Wayne Lockwood: How the Rise of Data is Fueling ABM Success

We live in a business world that is dominated by data. In fact, data is one of the most valuable commodities today. It forms the basis of any successful marketing or ad campaign, is driving some of the biggest tech acquisitions, and is literally fueling the rise of Artificial Intelligence—a technological revolution that’s already changing the face of every industry it touches. What’s more, it’s readily accessible: with relative ease, companies can acquire incredibly precise data on their customers and prospects (although this overabundance of data comes with its own challenges too).

The rise of what some are calling the “Data Economy” also comes at an opportune moment for B2B marketing and sales in particular, as they move increasingly towards account-based frameworks. Of course, ABM has itself been enabled by the rise of “Big Data” and all the exciting opportunities that come with it. Chicken vs. egg questions aside, B2B marketers’ increased ability to obtain and use unprecedented levels of data presents them with a perfect opportunity to excel at ABM.

In this blog, I’ll cover how data plays a significant role in the success of account-based marketing (ABM) and what that means for marketers.

ABM is Personal

First, there’s the fundamentally targeted nature of ABM. The term account-based marketing can be somewhat misleading: you’re still engaging with and selling to leads within your target accounts, so it’s no less personal in that regard. But ABM actually requires an even greater measure of targeting and personalization than a purely lead-led approach.

To quote Leadspace VP Product and Partnerships Travis Kaufman from the recently-published The State of Account-Based Marketing report:

“To succeed at ABM, you must understand the company characteristics as well as the characteristics of the individuals within those organizations. Having this understanding allows you to align your teams around critical accounts that need focus and build programs designed to engage the people who influence the purchase decision.”

Specifically, to actually engage with an account, you need to:

Compile a list of qualified, named accounts to target. This is itself a highly data-driven process, which will require constant monitoring and adjustment as you gradually refine your profile of your ideal account by learning from won/lost deals.
Identify the key influencers with buying power within each account.
Identify other peripheral influencers who could potentially sway a decision. Stakeholders like HR, Finance or IT might not actually sign off on the purchase, but may need to be consulted, and could be required later to facilitate its implementation.
Compete for these people’s attention with personalized content, in a content-overloaded environment. This requires understanding their interests, needs, pain-points, and other important factors affecting their propensity to buy. This may include the compatibility of their existing technology stack as well as other details like the level of interest or engagement to ensure the content you’re serving is relevant, and what platforms are best to reach them on.
Have an understanding of the site-level context of any given account: You need to know whether the company you’re targeting is actually an “account” in its own right, or if it’s actually a branch (e.g., subsidiary, local branch, franchise) of a larger parent account. If the latter, which site should you be targeting, and is anyone already effectively selling into this account at a different site level?

To do all of this you need highly granular data on both the individual and account level. You then need to combine account- and individual-level data to gain a holistic view of the entire account.

Keeping Up=More Data Than Ever

ABM requires building an intricate network of intersecting intelligence on both accounts and leads instead of keeping track of a single lead. Apart from being a challenge in itself, ABM practitioners need to be plugged into the constant changes and developments in data across both the account and individual level:

On the account level: If a company enters/exits a buying cycle, or alters the relevant budget, or changes its technology stack, or makes a significant new hire, or moves buying power/job responsibilities among its leadership, etc.

On the individual level: Simultaneously, you need to watch if any of those influencers you are engaging with or trying to reach are getting promoted, moving department, changing job function or leaving the company.

Again, this requires a lot of granular data, in real-time—far beyond the basics like job titles, company size, industry, and so on. Today, this kind of highly detailed personal data is available, and marketers who make do with only basic, superficial data sets like job title are at real risk of being outmaneuvered.

Personalize Your AdTech

Among the most exciting developments in B2B marketing, execution is the evolution of personal ad targeting.

Until recently, unlike their counterparts in the consumer industry, B2B marketers could only target company IPs for online ad campaigns, as opposed to specific individuals. Unsurprisingly, this basic method generated meager returns: if you’re blasting content at an entire company—from the CEO down to every junior intern—the vast majority of those it reaches are totally irrelevant.

But B2B AdTech is catching up, finally. We see platforms tap into the oceans of big data potential to enable personal targeting that’s on-par with the B2C equivalent. That’s ideal for enabling account penetration, by targeting the right people inside your target accounts with relevant content consistently, wherever they’re browsing.

ABM can’t be ignored right now. It’s no surprise that the majority of B2B companies, of all sizes, are adding ABM as a strategy within their marketing plan. Have you incorporated ABM yet? What successes have you had with it? Tell me about your experiences in the comments.

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