Step 1: Define the Purpose of Your Signal System

Establish the core outcomes your signal engine must drive:

• Score account awareness & buying readiness
• Trigger automated outbound / ads / tasks
• Push high-priority CRM + Slack alerts to reps
• Track expansion, retention, and churn risk
• Build high-intent ABM segments dynamically

Step 2: Map Your Signal Categories

All signals fall into three universal buckets:

- First-party: Product usage, website activity, CRM interactions

- Second-party: Partner overlaps, referral networks, ad engagement

- Third-party: Public data (funding, hiring, tech changes, social activity)

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Step 3: Instrument First-Party Signals

Capture the highest-quality engagement signals your company owns:

• CRM activity (emails, replies, calls, tasks)

• Product analytics (activation, feature use, usage depth)

• Website events (visits, pricing page, form submissions)

• Marketing & content events (webinars, downloads, campaigns)

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Step 4: Add Second-Party Signals

Expand visibility using partner ecosystems and shared intent sources:

• Partner overlaps

• Warm intros or mutual connections

• Review-site behaviour

• Ad and LinkedIn engagement

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Step 5: Layer Third-Party Signals

Track buying readiness cues happening outside your ecosystem:

• Tech-stack changes, funding rounds, hiring spikes

• Market news and category activity

• Social engagement & keyword/search trends

• Firmographic + technographic enrichment updates

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Step 6: Centralize All Signals

Bring everything together into one system of record:

• Ingest all signals into CRM or your warehouse

• Use ETL/integrations to eliminate data silos

• Tag each signal with source, timestamp, account ID, and contact ID

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Step 7: Normalize & Score

Clean and structure all signals into measurable scoring inputs:

• Standardize data formats across sources

• Weight signals by type, recency, intensity, and impact

• Convert raw signals into Awareness Stages:

• Identified

• Aware

• Interested

• Considering

• Selecting

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Step 8: Enrich Accounts & Contacts

Combine fit + intent to maintain a real-time GTM profile:

• Merge firmographic & technographic fit with live signal data

• Maintain updated tiers, personas, and ownership inside CRM

• Track account health, readiness, and post-sale risk in one view

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Step 9: Trigger Automations

Activate the entire GTM engine when meaningful signals fire:

• Launch outbound sequences when awareness increases

• Trigger ads, retargeting, or ABM plays for high-intent accounts

• Create tasks for AEs/SDRs directly from CRM events

• Push top-priority alerts to Slack for instant follow-up

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Step 10: Build ABM Lists & Post-Sale Monitors

Use signals to power full-funnel revenue workflows:

• Combine fit + intent to create precise ABM segments
• Track usage decline or champion changes for retention motions
• Surface upsell/cross-sell moments using feature adoption signals

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Step 11: Close the Feedback Loop

Feed meeting + deal outcomes back into scoring logic Increase weights for signals that drive revenue; decrease noise Review dashboards monthly to reassess signal ROI and effectiveness

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