I’ve shared quite a bit about ICP, account scoring, territory design and compensation over the last year of this newsletter. These are all critical for great GTM execution but they’re also, admittedly, a bit on the nerdy side. There’s been plenty of analysis, some esoteric concepts and quite a few spreadsheets.
But I think we’ve got all those beat—both for strategic importance and for nerdiness—with today’s account segmentation conversation with Grant Hefler. Grant is an Executive Consultant at SBI, one of the premiere GTM consulting firms. They help PE-backed and publicly traded B2B SaaS companies grow faster through GTM strategy consulting, training and operational execution.
Grant walks us through the whole segmentation process step-by-step. We got deep on tricky analysis topics that impact ICP scoring and account potential, covering things like cohort selection, sample sizes, factor weighting, frontier analysis and P x Q approaches. It wasn’t all nerding out on analysis, though. We also talked about running the process, managing stakeholders, plugging into the larger annual planning exercise and how to put segmentation results to work.
I loved this conversation because there’s no fluff. It’s just a ton of really practical and useful stuff for anyone looking to do segmentation.1 Seriously, keep this one for reference.
Highlights below in bullet point form.
(1:47) What is segmentation anyway?
“So account segmentation, you know, it’s right in the name. Everything is at the account level. […] it addresses like three key things: it’s your strategy, your prioritization, and then also your resource allocation.”
The purpose of account segmentation is to drive strategy, prioritization, and resource allocation.
It’s an account-level exercise that’s distinct from broad market segmentation. This isn’t a TAM calculation which is best done top-down.
At the core, it produces two key metrics:
ICP score – a fit score that determines how well an account aligns with your ICP. This is not the same as propensity to buy.
Account potential – how much an account could spend based on bottoms-up financial and firmographic data.
Once you’ve got an ICP score and an account potential, you can use them to inform all your downstream processes like capacity modeling, comp plan design, coverage models and territory.
(5:44) Getting started
“Having nice clean data is really big. You have that, we’re at a really great spot already. If we have… even the skeleton of the ideal customer profile, that’s pretty much all you’re gonna need to get started with segmentation.”
Gather as much clean, reliable customer data as you can. The most important thing is a structured customer “cube”2 with accurate accounts, spend, and hierarchy information, since this drives how segmentation and coverage will operate.
Get a reasonably complete list of targetable accounts so segmentation reflects the true SAM.
Start with a basic ICP hypothesis. At minimum, this means a working understanding of who you believe you should be targeting. You can use the segmentation work to help refine this.
Prepare firmographic and technographic data. You’ll want to refresh your data from a 3rd party data provider to make sure it’s as up-to-date as possible (even though it’ll never be perfect).
Come with as much strategic clarity as you can. This can include high-level ideas around coverage model, team structure, and major priorities.
(11:47) ICP scoring
“Really it’s a framework to describe the most attractive and the best fitting accounts for your solutions based on their firmographics and technographics.”
ICP is a framework defining the best-fit accounts based on firmographic and technographic factors—not personas and not propensity-to-buy.
ICP development requires art and science. You should base it on your existing data but it depends on business strategy. For example, your company may be moving upmarket which means yesterday’s customer metrics aren’t a good fit for tomorrow’s desired state.
ICP is meant to be a relatively stable, long-term fit model that should be considered separately from short-term behavioral or intent signals.3
Calculating an ICP score
Select 4–6 measurable factors that correlate with historical success (e.g., employee count, revenue, industry, cloud maturity, tech stack, budget metrics).
Assign each factor a weight within an overall score which usually sums to 100 (e.g. 5 factors might each be weighted 20 points).
Break each factor into cohorts (e.g., employee count bands; industry sub-segments) to allow comparison of account performance across groups.
Figuring out cohort boundaries is easier said than done, but they often become clear when you look at ARR distribution and other deal metrics through the lens of business strategy (e.g. more granular employee bands for mid-market companies if that’s a focus area).
Always make sure cohorts have an adequate number of accounts. What counts as “adequate” varies but you’ll probably want ~10 deals in each cohort to have reasonable confidence.
Categorical factors (e.g., industry) may require further decomposition or combination with another categorical factor (e.g., industry × country) to capture meaningful variation. But again, don’t slice too thin.
Within each factor, give each cohort a sub-weight (up to that factor’s total weight) based on how it performs on deal metrics such as win rate, deal size, cycle time, and ARR contribution.
Rank cohorts within each factor based on these metrics, and assign sub-weights proportionally based on the ranking.
The final ICP score for an account is the sum of its sub-weights across all factors.
(26:45) Account potential
“The idea is to estimate how much—at the account level—each customer or prospect can spend on your solution.”
Account potential estimates how much each customer or prospect could spend on your solution. You can measure this at the account level overall or at the product/line of business level.
There are two primary methods for calculating account potential: frontier analysis and P x Q.
Frontier analysis
Use customer spend as a percentage of a financial metric (e.g., revenue, IT budget, ARR per employee).
Group accounts into cohorts using a categorical and size metric (commonly industry × company size). Just like with ICP, make sure you’ve got enough samples in each cohort (Grant recommends at least 10) and watch out for outliers.
Within each cohort, compute the 80th percentile of spend ratio to establish a benchmark (“frontier”).
You can then scale the “frontier” to each account’s size (e.g., 80th percentile ARR/employee × actual employee count) to calculate potential.
2. P × Q model
This approach works best for per-user pricing models.
Q = estimated number of potential users (e.g., employees who could adopt the product).
P = price per user or per seat.
Total potential for each account = P × Q. You can also do this at the product level if products have different prices and user bases within an account.
Remember, potential is directional, not literal. Nobody should treat this as a precise forecast. Potential simply identifies where the most value exists if everything else goes according to plan.
(35:10) Operationalizing it all
“…not just for segmentation and for the ideal customer profile, but depending what downstream go-to-market initiatives you’re also working on, having change management plans set up for territories, quota, comp...”
Use ICP score + account potential to build prioritization models for customers and prospects.
ROAD model for existing customers
Plot customers on a scatter plot where the X axis is current spend and the Y axis is account potential.
ROAD stands for Retain, Opportunistic, Acquire and Develop. Each of these represents a quadrant in your scatter plot. For example, Acquire accounts would be in the top left with relatively low existing spend but high potential and Develop accounts would be in the top right with high spend and headroom to spend even more.
Use the ROAD placement of the customer to determine their service model (e.g. white glove account management for Develop customers and put hunters on Acquire customers).
Plot prospects similarly to the ROAD model but use ICP score for the X axis. The top right quadrant (high fit + high potential) becomes the foundation of your new logo assignments.
Build balanced territories by distributing ICP quality and white-space potential evenly across reps, ensuring equitable opportunity.
Feed segmentation into quota, comp, and capacity planning by testing whether territories provide enough potential for target attainment and determining required headcount.
Direct marketing spend and other GTM investment towards high fit, high-potential segments.
Plus one bit of technical difficulty around minute 21.
This is just a fancy name for a spreadsheet of customer accounts with relevant firmographics, hierarchy data and spend (optionally broken out by product line or other factors).
I’ve written about this myself in the form of the “fit vs timing quadrant”
