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Lightweight customer relationship management for tracking contacts, deals, and interactions
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Basé sur la classification professionnelle SOC
| name | crm-lite |
| description | Lightweight customer relationship management for tracking contacts, deals, and interactions |
Lightweight customer relationship management for tracking contacts, deals, and interactions. Simple, fast, no complex CRM software needed.
Trigger when user says:
Best for:
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]
# [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 existing contact:
**Which contact:** [Search by name/company]
**Field to update:** [Contact info/Status/Notes/Next follow-up]
**New value:** [Updated information]
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 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
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 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]
Set follow-up reminder:
**Contact:** [Name]
**Follow-up Date:** [When]
**Purpose:** [Why follow up]
**Type:** [Email/Call/Meeting]
**Notes:** [Context for follow-up]
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
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_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
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)
}
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"
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
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)
}
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)
}
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
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
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)
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]
# 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] - $[value] - [probability]% - [Days to Close]
## ⚠️ At Risk Deals (Stalled >21 days)
1. [Deal Name] - [Company] - Stalled for [days] days
2. [Deal Name] - [Company] - Stalled for [days] days
## 📅 Today's Follow-ups
- [ ] [Time] - [Contact] - [Purpose] - [Type]
- [ ] [Time] - [Contact] - [Purpose] - [Type]
## 💡 AI Recommendations
- Deal [Name] has high churn risk based on activity decline
- Schedule follow-up with [Contact] - not contacted in 30 days
- Consider upsell opportunity with [Company] - usage at 85%
# Weekly CRM Analysis - [Week]
## 📈 Performance
- **New Deals Added:** [count]
- **Deals Moved Forward:** [count]
- **Deals Closed Won:** [count] ($[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
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.
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?
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?
Keep Information Current
Qualify Before Investing
Build Relationships
Qualify Rigorously
Move Deals Forward
Learn from Every Deal
❌ 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
❌ 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
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]
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
Daily Agenda → CRM
Load daily agenda → Prioritize follow-ups → Update CRM tasks
Session Summary → CRM
End meeting → Generate session summary → Log interaction → Update deal
CRM → Project Status
Update deal status → Refresh project dashboard → Track overall progress
Level 1 (Guided):
Level 2 (Confident):
Level 3 (Anticipatory):
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"
PersonalOS skill: account-management
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