| name | abm-1-to-many |
| description | Execute Programmatic ABM (1-to-many) for 50-200+ accounts — automated personalization, scaled outbound, lookalike expansion. Triggers on: "1-to-many ABM", "programmatic ABM", "scaled ABM", "automated ABM". |
| license | MIT |
| compatibility | Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose |
| metadata | {"version":"1.0.0","author":"LeadMagic","category":"abm","tags":["abm","1-to-many","programmatic-abm","scaled","automation"],"frameworks":["TOPO Programmatic ABM","Clay Automation Patterns","ITSMA — Account-Based Marketing"]} |
ABM 1-to-Many (Programmatic)
Overview
Programmatic ABM for 50-200+ accounts using automation, lookalike modeling,
and scaled personalization. This tier uses the same methodology as 1-to-1 and
1-to-few but replaces manual effort with AI and workflow automation.
Authoritative Foundations
- TOPO Programmatic ABM — Named methodology governing recommendations in this skill's process.
- Clay Automation Patterns — Waterfall enrichment, Claygent research, and table-based GTM automation.
- ITSMA — Account-Based Marketing — Tier-based ABM (1:1 / 1:few / 1:many); measure pipeline from target accounts, not lead volume.
When to Use
- "Scale ABM to more accounts"
- "Programmatic ABM setup"
- "Automated account-based outreach"
- "Expand ABM coverage without headcount"
Step-by-Step Process
Phase 1: Lookalike Expansion
Start from Tier 1-2 winners and expand:
- ICP lookalike: Find accounts matching your top 10% win profile
- Intent lookalike: Accounts showing similar buying signals to closed-won
- Engagement lookalike: Accounts engaging with content the way winners did pre-opportunity
- Trigger lookalike: Accounts with same triggers (funding, hiring, tech change)
Phase 2: Automated Account Intelligence
Use enrichment and AI to auto-build briefs:
- Clay workflow: pull firmographics, technographics, news, signals
- AI summarizes: company snapshot, pain hypothesis, relevant proof points
- Auto-prioritize: score accounts 0-100 and assign to SDR queues
Phase 3: Scaled Personalization
- Dynamic landing pages: URL params personalize hero/headline by industry/company
- Tokenized email sequences: Merge fields beyond first name — industry, tech stack, signal
- Automated LinkedIn: AI drafts personalized connection notes and DMs
- Retargeting: Account-based ad audiences on LinkedIn by company name or domain
Phase 4: Automated Cadence Orchestration
- SDR assigned accounts per round (rotating to prevent burnout)
- Automated task creation in CRM per account
- AI drafts first outreach; SDR reviews and sends
- AI handles replies (OOO, not interested, wrong person); SDR handles positive replies
Phase 5: Feedback Loop
- Weekly review: which accounts engaged, which didn't
- Kill accounts after 8 touches with no reply
- Feed winners back into lookalike model
- Continuously refine ICP based on engagement patterns
Output Format
Programmatic ABM plan with: lookalike criteria, enrichment workflow, personalization
templates, SDR routing rules, and optimization framework.
Quality Check
Before delivering, verify:
Common Pitfalls
- Treating ABM as a marketing-only initiative. ABM requires tight sales alignment. Without BDRs assigned to specific accounts and shared account briefs, marketing produces content nobody uses. Fix: weekly ABM standups with marketing + BDRs + AEs.
- One-size-fits-all tiering. Applying the same playbook to Tier 1 and Tier 3 accounts. Fix: Tier 1 gets custom content and executive engagement; Tier 3 gets automated personalization.
- Measuring ABM on MQLs. ABM success is pipeline from target accounts, not lead volume. Fix: track coverage %, engagement depth, pipeline created, and win rate by tier.
Execution Artifacts
references/framework-notes.md — named frameworks, citation anchors, and operating assumptionstemplates/output-template.md — copy-paste deliverable structure for the userscripts/check-output.py — local checklist validator for required sections
This skill includes lightweight artifacts the agent can load on demand:
Use the artifacts when the user asks for an implementation-ready deliverable, a repeatable workflow, or a quality check rather than generic advice.
Implementation Depth
Use this section when the user asks for a finished asset, not a high-level explanation.
Diagnostic Questions
- What is the primary motion: founder-led, sales-led, product-led, partner-led, or lifecycle-led?
- Which ICP tier is the output for: small business, mid-market, enterprise, or mixed?
- What proof is available today: customer stories, usage data, third-party validation, screenshots, or none?
- What system will execute the work: CRM, sequencer, warehouse, support desk, product analytics, or manual workflow?
- What decision will the user make from this output: launch, prioritize, route, rewrite, score, coach, or measure?
Framework Application
Map the recommendation explicitly to the named frameworks in this skill:
- TOPO Programmatic ABM: apply only the part that directly improves the requested deliverable.
- Clay Automation Patterns: apply only the part that directly improves the requested deliverable.
- ITSMA — Account-Based Marketing: apply only the part that directly improves the requested deliverable.
Deliverable Standard
A strong output from this skill includes:
- A crisp diagnosis of the current situation
- A recommended path with tradeoffs, not a generic list
- A concrete artifact the user can use immediately: table, script, checklist, scorecard, sequence, dashboard spec, or implementation plan
- A measurement plan with leading and lagging indicators
- Risks and edge cases called out before execution
Adaptation Rules
- For small business: reduce complexity, shorten time-to-value, and prioritize owner/operator clarity.
- For mid-market: include workflow ownership, handoffs, integrations, and enablement assets.
- For enterprise: include governance, risk, procurement, stakeholder mapping, and proof requirements.
Related Skills
- clay-automation, ai-sdr-setup, list-building, signal-scoring, icp-scoring
