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Zoho CRM Scoring Rules: Automatically Prioritize Leads

Scoring Rules in Zoho CRM automatically assign points to leads — based on their properties and behavior. Sales reps can see at a glance which leads have the highest purchase probability and who to contact first.

What Is Lead Scoring?

Lead scoring assigns each lead a numerical score that reflects their relevance and readiness to buy. Typical scoring criteria:

  • Firmographic data: Company size, industry, region, budget
  • Behavior: Website visits, email opens, form submissions
  • CRM activities: Number of calls, booked appointments, demos

Create a Scoring Rule in Zoho CRM

  1. Go to Settings → Automation → Scoring Rules
  2. Click New Scoring Rule
  3. Select the module (e.g., Leads)
  4. Give the rule a name (e.g., “Lead Quality Score”)
  5. Click Next

Configure Scoring Criteria

In the editor, add individual scoring conditions:

  1. Click Add Criterion
  2. Choose field, operator, and value: e.g., “Industry = Software” → +20 points
  3. Choose whether points are added or deducted
  4. Repeat for additional criteria

Example scoring:

Criterion Points
Industry = Software or IT +20
Employees > 50 +15
Country = Germany, Austria, or Switzerland +10
Lead Source = Website Form +10
Email domain is @gmail.com (personal) –10
Phone is empty –5

View the Score in CRM

After saving the scoring rule, Zoho CRM automatically calculates the score for all existing and new leads. You can see the score:

  • In the lead record as its own field (by default in the top right)
  • In the lead list view as a column (add via column customization)
  • As a sort criterion — sort leads by score descending

Create Score-Based Views

Create a custom view for “Top Leads”:

  1. Go to the leads list
  2. Click Create View
  3. Add the filter: Score ≥ 40
  4. Sort by Score (descending)
  5. Save as “Top Leads” and share with the team

Combine Scoring with Zia

Zoho CRM’s AI assistant Zia also offers an AI-based conversion prediction. This can be shown alongside the manual score and provides a data-driven second perspective.

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