Use historical data to define what a high-value account looks like.

• Export Closed Won and Closed Lost (last 12-24 months)
• For Closed Won:
  • Identify highest revenue customers
  • Analyze:
    • ACV
    • Sales Cycle
    • Expansion revenue
  • Look for patterns across:
    • Industry
    • Company size
    • Geography
• For Closed Lost:
  • Identify common disqualification patterns:
    • Poor-fit industries
    • Company size mismatch

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Set up how accounts will be grouped and prioritized.

• Define segment based on:
  • Company size
  • Industry
  • Business model
• Define scoring logic using:
  • Firmographics (size, revenue, industry)
  • Technographics
  • Signals
• Define tier structure:
  • Tier 1: Strong fit
  • Tier 2: Good fit
  • Tier 3: Low fit

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Structure the account database in Clay.

• Create core fields:
  • Company name
  • Domain
  • Linkedin URL
• Add enrichment fields:
  • Employee count
  • Revenue
  • Industry
  • Location
  • Tech stack
• Add logic fields:
  • Segment
  • Tiering

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Populate data and apply scoring.

• Run enrichment in Clay:
  • Fill missing firmographic data
  • Add technographics data
• Apply scoring logic:
  • Use Clay formulas
  • Assign final tier

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Push structured account data into CRM.

• Create properties:
  • Account Tier
  • Segment
  • Key firmographic fields
• Sync data from Clay:
  • Company
  • Contacts
• Ensure:
  • Correct field mapping
  • No duplication

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Automate tier assignment and updates.

• Create company-based workflows
• Use property-based triggers
• Enable re-enrollment
• Assign tiers dynamically
• Add fallback:
  • Flag accounts with missing tier

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Continuously improve accuracy and performance.

• Track:
  • Conversion rate by tier
  • Deal size
  • Sales cycle
• Monitor
  • Data quality
  • Tier distribution
• Run
  • Monthly data reviews
  • Quarterly strategy updates

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