Beyond Job Titles: Identifying B2B Buyers for ABM Programs

In the world of B2B marketing, you’ll be surprised how often people make the mistake of targeting based on titles. It doesn’t matter if you’re running display programs or social campaigns, the first crucial step is to find the right individuals. But using job titles alone won’t scratch that itch.

As SiriusDecisions explained when it revealed its recently-updated Demand Unit Waterfall, it’s up to marketing and sales teams to think, plan, execute, and measure againstBuying GroupsandDemand Centersat each stage.

Salespeople working to penetrate big accounts are always on the lookout for the stakeholders and influencers who make up a Buying Group. Considering there are close to seven decision makers involved in an average B2B deal, and those separate influencers can be any of seven different types, identifying these people becomes paramount in cross-channel ABM programs, since you need to engage each and every one of them individually.

The result, when done right? Higher returns on display media spends for both social media targeting and programmatic display targeting, because you’ve done a better job of identifying these buyers.

How to do it right? Today, that entails using a machine learning/A.I. solution capable of analyzing massive amounts of data from multiple sources to identify the actual buyers you want to reach. Because if you rely on job titles for your ABM targeting, you’re going to find yourself sorely disappointed.

Why titles may steer you wrong

Relying on a person’s job title isn’t enough to identify these buyers, for a variety of reasons.

  • Job titles often don’t summarize the actual responsibilities and relationships entailed in a position. This means that keyword searching for B2B outreach is becoming pointless. Titles don’t tell the whole story, so marketers spend money to target the wrong people and miss out on intended leads.
  • Titles aren’t precise enough. A few words may be sufficient to indicate a departmental link within an organization, but it’s not enough to really capture one’s role. “Director of Operations”, for instance, is a vague title that could be meaningful to colleagues, yet mean something completely different at another organization.
  • Many different titles exist, with very little standardization. Advertising database software to “database engineers” seems like a simple way to target an audience, yet whether or not they play a part in the buying process is a mystery.
  • Titles are too specific. Many job titles cater specifically to a vertical, despite being publicly searchable. They’ll include terms familiar to insiders, so titles end up full of jargon and buzzwords that are like a foreign tongue to outsiders.
  • Identical or closely-related positions can have different titles (sometimes even within the same enterprise).
  • Off-center, inventive job titles further muddy the waters. Trendy titles are meant to ride the wave of public fascination with a hot sector or industry. Job searching platforms and online job applications are on the rise, so a title can be an attempt to stand out from the thousands of other applicants. Some organizations want to showcase their uniqueness by inventing their own job titles.

So, what does all this mean? It means that titles can’t be trusted. Identifying the right B2B targets requires a deep-dive that goes far beyond job titles.

A.I. to the rescue

A machine learning solution penetrates the miasma of synthetic titles and conflicting name-ology, because it collects data from multiple sources, including social media and the web, to infer the true meaning behind job titles and uncover the real roles their owners play within targeted accounts.

A profile headline on a social network, for instance, can be a better indicator than a title of what a person really does in their job. It gives them an outlet to diverge from traditional labels and express their job in numerous ways, as plainly or colorfully, excessively or tersely as desired.

Let’s take a look at one quirky real-case example that’s a favorite of ours.

At MarianaIQ, analysis found that Code Ninjas were typically junior-to-mid-level developers working on UIs or applications, while Code Monkeys tended to be more senior engineers employed at larger companies.

What’s the nuance behind these names? Young app devs see themselves as being agile and adept in many platforms and languages, while their older peers at those big corporations work in more constrained (and seemingly repetitive) programming environments. The distinction is certainly relevant to marketers.

How does machine learning dive deeper?

Employing a machine learning platform (like the one we offer at MarianaIQ), a marketer enters the title they want to target, as shown in the images above. Persona-building software then figures out semantically similar titles, sparing the dull work of thinking up all the titles that could be related.

Is it a keyword search of different titles? No, it goes deeper – and here’s an example how.

Say you’re searching for the VP of IT. Inputting this into the “title” gives you these results:

  • VP of IT
  • VP Information Systems
  • Senior Manager IT
  • Senior IT Manager
  • Director IT
  • CIO
  • VP IT
  • Senior Director IT
  • IT Director
  • Director IT Infrastructure
  • IT Management Consultant

Now you can compare these to a similar-sounding yet very functionally different job title: VP of IT Sales. Here, the results are:

  • VP of IT Sales
  • VP Strategic Accounts
  • Client Executive
  • Strategic Account Director
  • Senior Strategic Account Manager
  • Strategic Account Manager
  • Client Services Executive
  • Senior Business Development Executive
  • Senior Business Development Manager
  • Business Development Executive
  • Business Development Consultant

Clearly, the results are very different, despite the similar job titles. This is what machine learning has enabled: building personas that are actually accurate. The platform you use may take this even further, allowing you to search for interests under a certain title.

Not only can you target people based on their titles, these titles and interests get expanded in a semantic space.  For ABM, uncovering this kind of insight about what people actually do, not what their job titles say they do, absolutely requires A.I. if lead generation and prospect engagement is going to work at scale.

In actual ABM application

In real-world use, machine learning is already paying very real dividends for marketers engaged in ABM:

  • They’re able to use it directly for internal ABM programs utilizing non-paid channels, such as email.
  • They’re realizing higher value and returns from social targeting programs, capturing not only more leads but leads of higher quality: those elusive “Buyer Group” stakeholders
  • In programmatic display advertising, Kwanzoo leverages the capabilities delivered by MarianaIQ’s Deep Learning platform to optimize targeting.

At the end of the day, ABM can only be effective at scale if B2B marketers are able to look past job titles and build 360º profiles of the key decision makers they’ve got to engage. Machine learning is the solution that’s already making that happen.

About Venkat Nagaswamy

As co-founder and CEO of MarianaIQ, Venkat Nagaswamy brings a long and diverse background in high technology to bear on applying artificial intelligence and Deep Learning to help marketers make account-based marketing (ABM) an at-scale reality. “Big Kat,” as he was nicknamed by friends and colleagues, has led teams in creating analytics, technology and business development solutions at McKinsey, Juniper Networks and GE Plastics, among others. He’s worked in enterprise and digital consumer hardware, SaaS, corporate and business unit strategy, market entry strategy, product development, marketing planning and more, allowing him to understand martech challenges from both the CTO and CMO’s point of view. A proud graduate of the University of Michigan and the Georgia Institute of Technology, he holds an MBA and a Master’s in Aerospace Engineering.

 

 

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