Lead Scoring Keywords

General Bots includes native lead scoring capabilities through BASIC keywords, enabling automated lead qualification, AI-enhanced scoring, and CRM integration directly from conversational flows.

Overview

Lead scoring assigns numeric values to prospects based on their attributes and behaviors. Higher scores indicate greater sales readiness. General Bots provides both rule-based and AI-enhanced scoring approaches.

SCORE LEAD

Calculate a lead score based on profile and behavior data using configurable rules.

Syntax

score = SCORE LEAD lead_data

Example

lead_data = NEW OBJECT
lead_data.email = "john@company.com"
lead_data.name = "John Smith"
lead_data.company = "Acme Corp"
lead_data.job_title = "VP of Engineering"
lead_data.industry = "Technology"
lead_data.company_size = "Enterprise"

score = SCORE LEAD lead_data

TALK "Score: " + score.score
TALK "Grade: " + score.grade
TALK "Status: " + score.status
TALK "Top recommendation: " + score.recommendations[0]

Return Object

The SCORE LEAD keyword returns an object containing:

Score Breakdown

score = SCORE LEAD lead_data

TALK "Demographic score: " + score.breakdown.demographic
TALK "Firmographic score: " + score.breakdown.firmographic
TALK "Behavioral score: " + score.breakdown.behavioral
TALK "Engagement score: " + score.breakdown.engagement

AI SCORE LEAD

Use AI/LLM-enhanced scoring for more nuanced lead evaluation.

score = AI SCORE LEAD lead_data

TALK "AI Score: " + score.score
TALK "Confidence: " + score.breakdown.ai_confidence
TALK "Reasoning: " + score.breakdown.ai_reasoning

AI scoring considers factors that rule-based scoring might miss, such as company news, market conditions, and subtle signals in communication patterns.

When to Use AI Scoring

AI scoring works best for complex B2B scenarios where context matters significantly. Rule-based scoring is faster and sufficient for high-volume B2C leads with clear qualification criteria.

' Use AI for enterprise leads, rules for SMB
IF lead_data.company_size = "Enterprise" THEN
    score = AI SCORE LEAD lead_data
ELSE
    score = SCORE LEAD lead_data
END IF

GET LEAD SCORE

Retrieve an existing lead score from the database.

score = GET LEAD SCORE "lead-id"
TALK "Current score: " + score.score
TALK "Last updated: " + score.updated_at

QUALIFY LEAD

Check if a lead meets the qualification threshold for sales handoff.

Default Threshold (70)

result = QUALIFY LEAD "lead-id"
IF result.qualified THEN
    TALK "Lead is qualified: " + result.status
    ' Notify sales team
    SEND MAIL TO "sales@company.com" SUBJECT "New Qualified Lead" BODY result
ELSE
    TALK "Lead needs more nurturing. Score: " + result.score
END IF

Custom Threshold

' Enterprise deals require higher qualification
result = QUALIFY LEAD "lead-id", 85

IF result.qualified THEN
    TALK "Enterprise lead qualified for sales"
END IF

Qualification Result

UPDATE LEAD SCORE

Manually adjust a lead’s score based on specific actions or behaviors.

Add Points

' Lead attended webinar
new_score = UPDATE LEAD SCORE "lead-id", 10, "Attended product webinar"
TALK "Score updated to: " + new_score.score

Deduct Points

' Lead unsubscribed from newsletter
new_score = UPDATE LEAD SCORE "lead-id", -15, "Unsubscribed from email"

Behavioral Scoring

ON "webinar:attended"
    UPDATE LEAD SCORE params.lead_id, 15, "Webinar attendance"
END ON

ON "pricing:viewed"
    UPDATE LEAD SCORE params.lead_id, 20, "Viewed pricing page"
END ON

ON "demo:requested"
    UPDATE LEAD SCORE params.lead_id, 30, "Requested demo"
END ON

ON "email:bounced"
    UPDATE LEAD SCORE params.lead_id, -25, "Email bounced"
END ON

Complete Lead Nurturing Flow

' lead-nurturing.bas
PARAM email AS string
PARAM name AS string
PARAM company AS string
PARAM source AS string

DESCRIPTION "Process and score new leads"

' Build lead profile
WITH lead
    .email = email
    .name = name
    .company = company
    .source = source
    .created_at = NOW()
END WITH

' Initial scoring
score = SCORE LEAD lead

' Store lead
INSERT "leads", lead
SET BOT MEMORY "lead_" + email + "_score", score.score

' Route based on score
IF score.status = "hot" THEN
    ' Immediate sales notification
    SEND MAIL TO "sales@company.com" SUBJECT "Hot Lead: " + name BODY score
    SEND TEMPLATE "hot-lead-welcome", "email", email, #{name: name}
    
ELSEIF score.status = "warm" THEN
    ' Schedule nurture sequence
    SEND TEMPLATE "welcome", "email", email, #{name: name}
    SET SCHEDULE DATEADD(NOW(), 3, "day"), "nurture-day-3.bas"
    
ELSE
    ' Cold lead - educational content
    SEND TEMPLATE "educational", "email", email, #{name: name}
END IF

TALK "Lead " + name + " processed with score " + score.score + " (" + score.status + ")"

Lead Scoring Configuration

Configure scoring weights in your bot’s config.csv:

key,value
lead-score-job-title-weight,20
lead-score-company-size-weight,15
lead-score-industry-weight,10
lead-score-engagement-weight,25
lead-score-behavioral-weight,30
lead-score-qualification-threshold,70

Title-Based Scoring

Company Size Scoring

Behavioral Actions

Scheduled Score Decay

Implement score decay for inactive leads:

' score-decay.bas
SET SCHEDULE "every day at 2am"

' Find leads with no activity in 30 days
stale_leads = FIND "leads", "last_activity < DATEADD(NOW(), -30, 'day') AND score > 20"

FOR EACH lead IN stale_leads
    UPDATE LEAD SCORE lead.id, -5, "Inactivity decay"
NEXT lead

TALK "Processed " + LEN(stale_leads) + " stale leads"

Integration with CRM

Push qualified leads to external CRM systems:

result = QUALIFY LEAD lead_id

IF result.qualified THEN
    ' Push to Salesforce
    crm_payload = NEW OBJECT
    crm_payload.email = lead.email
    crm_payload.name = lead.name
    crm_payload.score = result.score
    crm_payload.status = "Qualified"
    
    POST "https://api.salesforce.com/leads", crm_payload
    
    ' Mark as synced
    UPDATE "leads", "id = " + lead_id, #{crm_synced: true, synced_at: NOW()}
END IF

Best Practices

Start with simple rules. Begin with basic demographic and firmographic scoring, then add behavioral triggers as you gather data.

Align scoring with sales. Work with your sales team to define what makes a “qualified” lead. Their input ensures scores reflect actual sales readiness.

Review and adjust regularly. Analyze conversion rates by score range monthly. Adjust weights if high-scoring leads aren’t converting.

Combine rule-based and AI scoring. Use rule-based scoring for speed and consistency, AI scoring for complex enterprise deals requiring nuanced evaluation.

Implement score decay. Leads that go cold should have their scores decrease over time to keep the pipeline accurate.

Track score history. Store score changes with timestamps and reasons for audit trails and analysis.

' Log all score changes
ON "lead:score:changed"
    INSERT "score_history", #{
        lead_id: params.lead_id,
        old_score: params.old_score,
        new_score: params.new_score,
        reason: params.reason,
        changed_at: NOW()
    }
END ON

See Also

• SEND TEMPLATE - Nurture campaign emails
• SET SCHEDULE - Automated scoring jobs
• ON Keyword - Event-driven score updates
• GET / POST - CRM integration