abm-1-to-many

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:

• All required sections are complete
• Output matches the user's stated need
• Named frameworks are cited for key recommendations
• No vague claims — every recommendation has a specific action
• Deliverable is ready for operational use, not just conceptual

Common Pitfalls

1. 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.
2. 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.
3. 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 assumptions
• templates/output-template.md — copy-paste deliverable structure for the user
• scripts/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

1. What is the primary motion: founder-led, sales-led, product-led, partner-led, or lifecycle-led?
2. Which ICP tier is the output for: small business, mid-market, enterprise, or mixed?
3. What proof is available today: customer stories, usage data, third-party validation, screenshots, or none?
4. What system will execute the work: CRM, sequencer, warehouse, support desk, product analytics, or manual workflow?
5. 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