vertical markets.
These three approaches have one thing in common: They’re static lists of accounts that don’t change very much over time.
While these methods of account identification have delivered tremendous value for marketers, they’ve also raised some important questions, including: How often should that static list be refreshed and reprioritized? And more importantly, how do you align sales and marketing resources when you have new accounts coming in all the time?
Generally, there are always new accounts showing interest. And without coordinated efforts from both sales and marketing early in the buying cycle, these accounts are likely to turn toward competitors to address their needs.
Ignoring accounts that are showing intent is like being an ostrich with your head in the sand. Instead of ignoring these accounts, we need to chase them and dynamically incorporate them into an account list.
Moving to a new, dynamic model
The building blocks of a more fluid target account list already exist today, and they’re built on AI-powered intent data. Intent data gives B2B marketers a window into the behavior of their accounts, whether they’re looking up a relevant topic, researching the space, learning more about a competitor or talking about the industry.
As companies start to show higher levels of intent, marketers can immediately prioritize and align sales and marketing resources to engage and convert them. On the flip side, if a target account’s intent level decreases, they can easily move the account into a nurture stream and advise sales to follow up at a later date.
With an evergreen, dynamic list, marketers no longer have to worry about missing out on accounts showing interest in their company and solutions. Instead, they can be proactive and reach buyers early on in the buying cycle with relevant, engaging messages.
But the key to really incorporating this type of dynamic list into your ABM strategy is automation.
With AI technology, marketers can incorporate audiences showing initial signs of intent and automatically trigger advertising campaigns or deliver personalized messaging to start those relevant conversations earlier in the buying cycle. For example, if a specific company is researching one of your competitors, you can automatically trigger an advertising campaign that delivers messaging about your product’s benefits vs. your competitors’.
With this type of insight-fueled automation, marketers are no longer confined to a specific list of accounts. Instead, they can focus on delivering relevant messaging and enabling sales with the tools they need to move the conversation forward and help drive revenue.
Artificial intelligence is changing the way B2B marketers run ABM programs, and now AI-powered intent data is paving the way for a whole new model of account selection. We now have the technology to move from a static model to a dynamic model of ABM, allowing us to continually take action and push through the buying cycle.
As more companies start leaning into these new technologies, it will change not just the way we think about targeting accounts, but how we think about ABM in general.