Demand Gen & Pipeline Build Plan
From TAM to a campaign plan with channels, signals, and named accounts — one workflow.
TAM → tiered campaign plan with signals and channels, one prompt
| Tier | Signal | Channel |
|---|---|---|
| A — 312 | Intent ≤30d, tech fit confirmed | SDR + ABM ads |
| B — 920 | Recent intent OR tech fit | Content syndication |
| C — 2,608 | Firmographic fit only | Paid social / brand |
Overview
Multi-channel pipeline-build plan that goes from TAM to a sized, channel-tagged campaign — segmenting on intent + technographic fit, naming the accounts to start with, and tying each channel to the signal that justifies it.
Use cases
QBR-ready plan in a single afternoon
Your DG director walks into the next pipeline-build QBR with a tiered list (sized via `search_companies`), the channel mix per tier, the signal each channel is activating, and named persona spine — not a slide deck of intentions. Every number is cited. The CRO stops asking 'where does that come from'.
SDR + ABM teams stop fighting over the same list
Tier A is the SDR + ABM-ads list (high signal, expensive motion). Tier B is content syndication (lower friction). Tier C is paid social (cheap, broad). Tiering is signal-defined and reproducible, which is how cross-team handoffs stop relying on personality.
View workflow prompt
# Demand Gen & Pipeline Build Plan
## Parameters
- `{{product_category}}` *(required)* — Phoenix product category the campaign promotes. Example: `cloud cost optimization`
- `{{target_geography}}` *(required)* — Target region as an ISO-style descriptor. Example: `US`
- `{{target_segment}}` *(required)* — Sales-segment definition (industry + employee + revenue band). Example: `mid-market software, 200-2000 employees`
## Purpose
Turn {{product_category}} into a quarter-ready demand-gen plan for {{target_segment}} in {{target_geography}}. Output is a tiered account list, the channel mix per tier, and the signal each channel is keyed to.
## Process
1. **Size the TAM** — `search_companies` filtered by {{target_segment}} + {{target_geography}} returns the total count and the cohort identity (companyId/companyName/domain). For the distribution by employee band and the top 10 industries by share, enrich the returned cohort via `company_firmographic` (employees, industry per company) — those fields are not in the search output.
2. **Find buying-aware accounts** — `intent_category` and `company_intent` for topics related to {{product_category}}. Carve TAM into Tier A (rising intent, ≤30d), Tier B (recent intent or technographic fit), Tier C (firmographic fit only).
3. **Confirm fit** — `company_technographic` for tech-stack overlap that signals {{product_category}} relevance. Filter Tier A/B to confirmed buyers vs. lookalikes.
4. **Channel mapping** — for each tier, name 2-3 channels (paid social, ABM ads, SDR-led, content syndication, partner-co-marketing) and *which signal* triggers each.
5. **Persona spine** — `contact_search` for the top 3 personas across Tier A. Name the titles + functions the campaign will index against.
## Output Format
Markdown with:
- `# Demand Gen Plan — {{product_category}} × {{target_segment}}`
- `## TAM Snapshot` — sized total + distribution table
- `## Tier A / B / C` — account counts + the signal that defines each tier
- `## Channel Plan` — table: tier | channel | signal it activates | volume target
- `## Personas` — table: title | function | seniority floor | tier coverage
- `## Citations` — every count cites its HG tool
## Quality Checklist
- TAM count is a real number from `search_companies`, not a guess
- Tier A list is ≤10% of TAM (otherwise the tiering is too loose)
- Every channel is tied to a *named* signal, not a vibe
- Personas are titles + functions, not aspirations