The 4 Types of Data You Need to Identify Accounts for Your ABM Program

Across many ancient philosophies and cultures, it’s believed that the universe is formed by the four essential elements: earth, air, fire, and water. These four elements operate together in harmony to bring a balance to nature.

There are also 4 types of data that are essential for building your Account Based Marketing foundation: firmographic, technographic, intent, and engagement.

Choosing Your List for Account Based Marketing

The most important step in the 6-step process for Account Based Marketing is choosing which target accounts will receive the focus of our resources. To do this correctly, we need the right data as your foundation. For the most successful program, and to strike a harmonious balance in ABM, take advantage of each of them.

The kind of data inputs will vary for your organization depending on many factors, but the process will likely include these a mix of these.

  1. Firmographic Information

Chances are, you already have a pretty good idea about the kinds of companies most likely to deliver the big deals. Ask yourself 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.

  1. Technographic information

At this stage of the account selection process, you’re looking to define which technologies your target accounts currently use, or are looking to invest in.

Globally, companies spend $3.54 trillion dollars in IT (according to 2016 numbers from Gartner), making it a key imperative to focus on only those organizations most likely to demonstrate a fit with your product.

Consider what complementary technologies pair well with your solution, and in contrast, which technologies make an investment less likely. For example, knowing that a company uses Marketo, Salesforce, or SAP might just make them a more attractive candidate for your solution.

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.

  1. Intent data

One of the key elements of a Marketing Qualified Account is understanding intent. Firmographic and technographic data are both static descriptors that decrease the total size of your audience and thereby concentrate your efforts. But, intent data uses the behavior of contacts at these target accounts to indicate a more urgent qualification and fit.

(This is where Lead-to-Account Matching is critical.)

Seek signs that a target account is in the market right now for solutions like yours. This could include any behavioral data that indicates priority, including:

  • Topics people at this company are researching on 3rd 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.

  1. Engagement data

While intent data can signify what buying activity an account is exhibiting elsewhere on the internet, engagement data seeks to identify 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 strong 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.

All – or nothing.

In fact, none of the four data types above are enough by themselves to formulate a sound account selection strategy. Just like the four elements, which work in tandem, these four data sources should be part of a holistic account selection strategy.

For more information on how to build your ABM strategy, download The Clear and Complete Guide to Account Based Marketing.

Do you use any other data sources?

Brandon Redlinger
Brandon Redlinger
Brandon Redlinger is the Head of Growth at Engagio, the 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.

4 Responses to “The 4 Types of Data You Need to Identify Accounts for Your ABM Program”

July 12, 2017 at 9:00 pm, Rick Vargas said:

You forgot – Predictive!!!

(This is the CAPGEMINI guy who signed you/Marketo when you were a $13M/year company).

Reply

July 13, 2017 at 12:37 am, Jon Miller said:

Rick, great to hear from you!
I think predictive is not it’s own type of data, rather it’s taking all these types of data and using them as part of an algorithm. Make sense?

Reply

July 26, 2017 at 7:16 am, Daisy Nosh said:

Great post..!!!

Loved reading it.
Thank you

Reply

January 16, 2019 at 7:06 am, infodepots said:

Well said about data and how to utilize it…..Thanks for sharing this great post

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