The Art and Science of Selecting Accounts for Your ABE Program
Are you left-brained or right-brained?
To answer this question we’ve got to understand how our brains work. You likely know the basics here. Our brain is split into two hemispheres, with each side controlling a different set of functions.
Generally, the left side of our brain dominates reading, writing, logic and mathematical computations. The right side of our brain is in charge of processing music, artistic senses, visual imagery and creativity.
Now, back to the question at hand. Are you left-brained or right-brained? The truth is, nobody is truly one or the other. Everything in nature strikes a balance. Our brains are no different.
The reason our brains work as well as they do (well… for most people anyway) is because of their natural asymmetry. It takes both sides of the brain working in tandem for us to function properly. When you wink your right eye, that’s actually the left side of your brain at work.
A Blend of Art and Science
When it comes to Account Based Everything, we can learn something from this balance in nature. Selecting your target accounts (the most important step in the process) is a mixture of art and science, both intuition and logic.
Effective account selection combines gut feel, historical performance, and sometimes predictive data science to come up with an “Ideal Customer Profile.”
The Role of Data in Account Selection
Data plays a critical role in this process. You’ll almost certainly look at four types of data inputs:
- 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?
All Together Now – How to Strike a Balance
These four data sources are critical inputs whether you’re compiling your account list manually or using a more automated approach using predictive analytics. Combining multiple tactics gives you the balance you need to make this critical decision.
Picking accounts manually
This is where gut feeling comes into play. Partner with sales teams to compile a list of target accounts, and combine them into a master list. This will not generally be wildly off base – who better knows your market than the people tasked with selling into it? Another benefit of involving sales in the process is the buy-in they’ll have in the eventual outcome.
Enhancing with data
Without adding a bit of analysis to this manually-decided list, you will likely miss some big opportunities, while assigning more value than appropriate to others. Consider enhancing your own insight about prospective accounts with data from third-party vendors. The Sales Intelligence market has grown enormously (in both sophistication and coverage) in recent years, making it easier than ever to find information about the companies you’re considering.
- Dun & Bradstreet (and their acquisition, NetProspex)
- com (Salesforce)
You can create a simple scoring model by weighing the inputs your team decides are most important (across the four types of data I mentioned earlier) and scoring each company. Here’s a very simple example, where Company A and Company D make the cut.
|Tier||Co. size (x2)||Have right CRM (x5)||Engagement (x1)||Score|
|Company A||10 (x2=20)||8 (x5=40)||10 (x1=10)||70|
|Company B||3 (x2=6)||1 (x5=5)||0 (x1=0)||11|
|Company C||10 (x2=20)||2 (x5=10)||5 (x1=5)||35|
|Company D||10 (x2=20)||10 (x5=50)||7 (x1=7)||77|
Leveraging Predictive Analytics
Taking a more predictive approach to building your target account list can out-perform manual selection and scoring. This is due, in part, to the reality that many factors (more than the attributes you can identify with gut feel and manual data analysis) contribute to a successful sale. Much of this information is invisible to your teams.
Automated and predictive techniques can build models that better predict the propensity of a given account to buy. Just as Netflix can predict which moves you’ll like based on the ones you already liked, predictive analytics chooses the companies most likely to buy by analyzing the ones who have already bought (or become opportunities.)
“The majority of ABM programs have a list of targeted accounts in the 500 to 2,000 range, so that’s still a lot of activity to track manually. Predictive is one thing that enables companies to scale their ABM efforts, something which was not possible even a few years ago.” – Megan Heuer, SiriusDecisions
So, Right-Brain or Left-Brain?
Your approach to Account Based Everything will be unique for your given organization. There is no one-size-fits-all target account list.
Take a note from nature, and develop the best possible list by combining these tactics above. Tap into the intuition of your right-brain with the capabilities of your left-brain logic (and some help from predictive capabilities.) Working in tandem, these techniques will supply an ample starting ground for ABE, and a constant flow of new accounts to keep your program going strong.
Which techniques have worked well for you?