For years, targeting key accounts has been a cornerstone of effective B2B growth.

The ITSMA pioneered the concept of account-based strategies over a decade ago, reporting that it “delivers the highest Return on Investment of any B2B marketing strategy or tactic. Period.”

Period.

So what is with the sudden resurgence of attention and excitement (and buzz!) about this industry term? Today, over 90% of B2B organizations believe Account-Based Marketing (ABM) is a “must-have” tactic, according to SiriusDecisions.

One reason is the immense amount of data that is now available to us to make account-based strategies more efficient.

When it comes to at all information on the internet, according to IBM, every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. Within this massive amount of information, there is more insight about our buyers available to us than ever before.

Most importantly, it’s not that there’s a lot of data available to us – there’s more specific information available about individuals at target accounts that allows us to be highly targeted, highly relevant, and highly effective in our messaging. The Sales Intelligence market has grown enormously in both sophistication and coverage in recent years.

No wonder we’re all so excited about the promise of Account-Based Marketing. It’s more attainable now than ever before.

The Rise of Intent Data

There are four key types of data used to create target account lists for Account-Based Marketing: firmographic, technographic, intent, and engagement data.

• Firmographics – what company characteristics best predict a successful sales process?
• Technographics – what technologies do they currently use or are looking to invest in?
• Intent Data – is the company showing signs that they’re in the market right now for solutions like yours?
• Engagement Data – How engaged is your company with this account right now?

Firmographic and technographic data are both static information formats that help to concentrate your efforts within the massive amount of potential accounts. Intent and engagement data sets, on the other hand, use explicit behavior to indicate a more urgent qualification and fit, and help to prioritize accounts.

67% of the buyer journey takes place digitally, according to SiriusDecisions. Intent data captures this digital research and activity from individuals at your target accounts. For that reason, one of the most important elements of identifying Marketing Qualified Accounts is understanding the intent of contacts at key target accounts.

Note: To tie the behavior of an individual, it’s important to using Lead to Account (L2A) matching, to analyze each lead and identify which Account he or she should be part of. That data is then used for analytics, routing, scoring, and so on.

Who’s In-market, Right Now?

Intent data can uncover signs that a target account is in the market right now for solutions like yours.

“If you have an account in which for the past few weeks, multiple contacts have been researching a new phone system and downloading white papers about new phone systems, that not only tells you if they meet your buyer criteria, but it tells you they’re in the market now.”
– Alison Murdock, Verto Analytics

This can include any behavioral data that indicates the right activity, including:

• Topics people at this company are researching on 3^rd party sites
• Participation in forums
• Content downloads
• Ad clicks

This data is sourced from forums, job boards, and similar sources. In addition, intent vendors such as Bombora, MRP, and The Big Willow can deliver a layer of insight to maximize your findings, depending on your specific solution and targeting criteria.

“All enterprise IT vendors sell hard to the same 5,000 companies.
So intent becomes key: get to them when they’re actively thinking of your kind of solutions.” 

-Henry Schuck, 
DiscoverOrg 


Using Intent Data to Define Marketing Qualified Accounts (MQA)

Companies can use this intent data to score target accounts manually, or as part of predictive scoring to identify Marketing Qualified Accounts. When manually scoring, applying proper methodology will help to ensure your process is more rigorous. Begin with the entire market or territory, then score each account according to the dimensions most relevant to your situation.

However, not all engagement is created equal, which is you need the ability to measure specific types of engagement to operationalize the account funnel. In particular:

• People –  Knowing exactly who (title, job level, department, etc.) has engaged with your company is a critically important. Would you treat an intern the same way you would treat an executive who has engaged with you? The whole idea of demand units is tracking engagement for different groups and personas.
• Actions – Visibility into specific actions taken by a target account is another key component. The action of requesting a demo deserves more attention than downloading a top-of-funnel whitepaper.
• Timeframe – “When” the account engagement takes place can be just as important as “how” much time is involved. You need to consider the specific timeframe engagement has happened at an account.
• Exclusions – Knowing who not to go after is just as important as knowing who to go after. Defining rules and filters for types of engagement that are excluded eliminate false positives, which in turn save your team time and energy. For example, partners or companies that don’t fit your account prospect criteria.

“A key part of the new account-based funnels is the ability to prioritize demand by scoring account engagement,”
-Jon Miller, Engagio.

Have you successfully used intent data? Or are you finding more success with other data types?