Every year during annual planning, B2B sales leaders make a set of big decisions to figure out how to narrow down the world to a set of accounts the team should be focused on in the next year.
But they’re stuck making these big account selection decisions with missing data. These data gaps make it difficult to research and execute their account selection strategy, which happens to be one of the core elements of GTM planning. If you don’t start with a foundation of the right accounts, then everything after that is wasted effort. Even the best sales reps can’t sell to the wrong accounts.
Sales leaders have lots of data. Lots. But they don’t always have the right data.
That’s changing, however. AI is making it possible to find exactly the data you need to make those big strategic business decisions.
Even as recently as a year or two ago, companies would resort to scraping data from websites (or paying for a tool that did this). To do this data scraping, you had to know exactly what you were looking for. You’d need to search for a URL that looks a certain way, content that includes specific words, or a particular piece of Javascript.
But generative AI doesn’t need that. It can look for information much in the same way a human would, which opens up so many more possibilities. Sales leaders can finally get the data they need to inform their big decisions, like which accounts their sales teams should focus on next year.
So let’s talk about the role of AI and data in the different aspects of account selection.
Account selection includes:
- ICP definition: What criteria comprise a good-fit account?
- Account score: How do you rate accounts?
- Segmentation: How do you prioritize market segments?
- Territory design: How do you divvy up high-potential accounts?
ICP definition
When we talk about ICP definition for B2B GTM teams, we are attempting to clearly articulate what criteria comprise a good-fit account. What does our very best prospect look like?
Most companies already have a good definition of the basics of what goes into ICP. They know what firmographics, like company size and industry, they need to focus on.
But the details are almost always evolving. What else do you need to know about a company to determine if they’re worth selling to?
This kind of data is unique to your organization, highly specialized, and requires some level of interpretation or inference to answer that question of "Is this company a good prospect?"
Until now, this data hasn’t been easily available from existing data enrichment because it’s specialized and requires some level of human interpretation. This means that you’ve either been using SDRs to research accounts individually, or just not getting this data at all.
But AI is finally making it possible to find exactly the data you need to fill in your gaps. Gen AI can look for things much in the same way a human would, which opens up so many more possibilities to identify specialized, unique data that requires some kind of interpretation - the data that tells you what else you need to know about a company to decide if they’re worth selling to.
Instead of relying on someone else’s database, you can now find exactly the data you need straight from the source to answer those tough questions and refine your ICP definition to something that's more specific, more accurate, and more operationalizable.
Account score
The next component of account selection is the account score. This is the quantitative implementation of your ICP. How do you rate accounts? How much weight do you give to each of the criteria you identified in your ICP definition?
You have a set of criteria that go into a good-fit prospect. And some criteria that are deal breakers - things a prospect company has to have or be to be qualified at all. You've probably also identified some nice-to-have criteria; those are the things that take a prospect from good to great. You’ll likely rate these kinds of criteria differently. The deal breakers get more weight than the nice-to-haves.
A robust account score should also include some variable or behavioral criteria. What intent or timing signals factor in? What about marketing or sales engagement? Are there things an account (or a contact associated with that account) needs to have done to reach a certain account score?
Account scoring is really about being able to quantifiably identify your good, better and best prospects.
Segmentation
Next comes segmentation: how you prioritize market segments for outreach. Which ones do you target first?
You likely think about segmentation in a few ways. You want to figure out prioritization (which accounts to work first) and allocation (who works which accounts).
Segmentation may be based on your account scoring methodology. You probably have thresholds for certain segments. For example, if an account scores 80 or higher (out of a possible 100), then it goes into a high-priority segment. You may also identify segments of customer lookalikes that are very closely related to your best customers in key ways. These sorts of segments help you understand which sets of accounts to work first.
You probably also have segments based on the market. These could be related to company size or expected spend, or a company’s industry or location. These segments may dictate which sales reps can work these accounts. For example, you may have an enterprise segment or a specialized vertical segment.
You’ll likely segment your account base in multiple ways, and this may change over time. But this segmentation is important for the fourth and final component of account selection - territory design.
Territory design
Once you’ve got all the foundational pieces of what makes a good prospect set, it’s time to think about territory design. How do you allocate quota capacity across your high potential segments?
There are lots of different territory designs (more about choosing your model here), but if you’re reading this, you already know we think a dynamic model is the best choice for many B2B SaaS companies. Dynamic books gives you the flexibility and adaptability to keep sales reps focused on the best possible accounts in your market at all times.
But regardless of the particular territory model you implement, you want to be sure that every rep has a right-sized book of high-potential prospect accounts to call on. If you did your job in the previous steps by figuring out ICP, account scoring and segmentation, then your territory design should fall into place relatively easily.
Even if you don’t embrace a true dynamic books approach, you’ll want to revisit your territory allocations regularly over the year (and more than just halfway through - please do this at least quarterly, if not more often).
Better data through AI = better account selection
Sales leaders have long been stuck making big annual planning decisions with missing data. These data gaps make it difficult to research and execute their account selection strategy.
But AI is finally making it possible to find exactly the data you need to fill in your gaps and make better decisions. Generative AI can do more than just enrich a set of accounts. It can help you find and interpret the specialized and complex data you need to know about a company to determine if they’re worth selling to.
We're going to follow up with deep dives into each of the 4 components of account selection over the next few weeks. Stay tuned for more.
