Function-Led Buying Signal Prospect
The function owns the budget and just added a tool — that's the signal. No fabricated 'hiring deltas', just two real point-in-time facts composed honestly.
Sample output for this workflow will appear here once it is captured.
Run the workflow in Claude, ChatGPT, or HG Insights Playground using the buttons below to see real output.
Overview
Surface accounts whose buying function owns a high share of tooling adoption (`departmentUsageShare` on `company_fai`) AND has recently added a function-relevant product (`firstVerifiedDate` on `company_technographic`). The function-led-signal workaround for the absence of a native FAI delta: two point-in-time signals composed deliberately, not one inferred trend.
Use cases
Two real signals beat one inferred one
Your SDR runs this with a function and the products it owns. The output is accounts whose `departmentUsageShare` shows the function is invested in tooling AND whose `firstVerifiedDate` shows a recent product add by that function. Honest about what HG MCP actually exposes: there is no native FAI delta series, so the workflow composes two point-in-time signals instead of fabricating a trend.
Function first, not headcount first
Per `hg-fai-vs-contact-search`, FAI tells you WHICH function owns the decision — it's not a generic employee-count delta. The contact suggestion per row maps to the buying-function's seniority, so the SDR opens with the person whose budget actually owns the tooling category.
View workflow prompt
# Function-Led Buying Signal Prospect
## Parameters
- `{{domains}}` *(required)* — Comma-separated target-account domains (the territory's named-account list). Example: `acme.com,globex.com,initech.com`
- `{{buying_function}}` *(required)* — The function whose tooling ownership is the buying signal (Engineering, Security, Sales Ops, Marketing, Finance, HR, etc.). Example: `Security`
- `{{function_products}}` *(required)* — Comma-separated product names owned by {{buying_function}} to evaluate for usage-share + recent adds. Example: `Wiz, CrowdStrike, Snyk`
- `{{your_product}}` *(required)* — Your product or category — names the connection to the function's investment. Example: `Wiz`
- `{{added_within_months}}` *(required)* — Lookback for the recent-product-add part of the composite signal (use 12 months — function-led signals decay slower than other triggers). Example: `12`
## Purpose
You are an SDR working a function-led signal for {{your_product}}. Per [`hg-fai-vs-contact-search`](https://phoenix.hginsights.com/gtm/skills/hg-fai-vs-contact-search), `company_fai` is the FUNCTION-level signal — who owns the buying decision. **`company_fai` is point-in-time only** — there is no native trend series. This workflow composes two point-in-time signals deliberately: high `departmentUsageShare` for {{buying_function}} on at least one of {{function_products}} (the function is invested in tooling) AND a recent `firstVerifiedDate` for a {{buying_function}}-owned product (the function is actively buying). Either signal alone is weak; the composition is the buying-signal proxy.
## Process
1. **Resolve products** — for each name in {{function_products}}, call `get_vendor_information` (`vendorId: <id>, includeProducts: true`) with a vendor_id resolved via discovery to capture the `vendor_id` and `product_id`s. Collect the resolved IDs as `functionVendorIds` and `functionProductIds`.
2. **Per-account FAI probe** — for each domain in {{domains}}, `company_fai` with `productIds: functionProductIds` (preferred; `products: {{function_products}}` as fuzzy fallback). Capture `departmentUsageShare` per product for the {{buying_function}} department. Keep accounts where {{buying_function}} owns ≥40% share on at least one product.
3. **Recent-add probe** — for each survivor, `company_technographic` with `vendorIds: functionVendorIds`. Keep accounts with at least one row whose `firstVerifiedDate` falls inside the last {{added_within_months}} months.
4. **Firmographic gate** — `company_firmographic` per survivor for industry / employee band; drop ICP misses.
5. **Contact pass** — `contact_search` per survivor for {{buying_function}} senior titles (Director / VP / Head of); `contact_enrich` budget-capped at the top 5 accounts.
6. **Rank** — by `departmentUsageShare` (highest first), then by recency of the most-recent `firstVerifiedDate`.
## Output Format
- `# 🎯 Function-Led Buying Signal — {{buying_function}}`
- `## Ranked accounts` — table: rank | domain | industry | employee band | {{buying_function}} usage-share (cited product) | recent product add (name, date) | suggested contact | one-line hook
- Hook ties BOTH signals — the function's tooling ownership + the recent add — to {{your_product}} per [`opening-line-discipline`](https://phoenix.hginsights.com/gtm/skills/opening-line-discipline)
- Per-row citation: `[company_fai]` for usage-share, `[company_technographic]` for install date, `[company_firmographic]` for industry/employees
## Quality Checklist
- BOTH signals fire per row — usage-share ≥40% AND a recent product add inside the {{added_within_months}}-month window. Single-signal rows are cut.
- NO claims of a "hiring delta" or "FAI 6-month shift" — the tool is point-in-time; framing must reflect that.
- The hook explains WHY both signals together imply a {{your_product}} buying conversation — not just "your team is growing"