ABM doesn’t fail because it’s a bad idea
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It fails because it’s fragmented.

Most teams think they’re “doing ABM” because:

• ads are running
• content is publishing
• outbound is firing

But none of it is connected.
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Disconnected motion kills momentum.

You don’t have an ABM system.
You have three teams running three plays at three different tempos.
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Step 1 — Intent signals are first-class data

Forget CV-based scraping.
We start with first-party events:

Examples:

• pricing page view
• case study consumption
• demo scheduler opens
• repeat sessions within 7 days

This data is:

• structured
• timestamped
• machine readable

This is the trigger layer.
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Step 2 — Resolve identity at the account level

Once a high-intent event fires, the system resolves which company it belongs to.

This is done using:

• business IP intelligence (Snitcher, KickFire)
• reverse domain mapping
• firmographic APIs

Outputs:

• company domain
• name
• size
• industry
• location

Why this matters:

• Company identity is what sales cares about
• Individual identities at this stage are noisy
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Step 3 — Score against an explicit ICP

This is the real system pivot.

Instead of enriching everything,
we only score first, then enrich.

The qualification layer applies:

• rules (industry, size, fit criteria)
• engagement recency
• composite scoring (0–100)
• reason codes

This can be implemented with:

• rule engine
• custom code (node, python, serverless)
• or AI scoring (Claude Code)

This layer outputs:

• tier (A / B / C)
• score
• reason

Step 4 — Conditional enrichment

Now that you know which companies matter,
pull just the data you need.

For Tier A:

• contact discovery (roles)
• email verification
• technographics

For Tier B:

• partial enrichment
• wait for next signal

For Tier C:

• drop
• cheap retargeting

This keeps:

• cost low
• API calls minimal
• noise out of the system

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Step 5 — Activation into outreach systems

Now the activation layer takes over.

We push structured payloads such as:

{
 company: { domain, name },
 intent: “Demo page view”,
 score: 87,
 recommendedAngle: “Friction in demo ops”
}

to systems like:

• Instantly (API)
• HubSpot (custom API)
• Slack alerts for ops
• CRM for sales

This is not blasting.
This is engine-driven outreach.

Step 6 — System-wide measurement & feedback

Every outcome feeds the loop:

• opens
• replies
• meetings booked
• pipeline movement

This feeds back into:

• event rules
• scoring refinement
• messaging optimization

This turns ABM into a truly adaptive engine, not a set of disconnected plays.

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Why this converts — in real engineering terms

Because this system:

• uses first-party signals
• avoids spammy lists
• aligns sales with real intent
• preserves context across platforms
• measures impact holistically

It’s ABM as feedback-oriented infrastructure, not tactics.
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The real takeaway

ABM is not a campaign.
It’s a signal-driven decision system.

Once you build the system around real intent flow,
everything else is execution detail.

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