Customer Churn Predictor

​

Overview

​

What the Agent Needs to Accomplish

• Analyze customer usage patterns and engagement metrics
• Track support ticket frequency and sentiment
• Monitor billing and payment history
• Identify churn risk indicators
• Generate churn risk scores
• Recommend specific retention actions
• Track retention campaign effectiveness

​

How Pylar Helps

• Unified Customer Data: Combining usage, engagement, support, and billing data
• Real-time Analysis: Querying current customer behavior patterns
• Multi-Source Integration: Joining data from product analytics, CRM, support, and billing systems
• Pattern Recognition: SQL views identify churn indicators
• Actionable Insights: Generating retention recommendations based on data

​

Without Pylar vs With Pylar

​

Without Pylar

• ❌ Data scattered across multiple systems (analytics, CRM, support, billing)
• ❌ Complex ETL pipelines to combine data
• ❌ Difficult to get real-time churn risk scores
• ❌ Manual analysis of churn indicators
• ❌ No unified customer view
• ❌ Time-consuming to update models
• ❌ Limited ability to track retention actions

• 4-5 different data source integrations
• Complex data pipeline setup
• Custom ML model development
• Churn scoring infrastructure
• ~6-8 weeks development time

​

With Pylar

• ✅ Single endpoint for all customer data
• ✅ Real-time churn risk calculation
• ✅ Easy to update churn indicators
• ✅ Unified customer 360 view
• ✅ Built-in analytics on retention actions
• ✅ Simple query-based risk scoring

• Connect 4-5 data sources (1.5 hours)
• Create churn analysis views (3 hours)
• Build MCP tools with AI (1.5 hours)
• Connect to agent builder (15 minutes)
• Total: ~6-7 hours

​

Step-by-Step Implementation

​

Step 1: Connect Data Sources

1. Connect Product Analytics (Usage metrics, feature adoption, engagement)
2. Connect CRM (Account data, contract info, renewal dates)
3. Connect Support System (Ticket history, satisfaction scores)
4. Connect Billing System (Payment history, invoices, subscription status)

​

Step 2: Create Churn Analysis Views

CREATE VIEW customer_engagement_score AS
SELECT 
  c.customer_id,
  c.customer_name,
  c.subscription_tier,
  c.contract_start_date,
  c.contract_end_date,
  DATEDIFF(CURRENT_DATE, c.contract_end_date) as days_until_renewal,
  -- Usage metrics
  u.active_days_last_30,
  u.features_used_count,
  u.total_actions_last_30,
  u.last_activity_date,
  DATEDIFF(CURRENT_DATE, u.last_activity_date) as days_since_last_activity,
  -- Support metrics
  s.ticket_count_last_30,
  s.avg_satisfaction_score,
  s.avg_resolution_time_hours,
  -- Billing metrics
  b.total_invoices,
  b.payment_failures_count,
  b.days_overdue,
  -- Engagement score calculation
  CASE 
    WHEN u.active_days_last_30 >= 20 THEN 100
    WHEN u.active_days_last_30 >= 15 THEN 75
    WHEN u.active_days_last_30 >= 10 THEN 50
    WHEN u.active_days_last_30 >= 5 THEN 25
    ELSE 0
  END as usage_score,
  CASE 
    WHEN s.avg_satisfaction_score >= 4.5 THEN 100
    WHEN s.avg_satisfaction_score >= 4.0 THEN 75
    WHEN s.avg_satisfaction_score >= 3.5 THEN 50
    ELSE 25
  END as satisfaction_score
FROM crm.customers c
LEFT JOIN analytics.user_usage u ON c.customer_id = u.customer_id
LEFT JOIN support.ticket_summary s ON c.customer_id = s.customer_id
LEFT JOIN billing.payment_summary b ON c.customer_id = b.customer_id;

CREATE VIEW churn_risk_score AS
SELECT 
  e.*,
  -- Risk indicators
  CASE 
    WHEN e.days_since_last_activity > 30 THEN 1 ELSE 0
  END as low_activity_flag,
  CASE 
    WHEN e.ticket_count_last_30 > 5 THEN 1 ELSE 0
  END as high_support_flag,
  CASE 
    WHEN e.payment_failures_count > 0 THEN 1 ELSE 0
  END as payment_issue_flag,
  CASE 
    WHEN e.days_until_renewal < 30 THEN 1 ELSE 0
  END as renewal_approaching_flag,
  -- Churn risk score (0-100, higher = more risk)
  (
    (CASE WHEN e.days_since_last_activity > 30 THEN 30 ELSE 0 END) +
    (CASE WHEN e.active_days_last_30 < 10 THEN 25 ELSE 0 END) +
    (CASE WHEN e.ticket_count_last_30 > 5 THEN 20 ELSE 0 END) +
    (CASE WHEN e.payment_failures_count > 0 THEN 15 ELSE 0 END) +
    (CASE WHEN e.avg_satisfaction_score < 3.5 THEN 10 ELSE 0 END)
  ) as churn_risk_score,
  -- Risk level
  CASE 
    WHEN (
      (CASE WHEN e.days_since_last_activity > 30 THEN 30 ELSE 0 END) +
      (CASE WHEN e.active_days_last_30 < 10 THEN 25 ELSE 0 END) +
      (CASE WHEN e.ticket_count_last_30 > 5 THEN 20 ELSE 0 END) +
      (CASE WHEN e.payment_failures_count > 0 THEN 15 ELSE 0 END) +
      (CASE WHEN e.avg_satisfaction_score < 3.5 THEN 10 ELSE 0 END)
    ) >= 50 THEN 'High Risk'
    WHEN (
      (CASE WHEN e.days_since_last_activity > 30 THEN 30 ELSE 0 END) +
      (CASE WHEN e.active_days_last_30 < 10 THEN 25 ELSE 0 END) +
      (CASE WHEN e.ticket_count_last_30 > 5 THEN 20 ELSE 0 END) +
      (CASE WHEN e.payment_failures_count > 0 THEN 15 ELSE 0 END) +
      (CASE WHEN e.avg_satisfaction_score < 3.5 THEN 10 ELSE 0 END)
    ) >= 25 THEN 'Medium Risk'
    ELSE 'Low Risk'
  END as risk_level
FROM customer_engagement_score e;

CREATE VIEW retention_actions AS
SELECT 
  r.*,
  CASE 
    WHEN r.low_activity_flag = 1 THEN 'Send re-engagement email campaign'
    WHEN r.high_support_flag = 1 THEN 'Schedule success manager call'
    WHEN r.payment_issue_flag = 1 THEN 'Contact billing team for payment assistance'
    WHEN r.renewal_approaching_flag = 1 AND r.churn_risk_score > 30 THEN 'Offer renewal discount'
    WHEN r.avg_satisfaction_score < 3.5 THEN 'Request feedback and address concerns'
    ELSE 'Monitor closely'
  END as recommended_action,
  CASE 
    WHEN r.churn_risk_score >= 50 THEN 'Urgent'
    WHEN r.churn_risk_score >= 25 THEN 'High Priority'
    ELSE 'Normal'
  END as action_priority
FROM churn_risk_score r
WHERE r.churn_risk_score > 20;

​

Step 3: Create MCP Tools with AI

• Prompt: “Create a tool to get churn risk score for a customer by customer ID”
• AI generates: get_churn_risk(customer_id: string)

• Prompt: “Create a tool to list customers with high churn risk scores”
• AI generates: get_high_risk_customers(risk_level: string, limit: number)

• Prompt: “Create a tool to get recommended retention actions for at-risk customers”
• AI generates: get_retention_recommendations(customer_id: string)

• Prompt: “Create a tool to analyze churn patterns and identify common indicators”
• AI generates: analyze_churn_patterns(days_back: number, min_risk_score: number)

​

Step 4: Test and Publish

1. Test churn risk calculation
2. Verify high-risk customer identification
3. Validate retention recommendations
4. Publish tools
5. Connect to agent builder

​

Example Agent Interactions

​

Scenario 1: High-Risk Customer Identification

1. Calls get_high_risk_customers("High Risk", 50)
2. Analyzes results:
   • “I found 23 high-risk customers:
     • Acme Corp: 65 risk score, last active 42 days ago, 3 payment failures
     • TechCo: 58 risk score, 8 support tickets last month, low satisfaction
     • …”

​

Scenario 2: Retention Action Recommendation

1. Calls get_churn_risk("12345")
2. Calls get_retention_recommendations("12345")
3. Responds:
   • “Customer 12345 has a 72 churn risk score (High Risk)
   • Indicators: No activity for 38 days, 6 support tickets, payment failure
   • Recommended action: Schedule success manager call + offer renewal discount
   • Priority: Urgent”

​

Scenario 3: Churn Pattern Analysis

1. Calls analyze_churn_patterns(90, 50)
2. Analyzes patterns:
   • “Common churn indicators in last 90 days:
     1. 68% had no activity for 30+ days
     2. 45% had payment failures
     3. 52% had low satisfaction scores (less than 3.5)
     4. 38% had 5+ support tickets”

​

Outcomes

​

Churn Prevention

• Early Detection: 40% earlier identification of at-risk customers
• Retention Rate: 25% improvement in customer retention
• Proactive Actions: 3x increase in proactive retention outreach
• Churn Reduction: 30% reduction in overall churn rate

​

Data-Driven Decisions

• Risk Scoring: Automated, real-time churn risk scores
• Action Recommendations: Data-driven retention strategies
• Pattern Recognition: Identification of common churn indicators
• Effectiveness Tracking: Monitor retention campaign success

​

Business Impact

• Revenue Protection: Save $500K+ in annual revenue through retention
• Customer Lifetime Value: 20% increase in average CLV
• Customer Satisfaction: Improved satisfaction through proactive outreach
• Team Efficiency: 50% reduction in manual churn analysis time

​

Best Practices

1. Regular Monitoring: Check churn risk scores weekly
2. Action Follow-up: Track retention action effectiveness
3. Model Refinement: Update risk indicators based on outcomes
4. Communication: Integrate with CRM for automatic outreach
5. Analytics: Use Evals to track agent churn prediction accuracy

​

Next Steps

• Customer Support Agent Example - Build a support agent
• Sales Assistant Example - Analyze sales pipeline
• Revenue Operations Agent Example - Track revenue metrics