ICP and Buying Committee
Know who to target, what signals indicate they are in-market, and who the decision makers are inside a target account.
Why this matters
Your job is to find, score, and route the right accounts. Every list you build, every enrichment prompt, every n8n scoring node will encode an ICP definition. If your ICP is wrong, everything downstream is wasted compute and wasted AE time. Get this right and you compound.
ICP — Company level
- Size: 200 to 5,000 employees, $50M+ revenue
- Stage: Series B through public
- Data team: 3+ engineers (analytics engineer, data engineer, data platform)
- Stack signals (positive): Snowflake, Databricks, BigQuery, dbt, Fivetran, Airbyte, Unity Catalog
- Current orchestration: Airflow, cron jobs, homegrown scripts, or nothing
- Strong verticals: software/tech, fintech, retail/e-commerce, logistics, life sciences
- Weak verticals (today): heavily regulated legacy enterprise with no modern stack, very small startups with no data team yet
Buying triggers (in-market signals)
- Hiring data engineers, analytics engineers, or data platform engineers
- Job postings mentioning Airflow, dbt, Snowflake, Databricks
- New funding round (Series B+)
- Hiring a Head of Data, VP of Data, or Director of Data Platform
- Public AI/ML initiative announced (especially "AI-ready data" language)
- Databricks or Snowflake expansion announced
- Migration off legacy ETL (Informatica, Talend) mentioned in jobs or blog
- Recent dbt adoption (often signals appetite for modern orchestration next)
Buying committee personas
| Role | Titles | What they care about | What you say |
|---|---|---|---|
| Economic Buyer | Head of Data, VP of Data, Director of Data Platform | Reliability, TCO, team productivity, board-level data trust | Outcomes (uptime, MTTR, headcount efficiency), TCO vs Airflow + Astronomer |
| Technical Champion | Staff Data Engineer, Analytics Engineering Lead, Platform Lead | DX, debuggability, integrations, asset model | Hands-on demo, lineage, branch deployments, Python-native DX |
| Influencer | Analytics Engineer, BI Developer, Senior Data Analyst | Freshness, trust, self-service | Compass, asset checks, freshness SLAs |
| Late-stage Blocker | IT, Security, Procurement | SOC 2, SSO, governance, data residency, contract terms | Dagster+ Pro tier capabilities (Solo/Starter/Pro are the public tiers), security pages, SOC 2 reports |
Hands-on exercises
Find three real ICP matches on LinkedIn
Auto-savedFind three real companies on LinkedIn that match the ICP. For each: (a) company name, (b) why they fit (size, stack signals, verticals), (c) likely current pain, (d) first contact target (name + title), (e) why this person first (persona + hook).
Self-check
Answer from memory first. Then reveal the model answer and grade yourself honestly.
State the ICP at company level in one sentence.
Name four buying triggers that would put an account on your scoring list.
Name the four buying committee personas and what each cares about most.
A prospect's title is 'VP of Data'. What three questions are likely top of mind for them?
You see a Series B fintech with 800 employees that just posted three 'Senior Data Engineer (Airflow, dbt, Snowflake)' roles. Is this ICP and what is your first move?