AI

Artificial intelligence is changing the rules of account identification

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 intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. () 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 -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, 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. […]

  1. SwissCognitive

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  2. HealthIT Policy

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    https://t.co/…

  3. Simon Pitt

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  4. Dalith Steiger

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  5. Jon Carroll

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  6. Andy Fitze

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  7. Saurabh Chaturvedi

    RT @andy_fitze: Artificial intelligence is changing the rules of account …
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    https://t.co/R5tz…

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