crm-lite

name	crm-lite
description	Lightweight customer relationship management for tracking contacts, deals, and interactions

CRM Lite

Lightweight customer relationship management for tracking contacts, deals, and interactions. Simple, fast, no complex CRM software needed.

When to Use This Skill

Trigger when user says:

• "add contact"
• "update contact"
• "track deal"
• "CRM" or "customer relationship"
• "follow up with [person]"
• "meeting notes for [client]"

Best for:

• Freelancers and consultants
• Small business owners
• Sales representatives
• Client management
• Lead tracking
• Follow-up reminders

---

Tier 1: Guided Tracking (Beginner)

Contact Management

Add New Contact

Let's add a new contact:

**Name:** [Full name]
**Company:** [Company name]
**Email:** [Email address]
**Phone:** [Phone number]
**Role/Title:** [Job title]
**Source:** [How you met - referral, conference, cold outreach]
**Status:** [Lead, Prospect, Customer, Past Customer]
**Notes:** [Initial notes]

Contact Template

# [Name] - [Company]

**Contact Details:**
- Email: [email]
- Phone: [phone]
- LinkedIn: [linkedin_url]
- Website: [company_website]

**Role/Title:** [title]
**Status:** [Lead/Prospect/Customer]
**Source:** [how you met]

**Interactions:**
- [Date] - [Type: Meeting/Call/Email] - [Brief notes]

**Next Follow-up:** [Date and action]

Update Contact

Update existing contact:

**Which contact:** [Search by name/company]
**Field to update:** [Contact info/Status/Notes/Next follow-up]
**New value:** [Updated information]

Deal Tracking

Add New Deal

Let's track a new deal:

**Deal Name:** [Brief description]
**Contact:** [Which contact]
**Value:** [Estimated value]
**Stage:** [Lead Discovery → Proposal → Negotiation → Closed Won/Lost]
**Probability:** [Estimated win percentage]
**Expected Close Date:** [Target date]
**Notes:** [Initial notes]

Deal Template

# [Deal Name]

**Contact:** [Name - Company]
**Value:** $[amount]
**Stage:** [Current stage]
**Probability:** [percentage]%
**Expected Close:** [date]

**Timeline:**
- [Date] - Created - Stage: Lead
- [Date] - Moved to Proposal
- [Date] - Negotiation started

**Notes:**
- [Key details about this deal]
- [Decision makers involved]
- [Competitors considered]
- [Pain points addressed]

**Next Steps:**
- [ ] Follow up on proposal
- [ ] Schedule demo
- [ ] Send contract for review

Interaction Tracking

Log Interaction

Log an interaction with a contact:

**Contact:** [Name]
**Type:** [Meeting/Call/Email/Video Conference]
**Date:** [Date of interaction]
**Duration:** [How long]
**Attendees:** [Who was involved]
**Purpose:** [Why you met]
**Outcome:** [What happened]
**Next Actions:** [Follow-up tasks]
**Next Follow-up Date:** [When to follow up]

Interaction Template

## Interaction Log - [Date]

**Contact:** [Name] - [Company]
**Type:** [Meeting/Call/Email]
**Duration:** [minutes]
**Attendees:** [You, Contact, Others]

**Purpose:**
[Brief description of why you met]

**Discussion Summary:**
- [Key point 1]
- [Key point 2]
- [Key point 3]

**Outcome:**
- [What was accomplished]
- [Any decisions made]
- [Items agreed upon]

**Next Actions:**
- [ ] [Action item] - [Owner] - [Due date]
- [ ] [Action item] - [Owner] - [Due date]

**Next Follow-up:** [Date and purpose]

Follow-Up Reminders

Set follow-up reminder:

**Contact:** [Name]
**Follow-up Date:** [When]
**Purpose:** [Why follow up]
**Type:** [Email/Call/Meeting]
**Notes:** [Context for follow-up]

---

Tier 2: Confident Management (Intermediate)

Advanced Contact Management

Contact Scoring

Lead Scoring Model:

Score contacts based on engagement:

Demographics (0-20 points):
- Job title match: +10
- Company size: 0-10
- Industry fit: +5

Engagement (0-30 points):
- Responded to outreach: +10
- Attended meeting: +15
- Requested demo: +20
- Multiple touchpoints: +5 each

Behavior (0-30 points):
- Visited pricing: +10
- Downloaded content: +15
- Referred by customer: +20
- Active in conversations: +10 each

Fit (0-20 points):
- Budget alignment: +10
- Timeline urgency: +10

Total Score: 0-100
- 0-30: Cold Lead
- 31-50: Warm Lead
- 51-70: Hot Lead
- 71-100: Ready to Close

Contact Segmentation

segments:
  high_value:
    criteria:
      - deal_value: >$50,000
      - company_size: 100+ employees
      - budget_approved: true
  
  quick_wins:
    criteria:
      - deal_value: $10,000-$25,000
      - decision_maker_involved: true
      - timeline: <30 days
  
  long_term:
    criteria:
      - deal_value: >$100,000
      - timeline: 6+ months
      - stakeholder_count: 3+
  
  at_risk:
    criteria:
      - days_since_contact: >30
      - stage_stuck_days: >21
      - engagement_declining: true

Pipeline Management

Deal Stage Progression

pipeline_stages:
  lead_discovery:
    avg_duration: 14 days
    conversion_rate: 25%
    key_actions:
      - Initial qualification call
      - Needs assessment
      - Budget qualification
    
  qualification:
    avg_duration: 21 days
    conversion_rate: 60%
    key_actions:
      - Stakeholder identification
      - Decision process mapping
      - Competitor analysis
    
  proposal:
    avg_duration: 28 days
    conversion_rate: 40%
    key_actions:
      - Solution presentation
      - Proposal delivery
      - Negotiation prep
    
  negotiation:
    avg_duration: 14 days
    conversion_rate: 70%
    key_actions:
      - Contract review
      - Terms negotiation
      - Legal review
    
  closed_won:
    actions:
      - Contract signing
      - Onboarding kickoff
      - Success plan setup

Pipeline Health Metrics

def calculate_pipeline_health(deals):
    """Calculate overall pipeline health score"""
    
    total_value = sum(d['value'] for d in deals)
    weighted_value = sum(d['value'] * (d['probability'] / 100) for d in deals)
    
    # Velocity (how fast deals move)
    avg_cycle_time = calculate_average(deals, 'days_in_pipeline')
    
    # Coverage (do we have enough in pipeline?)
    monthly_target = 50000
    pipeline_ratio = total_value / monthly_target
    
    # Conversion rates by stage
    conversion_rates = calculate_conversion_by_stage(deals)
    
    return {
        "total_pipeline": total_value,
        "weighted_pipeline": weighted_value,
        "pipeline_coverage": pipeline_ratio,
        "avg_deal_velocity": avg_cycle_time,
        "conversion_rates": conversion_rates,
        "health_score": calculate_health_score(pipeline_ratio, avg_cycle_time)
    }

Automated Follow-Up

Follow-Up Sequences

email_sequences:
  cold_outreach:
    - day: 0
      subject: "Quick question about [company]"
      template: "introduction"
    
    - day: 3
      subject: "Still thinking about it?"
      template: "follow_up_1"
    
    - day: 7
      subject: "Value for [company]"
      template: "value_prop"
    
    - day: 14
      subject: "Last attempt"
      template: "break_up"
  
  post_meeting:
    - day: 0
      subject: "Thanks for meeting - Recap & Next Steps"
      template: "meeting_recap"
    
    - day: 2
      subject: "Any questions?"
      template: "check_in"
    
    - day: 7
      subject: "Moving forward?"
      template: "progress_check"
  
  proposal_sent:
    - day: 0
      subject: "Proposal attached - Review requested"
      template: "proposal_sent"
    
    - day: 3
      subject: "Feedback on proposal?"
      template: "proposal_feedback"
    
    - day: 10
      subject: "Next steps for proposal"
      template: "proposal_next_steps"

Activity Automation

def auto_generate_follow_ups():
    """Generate follow-up tasks based on recent activity"""
    
    recent_interactions = get_interactions(last_days=3)
    
    follow_ups = []
    for interaction in recent_interactions:
        if interaction['type'] == 'meeting':
            # Send recap within 24 hours
            follow_ups.append({
                'contact': interaction['contact'],
                'type': 'email',
                'due_date': interaction['date'] + timedelta(hours=24),
                'purpose': 'meeting_recap',
                'template': 'post_meeting'
            })
        
        elif interaction['type'] == 'proposal_sent':
            # Follow up on proposal in 3 days
            follow_ups.append({
                'contact': interaction['contact'],
                'type': 'email',
                'due_date': interaction['date'] + timedelta(days=3),
                'purpose': 'proposal_feedback',
                'template': 'proposal_follow_up'
            })
    
    return follow_ups

Integration with Other Skills

• daily-agenda: Review follow-ups scheduled for today
• session-summary: Capture client insights after meetings
• project-status: Track deal progress in projects

---

Tier 3: Anticipatory Management (Advanced)

Predictive Analytics

Deal Prediction Model

def predict_deal_outcome(deal, historical_data):
    """Predict likelihood of closing a deal"""
    
    features = {
        'value': deal['value'],
        'days_in_stage': deal['days_in_current_stage'],
        'days_total': deal['days_in_pipeline'],
        'stages_completed': deal['stages_completed'],
        'touchpoints': deal['total_interactions'],
        'last_activity_days': days_since(deal['last_activity']),
        'contact_score': deal['contact']['lead_score'],
        'company_size': deal['contact']['company_size'],
        'competitor_involved': deal['competitors_present']
    }
    
    # Weighted scoring model
    score = (
        features['contact_score'] * 0.3 +
        (features['touchpoints'] / 10) * 0.2 +
        (features['stages_completed'] / 5) * 0.2 +
        (1 - features['days_in_stage'] / 60) * 0.15 +
        (1 if not features['competitor_involved'] else 0.5) * 0.15
    )
    
    # Adjust based on historical patterns
    historical_win_rate = get_historical_win_rate(similar_deals)
    adjusted_score = (score + historical_win_rate) / 2
    
    return {
        'win_probability': adjusted_score * 100,
        'confidence': calculate_confidence(features),
        'risk_factors': identify_risk_factors(features),
        'recommended_actions': generate_recommendations(features)
    }

Churn Prediction

def predict_customer_churn(customer):
    """Predict likelihood of customer churn"""
    
    activity_metrics = {
        'login_frequency': calculate_frequency(customer['logins'], days=30),
        'feature_usage': get_feature_usage_ratio(customer),
        'support_tickets': customer['open_tickets'],
        'payment_issues': customer['payment_failures'],
        'nps_score': customer['latest_nps'],
        'usage_decline': calculate_decline(customer['usage_history'])
    }
    
    churn_risk = (
        (activity_metrics['login_frequency'] < 2) * 0.3 +
        (activity_metrics['feature_usage'] < 0.3) * 0.2 +
        (activity_metrics['support_tickets'] > 3) * 0.2 +
        (activity_metrics['nps_score'] < 7) * 0.15 +
        (activity_metrics['usage_decline'] > 0.5) * 0.15
    )
    
    return {
        'churn_risk': churn_risk * 100,
        'risk_level': 'high' if churn_risk > 0.7 else 'medium' if churn_risk > 0.4 else 'low',
        'primary_drivers': identify_drivers(activity_metrics),
        'retention_actions': suggest_retention_actions(churn_risk)
    }

Advanced Segmentation

RFM Analysis (Recency, Frequency, Monetary)

def rfm_segmentation(customers):
    """Segment customers using RFM model"""
    
    for customer in customers:
        # Calculate scores
        recency_score = score_recency(days_since(customer['last_purchase']))
        frequency_score = score_frequency(customer['purchase_count'])
        monetary_score = score_monetary(customer['total_value'])
        
        # Combine scores
        customer['rfm_score'] = f"{recency_score}{frequency_score}{monetary_score}"
        customer['rfm_segment'] = determine_segment(customer['rfm_score'])
        
    segments = {
        'Champions': ['555', '554', '544', '545'],
        'Loyal Customers': ['543', '444', '445', '354', '355'],
        'Potential Loyalists': ['534', '533', '443', '434'],
        'Recent Customers': ['343', '344', '335', '345'],
        'Promising': ['335', '435', '325', '245'],
        'Needs Attention': ['155', '154', '144', '214'],
        'About to Sleep': ['331', '321', '312', '221'],
        'At Risk': ['255', '254', '245', '244'],
        'Cannot Lose Them': ['155', '154', '144'],
        'Hibernating': ['332', '223', '232', '231'],
        'Lost': ['111', '112', '121', '131']
    }
    
    return customers, segments

Lifecycle Segmentation

customer_lifecycle:
  new_customer:
    criteria:
      - days_as_customer: 0-90
      - purchases: 1-3
    strategy:
      - Onboarding communication
      - Feature education
      - Early success checks
      - Welcome offers
  
  growing_customer:
    criteria:
      - days_as_customer: 90-365
      - purchase_frequency: increasing
      - usage_rate: >50%
    strategy:
      - Upsell opportunities
      - Product expansion
      - Referral requests
      - Loyalty program
  
  mature_customer:
    criteria:
      - days_as_customer: 365+
      - purchase_frequency: stable
      - usage_rate: >75%
    strategy:
      - Advanced features
      - Premium offerings
      - Advocacy programs
      - Strategic partnership
  
  at_risk_customer:
    criteria:
      - activity_decline: >30%
      - support_tickets: increasing
      - nps_score: <7
    strategy:
      - Win-back campaigns
      - Discount offers
      - Personal outreach
      - Problem resolution
  
  churned_customer:
    criteria:
      - last_purchase: >180 days
      - active_subscription: false
    strategy:
      - Reactivation campaigns
      - Win-back offers
      - Exit interviews
      - Competitor analysis

Intelligent Automation

Smart Task Prioritization

def prioritize_sales_tasks(tasks, deals, contacts):
    """Prioritize sales tasks using multiple factors"""
    
    for task in tasks:
        # Base score from task type
        task_type_scores = {
            'call_prospect': 10,
            'demo': 9,
            'proposal': 8,
            'follow_up': 7,
            'email': 5,
            'admin': 2
        }
        task['base_score'] = task_type_scores.get(task['type'], 5)
        
        # Boost based on deal value
        related_deal = find_deal(task['deal_id'])
        if related_deal:
            value_boost = min(related_deal['value'] / 10000, 5)
            task['score'] += value_boost
        
        # Boost based on contact score
        contact = find_contact(task['contact_id'])
        if contact:
            contact_boost = contact['lead_score'] / 20
            task['score'] += contact_boost
        
        # Boost based on urgency (deadline approaching)
        if task['due_date']:
            days_until_due = (task['due_date'] - today).days
            urgency_boost = max(0, (7 - days_until_due) / 7) * 3
            task['score'] += urgency_boost
    
    # Sort by score and return
    return sorted(tasks, key=lambda x: x['score'], reverse=True)

Opportunity Matching

def find_similar_won_deals(current_deal, historical_deals):
    """Find similar deals that were won to predict success factors"""
    
    similarity_factors = {
        'industry': 0.3,
        'company_size': 0.2,
        'deal_size': 0.25,
        'pain_points': 0.15,
        'stakeholder_role': 0.1
    }
    
    similar_deals = []
    for historical in historical_deals:
        if historical['status'] != 'won':
            continue
        
        similarity = 0
        for factor, weight in similarity_factors.items():
            if current_deal[factor] == historical[factor]:
                similarity += weight
            elif isinstance(current_deal[factor], int) and isinstance(historical[factor], int):
                # Numeric similarity
                similarity += weight * (1 - abs(current_deal[factor] - historical[factor]) / max(current_deal[factor], historical[factor]))
        
        if similarity > 0.6:
            similar_deals.append({
                'deal': historical,
                'similarity': similarity,
                'success_factors': extract_success_factors(historical)
            })
    
    return sorted(similar_deals, key=lambda x: x['similarity'], reverse=True)[:5]

CRM Dashboards

Daily Dashboard

# Today's CRM Overview - [Date]

## 🎯 Key Metrics
- **Pipeline Value:** $[total_value]
- **Weighted Pipeline:** $[weighted_value] (based on win probability)
- **Active Deals:** [count]
- **Follow-ups Due Today:** [count]

## 📊 Pipeline Health

Stage	Deals	Value	Win Rate
Lead Discovery	[n]	$[value]	[rate]%
Qualification	[n]	$[value]	[rate]%
Proposal	[n]	$[value]	[rate]%
Negotiation	[n]	$[value]	[rate]%
Closed Won	[n]	$[value]	100%

## 🔥 Hot Opportunities (High Probability + Value)
1. [Deal Name] - [Company] - $[value] - [probability]% - [Days to Close]
2. [Deal Name] - [Company] - $[value] - [probability]% - [Days to Close]

## ⚠️ At Risk Deals (Stalled >21 days)
1. [Deal Name] - [Company] - Stalled for [days] days
2. [Deal Name] - [Company] - Stalled for [days] days

## 📅 Today's Follow-ups
- [ ] [Time] - [Contact] - [Purpose] - [Type]
- [ ] [Time] - [Contact] - [Purpose] - [Type]

## 💡 AI Recommendations
- Deal [Name] has high churn risk based on activity decline
- Schedule follow-up with [Contact] - not contacted in 30 days
- Consider upsell opportunity with [Company] - usage at 85%

Weekly Analysis

# Weekly CRM Analysis - [Week]

## 📈 Performance
- **New Deals Added:** [count]
- **Deals Moved Forward:** [count]
- **Deals Closed Won:** [count] ($[value])
- **Deals Closed Lost:** [count] ($[value])
- **Win Rate:** [rate]%

## 🔍 Pipeline Velocity
- **Average Deal Cycle:** [days] days (vs. [benchmark])
- **Stage Dwell Times:**
  - Lead Discovery: [days] days
  - Qualification: [days] days
  - Proposal: [days] days
  - Negotiation: [days] days

## 👥 Contact Activity
- **New Contacts:** [count]
- **Interactions Logged:** [count]
- **Meetings Held:** [count]
- **Follow-up Completion Rate:** [rate]%

## 🎯 Top Performing Sources
1. [Source] - [count] deals - $[value]
2. [Source] - [count] deals - $[value]
3. [Source] - [count] deals - $[value]

## 📊 Conversion Funnel

Lead Discovery → [count] (100%) ↓ Qualification → [count] ([rate]%) ↓ Proposal → [count] ([rate]%) ↓ Negotiation → [count] ([rate]%) ↓ Closed Won → [count] ([rate]%)

## 💡 Insights & Opportunities
- [AI-generated insight 1]
- [AI-generated insight 2]
- [AI-generated insight 3]

## 🎯 Next Week Focus
1. Follow up with [count] at-risk deals
2. Advance [count] deals to next stage
3. Target [source] for new opportunities

---

Examples

Example 1: Adding a New Contact

User: "Add a new contact for Sarah Johnson at TechCorp"

Tier 1 Response:

Let's add Sarah Johnson as a new contact:

**Contact Details:**
- **Name:** Sarah Johnson
- **Company:** TechCorp
- **Email:** [I'll need her email]
- **Phone:** [I'll need her phone]
- **Role/Title:** [What's her role?]
- **Source:** [How did you meet her?]
- **Status:** Lead

**Notes:**
[Any initial notes about this contact?]

Please provide the missing details, or I can create a basic contact now and you can update later.

Example 2: Tracking a Deal

User: "I just had a great meeting with Acme Corp. They want to move forward with our Enterprise plan - $50,000"

Tier 2 Response:

Great news! Let me create the deal for you:

## Deal: Acme Corp - Enterprise Plan

**Contact:** [Which contact at Acme Corp?]
**Value:** $50,000
**Stage:** Proposal (since they want to move forward)
**Probability:** 70% (they've expressed interest)
**Expected Close Date:** [When do you expect to close?]

**Decision Makers:**
- [Who are the key stakeholders?]
- [Who has final approval?]

**Competitors:**
- [Who else are they considering?]
- [What's our advantage?]

**Next Steps:**
- [ ] Send proposal
- [ ] Schedule demo (if needed)
- [ ] Contract review
- [ ] Negotiation

**Timeline Estimate:**
Based on similar $50k deals, expect 2-4 weeks from proposal to close.

Would you like me to:
1. Create the deal now with this information?
2. Update an existing contact/deal?
3. Log the meeting details as well?

Example 3: Pipeline Review

User: "Show me my pipeline health"

Tier 3 Response:

## Pipeline Health Report

### Overall Score: 7.2/10 ⚠️

**Strengths:**
✅ Good pipeline coverage (3.2x monthly target)
✅ Strong conversion in Proposal stage (75% win rate)
✅ Average deal velocity improving (45 days vs. 52 last month)

**Concerns:**
⚠️ Low win rate in Qualification stage (45% vs. 60% benchmark)
⚠️ 3 deals stalled in Negotiation >21 days
⚠️ Pipeline concentration: 60% of value in 2 deals (risk)

### Pipeline Overview

Stage	Deals	Value	Weighted	Velocity
Lead Discovery	8	$120,000	$18,000	12 days ⬇️
Qualification	5	$85,000	$25,500	24 days ⬆️
Proposal	4	$180,000	$108,000	28 days ➡️
Negotiation	3	$95,000	$76,000	21 days ⬆️
Closed Won	2	$75,000	$75,000	-
----------------	-----	----------	--------	--------
Total	22	$555,000	$302,500	25 days avg

### 🔥 Hot Opportunities
1. **Enterprise Deal - MegaCorp** - $75,000 - 85% - Close in 7 days
2. **SaaS License - TechStartup** - $45,000 - 75% - Close in 14 days
3. **Consulting - FinanceCorp** - $35,000 - 70% - Close in 21 days

### ⚠️ At Risk Deals
1. **Integration Project - DataCo** - Stalled 28 days in Negotiation
   - **Issue:** Legal review delays
   - **Action:** Schedule check-in with legal team
   
2. **Annual Contract - RetailInc** - Stalled 24 days in Qualification
   - **Issue:** Budget reallocation
   - **Action:** Confirm if deal still active

3. **Custom Dev - StartupXYZ** - Stalled 22 days in Proposal
   - **Issue:** Competitor price undercut
   - **Action:** Value-based selling conversation

### 💡 AI Recommendations

**Based on historical data, here's what I recommend:**

1. **Focus on stalled Negotiation deals** (DataCo)
   - 82% of deals stalled >21 days in Negotiation are lost
   - Win-back rate: 23% with proactive outreach
   - Recommended: Call decision-maker directly

2. **Improve Qualification win rate** (45% vs 60%)
   - Top reason for lost deals: No clear champion
   - Recommendation: Confirm stakeholder mapping before advancing

3. **Pipeline diversification**
   - Current risk: 60% of value in 2 deals
   - Recommendation: Increase Lead Discovery activity
   - Target: Add 5 new leads this week

4. **Predictive insight: MegaCorp deal**
   - Similar deals: 85% win rate in this stage
   - Risk factor: Competitor involved (reduces win rate to 72%)
   - Recommended: Emphasize unique value propositions

### 📊 This Week's Goals
- [ ] Move DataCo from Negotiation → Closed Won or Lost
- [ ] Advance 2 deals to next stage
- [ ] Add 5 new qualified leads to pipeline
- [ ] Follow up with 10 cold leads

Would you like me to:
1. Create a daily agenda with follow-up tasks?
2. Draft emails for at-risk deals?
3. Analyze a specific deal in detail?

---

Best Practices

Contact Management

1. Keep Information Current

   • Update contact info regularly
   • Log every interaction
   • Note role changes and job transitions

2. Qualify Before Investing

   • Use lead scoring to prioritize
   • Confirm budget and timeline early
   • Identify decision makers

3. Build Relationships

   • Add value beyond sales
   • Remember personal details
   • Follow up promptly

Deal Management

1. Qualify Rigorously

   • Use BANT framework (Budget, Authority, Need, Timeline)
   • Document all decision criteria
   • Validate win probability regularly

2. Move Deals Forward

   • Always have next steps
   • Set clear deadlines
   • Remove stalled deals

3. Learn from Every Deal

   • Document why deals are won/lost
   • Analyze patterns
   • Improve process

---

Common Mistakes

Contact Mistakes

❌ Incomplete Information: Missing email, phone, or company ✅ Complete Profiles: All key contact fields filled

❌ No Context: Just a name, no notes or history ✅ Rich Context: Interaction history, preferences, relationship details

❌ Not Updated: Old information, no recent activity ✅ Current: Regular updates, recent interactions logged

Deal Mistakes

❌ Optimistic Probability: Every deal at 80% ✅ Realistic Scoring: Probability based on evidence

❌ Stalled Deals Ignored: Deals sitting in same stage for months ✅ Active Management: Move forward or close

❌ No Next Steps: Deal exists but no action items ✅ Clear Path: Always have defined next steps

---

CRM Templates

Contact Database Structure

contacts:
  - id: unique_identifier
    name:
      first: string
      last: string
    company: string
    title: string
    contact_info:
      email: string
      phone: string
      linkedin: string
      website: string
    demographics:
      company_size: number
      industry: string
      location: string
    status: Lead|Prospect|Customer|PastCustomer
    source: string
    lead_score: number
    created_at: date
    updated_at: date
    tags: [string]
    notes: string
    interactions:
      - date: date
        type: string
        duration: minutes
        outcome: string
        next_actions: [string]

Deal Database Structure

deals:
  - id: unique_identifier
    name: string
    contact_id: reference
    value: number
    currency: string
    stage: string
    probability: number
    expected_close_date: date
    created_at: date
    closed_date: date
    status: Active|Won|Lost
    competitors: [string]
    decision_makers: [string]
    win_reason: string
    loss_reason: string
    timeline:
      - date: date
        stage: string
        notes: string
    notes: string

---

Integration with Other Skills

Related Skills

• daily-agenda: Review today's follow-ups and prioritize tasks
• session-summary: Capture client insights after meetings
• project-status: Track deal progress in project dashboards

Workflow Integration

1. Daily Agenda → CRM

   Load daily agenda → Prioritize follow-ups → Update CRM tasks
   

2. Session Summary → CRM

   End meeting → Generate session summary → Log interaction → Update deal
   

3. CRM → Project Status

   Update deal status → Refresh project dashboard → Track overall progress
   

---

Progressive Disclosure

Level 1 (Guided):

• Basic contact and deal tracking
• Simple interaction logging
• Manual follow-up reminders

Level 2 (Confident):

• Contact scoring and segmentation
• Pipeline health metrics
• Automated follow-up sequences

Level 3 (Anticipatory):

• Predictive analytics for deals
• Advanced segmentation (RFM, lifecycle)
• Intelligent task prioritization
• AI-powered recommendations

---

Session Tracking

After using this skill, update personal-os/adaptation/skill-levels.md:

crm-lite:
  level: 1
  sessions_used: 1
  last_used: [current_date]
  progression_notes: "Added first contact, tracked initial deal"