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How To Use Data And Science To Ensure Account-Based Marketing Success

  • Written by Brandon Redlinger, Engagio
  • Published in Demanding Views

AAEAAQAAAAAAAAliAAAAJGVhNmZkZTgzLTVkMTAtNDg3Yi1iMDk5LTA3NTEwOWI0NzkwZA 1For 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 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 Everything (ABE). It’s more attainable now than ever before.

The Rise Of Data

The most important step in the ABE process is choosing which target accounts will receive the focus of our resources, and to do this correctly, we must use data. Not intuition, not random assignment. Data.

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

1) Firmographic Information – Which company characteristics best predict a successful sales process? The answer will likely take the form of:

  • Company size
  • Number of employees
  • Industry
  • Growth
  • Number of locations
  • and more

You can find this information from a variety of sources, including annual reports, LinkedIn and third party data vendors, such as Dun & Bradstreet and Reachforce. This is an excellent starting point for your account selection process, but it’s only the beginning.

2) Technographic Information – What complementary technologies pair well with your solution, and in contrast, which technologies make an investment less likely?

Source this data from desk research looking at forums, job boards, social media and other indications that an organization is utilizing certain technology. To bring efficiencies here, tap into the knowledge of competitive intelligence firms such as HG Data, or web scraping firms like Datanyze and BuiltWith.

3) Engagement Data – How engaged your company is with this account right now?

When faced with a long list of potential target accounts, you’ve got to start somewhere, and your quickest path to traction with ABE will be with those companies where existing activity indicates a substantial opportunity.

Your current level of engagement will include:

  • Past sales into the company
  • Rep activity levels
  • Account engagement by persona
  • Current coverage of key decision makers
  • Existing relationships and connections into the account
  • Executive entry points

This information is found from a variety of sources, including:

  • Your CRM data
  • Web analytics
  • Marketing automation reports
  • LinkedIn
  • Engagio
  • Sales rep activity
  • Executive input

This layer of information is not enough when considered alone. Instead, use intent data to prioritize from a longer list, rather than to supply your entire list.

4) Intent Data – Signs that a target account is in the market right now for solutions like yours.

Intent data uses the behavior of contacts at target accounts to indicate a more urgent qualification and fit. This could include any behavioral data that indicates priority, including:

  • Topics people at this company are researching on third-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.

The Power Of Intent Data And Predicting Success

Firmographic and technographic data are both static information formats that help to concentrate your efforts on 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.

Sixty-seven percent 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, none 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.

“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, 6sense

“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

Companies can use this intent data to score target accounts manually, or as part of predictive scoring to identify Marketing Qualified Accounts (MQA). 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.

See a simple example in this post.

You may decide to weigh intent data higher than other inputs, for example, company size or which CRM the company uses.

You can find more detailed information and insights about using intent data in your strategy by downloading The Clear and Complete Guide to Account Based Marketing.

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


Brandon Redlinger is the Director of Growth at Engagio, an Account-Based Marketing and Sales platform that enables teams to measure account engagement and orchestrate human connections at scale. He is passionate about the intersection between tech and psychology, especially as it applies to growing businesses. You can follow him on Twitter @brandon_lee_09 or connect with him on LinkedIn.