Functional Area Intelligence
Department-level product usage — who's using what, and where
What is it
FAI reveals WHICH DEPARTMENTS within a company are using which products. Instead of just knowing "Adobe uses Terraform," FAI tells you "Adobe's ENGINEERING TEAM IN SAN JOSE uses Terraform, and DevOps Engineers appear to be the decision-makers."
FAI includes: functional area (Engineering, Finance, IT, Marketing, Sales, Medical, etc.), role within that area (DevOps Engineer, Data Scientist, etc.), purchase role (Decision-Maker, Influencer, Individual Contributor), and geographic signals.
What problem it solves
Enterprise companies are not monoliths. Marketing buys different tools than Engineering. Knowing COMPANY-WIDE product installs isn't enough — you need to know which department owns the budget and which roles drive decisions. FAI solves the "who actually uses this?" problem.
What you can do with it
- Persona targeting: Target DevOps teams using Kubernetes, not just "companies using Kubernetes"
- Land-and-expand: Find which departments at existing accounts don't use your product yet
- Buying committee mapping: Identify decision-makers vs. influencers for a given product category
- Message personalization: Tailor outreach to the functional area
- Market analysis: Understand which departments drive adoption of a technology
Real-world example
Land-and-Expand at J&J
Your SaaS tool is used by J&J's IT Operations team. Using FAI, you discover that Engineering and Data Science teams at J&J also use similar tools from competitors. You build a targeted expansion campaign for those departments, referencing the existing IT Ops deployment as social proof. Result: 2 new departmental deals from an existing logo.
What the data looks like
Salesforce FAI for Linux (Top Departments & Roles)
Key fields you get
- Department metadata: Department name, department ID, department usage share (% of product signals from this dept), department signal strength (evidence volume)
- Role metadata: Role name, role ID, role usage share (% of department's usage from this role), role signal share
- Purchase influence: isDecisionMaker (boolean), isInfluencer (boolean)
- Geographic signals: Country, state, city where this role+product combo was detected
- Verification: Last verified date, total count of supporting records
- Product context: Product name, product ID
“At Salesforce, Linux is primarily an Engineering tool (76% of signals). Within Engineering, Software Engineers (59%) and DevOps Engineers (24%) are the key decision-makers. The IT department also uses Linux (20%), driven by Security Engineers (59% of IT usage) who are also decision-makers. If you're selling Linux tooling, target Software Engineers and DevOps Engineers in Engineering + Security Engineers in IT — all three roles can approve purchases.”
Business impact
Increases: Higher response rates when messaging the right department
Increases: More expansion revenue through cross-departmental land-and-expand
Decreases: Shorter sales cycles by engaging decision-makers directly
Increases: More accurate personas for campaign targeting
Directional outcomes HG customers commonly report. Actual results vary by program and data application.
Under the hood
How it's unique to the market
No one else does this. FAI is a proprietary HG capability that bridges product installs with organizational structure. Competitors can tell you "company X uses product Y." Only HG can tell you "the Engineering team at company X's London office uses product Y, and the decision-makers are DevOps Engineers."
How it's derived
HG's Functional Area Classifier (FAC) is an ML model that reads job titles from tracked documents (job postings, resumes, etc.) and infers three things: (1) functional area (e.g., ENGINEERING), (2) role within that area (e.g., DEVOPS_ENGINEER), and (3) purchase role (Decision-Maker / Influencer / Individual Contributor). This classification is joined with product install data to produce department-level usage signals.