342 lines
8.8 KiB
Markdown
342 lines
8.8 KiB
Markdown
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# Lead Scoring Keywords
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General Bots includes native lead scoring capabilities through BASIC keywords, enabling automated lead qualification, AI-enhanced scoring, and CRM integration directly from conversational flows.
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<img src="../assets/gb-decorative-header.svg" alt="General Bots" style="max-height: 100px; width: 100%; object-fit: contain;">
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## Overview
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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.
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## SCORE LEAD
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Calculate a lead score based on profile and behavior data using configurable rules.
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### Syntax
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```basic
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score = SCORE LEAD lead_data
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```
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### Example
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```basic
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lead_data = NEW OBJECT
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lead_data.email = "john@company.com"
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lead_data.name = "John Smith"
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lead_data.company = "Acme Corp"
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lead_data.job_title = "VP of Engineering"
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lead_data.industry = "Technology"
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lead_data.company_size = "Enterprise"
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score = SCORE LEAD lead_data
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TALK "Score: " + score.score
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TALK "Grade: " + score.grade
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TALK "Status: " + score.status
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TALK "Top recommendation: " + score.recommendations[0]
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```
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### Return Object
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The `SCORE LEAD` keyword returns an object containing:
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| Property | Type | Description |
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|----------|------|-------------|
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| `score` | Integer | Numeric score (0-100) |
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| `grade` | String | Letter grade (A, B, C, D, F) |
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| `status` | String | hot, warm, cold, or unqualified |
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| `breakdown` | Object | Score components by category |
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| `recommendations` | Array | Suggested next actions |
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### Score Breakdown
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```basic
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score = SCORE LEAD lead_data
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TALK "Demographic score: " + score.breakdown.demographic
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TALK "Firmographic score: " + score.breakdown.firmographic
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TALK "Behavioral score: " + score.breakdown.behavioral
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TALK "Engagement score: " + score.breakdown.engagement
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```
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## AI SCORE LEAD
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Use AI/LLM-enhanced scoring for more nuanced lead evaluation.
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```basic
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score = AI SCORE LEAD lead_data
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TALK "AI Score: " + score.score
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TALK "Confidence: " + score.breakdown.ai_confidence
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TALK "Reasoning: " + score.breakdown.ai_reasoning
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```
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AI scoring considers factors that rule-based scoring might miss, such as company news, market conditions, and subtle signals in communication patterns.
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### When to Use AI Scoring
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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.
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```basic
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' Use AI for enterprise leads, rules for SMB
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IF lead_data.company_size = "Enterprise" THEN
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score = AI SCORE LEAD lead_data
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ELSE
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score = SCORE LEAD lead_data
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END IF
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```
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## GET LEAD SCORE
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Retrieve an existing lead score from the database.
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```basic
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score = GET LEAD SCORE "lead-id"
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TALK "Current score: " + score.score
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TALK "Last updated: " + score.updated_at
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```
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## QUALIFY LEAD
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Check if a lead meets the qualification threshold for sales handoff.
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### Default Threshold (70)
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```basic
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result = QUALIFY LEAD "lead-id"
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IF result.qualified THEN
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TALK "Lead is qualified: " + result.status
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' Notify sales team
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SEND MAIL TO "sales@company.com" SUBJECT "New Qualified Lead" BODY result
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ELSE
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TALK "Lead needs more nurturing. Score: " + result.score
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END IF
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```
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### Custom Threshold
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```basic
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' Enterprise deals require higher qualification
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result = QUALIFY LEAD "lead-id", 85
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IF result.qualified THEN
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TALK "Enterprise lead qualified for sales"
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END IF
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```
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### Qualification Result
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| Property | Type | Description |
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|----------|------|-------------|
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| `qualified` | Boolean | Meets threshold |
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| `score` | Integer | Current score |
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| `threshold` | Integer | Applied threshold |
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| `status` | String | Current lead status |
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| `gap` | Integer | Points needed if not qualified |
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## UPDATE LEAD SCORE
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Manually adjust a lead's score based on specific actions or behaviors.
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### Add Points
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```basic
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' Lead attended webinar
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new_score = UPDATE LEAD SCORE "lead-id", 10, "Attended product webinar"
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TALK "Score updated to: " + new_score.score
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```
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### Deduct Points
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```basic
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' Lead unsubscribed from newsletter
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new_score = UPDATE LEAD SCORE "lead-id", -15, "Unsubscribed from email"
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```
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### Behavioral Scoring
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```basic
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ON "webinar:attended"
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UPDATE LEAD SCORE params.lead_id, 15, "Webinar attendance"
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END ON
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ON "pricing:viewed"
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UPDATE LEAD SCORE params.lead_id, 20, "Viewed pricing page"
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END ON
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ON "demo:requested"
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UPDATE LEAD SCORE params.lead_id, 30, "Requested demo"
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END ON
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ON "email:bounced"
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UPDATE LEAD SCORE params.lead_id, -25, "Email bounced"
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END ON
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```
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## Complete Lead Nurturing Flow
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```basic
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' lead-nurturing.bas
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PARAM email AS string
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PARAM name AS string
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PARAM company AS string
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PARAM source AS string
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DESCRIPTION "Process and score new leads"
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' Build lead profile
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WITH lead
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.email = email
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.name = name
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.company = company
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.source = source
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.created_at = NOW()
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END WITH
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' Initial scoring
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score = SCORE LEAD lead
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' Store lead
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INSERT "leads", lead
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SET BOT MEMORY "lead_" + email + "_score", score.score
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' Route based on score
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IF score.status = "hot" THEN
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' Immediate sales notification
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SEND MAIL TO "sales@company.com" SUBJECT "Hot Lead: " + name BODY score
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SEND TEMPLATE "hot-lead-welcome", "email", email, #{name: name}
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ELSEIF score.status = "warm" THEN
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' Schedule nurture sequence
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SEND TEMPLATE "welcome", "email", email, #{name: name}
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SET SCHEDULE DATEADD(NOW(), 3, "day"), "nurture-day-3.bas"
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ELSE
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' Cold lead - educational content
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SEND TEMPLATE "educational", "email", email, #{name: name}
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END IF
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TALK "Lead " + name + " processed with score " + score.score + " (" + score.status + ")"
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```
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## Lead Scoring Configuration
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Configure scoring weights in your bot's `config.csv`:
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```csv
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key,value
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lead-score-job-title-weight,20
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lead-score-company-size-weight,15
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lead-score-industry-weight,10
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lead-score-engagement-weight,25
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lead-score-behavioral-weight,30
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lead-score-qualification-threshold,70
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```
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### Title-Based Scoring
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| Job Title Pattern | Points |
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|-------------------|--------|
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| C-Level (CEO, CTO, CFO) | 25 |
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| VP / Vice President | 20 |
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| Director | 15 |
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| Manager | 10 |
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| Individual Contributor | 5 |
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### Company Size Scoring
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| Company Size | Points |
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|--------------|--------|
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| Enterprise (1000+) | 20 |
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| Mid-Market (100-999) | 15 |
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| SMB (10-99) | 10 |
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| Small (1-9) | 5 |
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### Behavioral Actions
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| Action | Typical Points |
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|--------|---------------|
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| Demo request | +30 |
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| Pricing page view | +20 |
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| Case study download | +15 |
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| Webinar attendance | +15 |
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| Blog subscription | +10 |
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| Email open | +2 |
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| Email click | +5 |
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| Unsubscribe | -15 |
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| Email bounce | -25 |
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## Scheduled Score Decay
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Implement score decay for inactive leads:
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```basic
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' score-decay.bas
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SET SCHEDULE "every day at 2am"
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' Find leads with no activity in 30 days
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stale_leads = FIND "leads", "last_activity < DATEADD(NOW(), -30, 'day') AND score > 20"
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FOR EACH lead IN stale_leads
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UPDATE LEAD SCORE lead.id, -5, "Inactivity decay"
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NEXT lead
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TALK "Processed " + LEN(stale_leads) + " stale leads"
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```
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## Integration with CRM
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Push qualified leads to external CRM systems:
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```basic
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result = QUALIFY LEAD lead_id
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IF result.qualified THEN
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' Push to Salesforce
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crm_payload = NEW OBJECT
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crm_payload.email = lead.email
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crm_payload.name = lead.name
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crm_payload.score = result.score
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crm_payload.status = "Qualified"
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POST "https://api.salesforce.com/leads", crm_payload
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' Mark as synced
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UPDATE "leads", "id = " + lead_id, #{crm_synced: true, synced_at: NOW()}
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END IF
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```
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## Best Practices
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**Start with simple rules.** Begin with basic demographic and firmographic scoring, then add behavioral triggers as you gather data.
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**Align scoring with sales.** Work with your sales team to define what makes a "qualified" lead. Their input ensures scores reflect actual sales readiness.
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**Review and adjust regularly.** Analyze conversion rates by score range monthly. Adjust weights if high-scoring leads aren't converting.
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**Combine rule-based and AI scoring.** Use rule-based scoring for speed and consistency, AI scoring for complex enterprise deals requiring nuanced evaluation.
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**Implement score decay.** Leads that go cold should have their scores decrease over time to keep the pipeline accurate.
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**Track score history.** Store score changes with timestamps and reasons for audit trails and analysis.
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```basic
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' Log all score changes
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ON "lead:score:changed"
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INSERT "score_history", #{
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lead_id: params.lead_id,
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old_score: params.old_score,
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new_score: params.new_score,
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reason: params.reason,
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changed_at: NOW()
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}
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END ON
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```
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## See Also
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- [SEND TEMPLATE](./keywords.md) - Nurture campaign emails
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- [SET SCHEDULE](./keyword-set-schedule.md) - Automated scoring jobs
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- [ON Keyword](./keyword-on.md) - Event-driven score updates
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- [GET / POST](./keywords-http.md) - CRM integration
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