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Lead Scoring

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Customer Segmentation Analysis

Customer Segmentation Analysis

Customer Churn Predictor

Customer Churn Predictor

Social Media Sentiment Tracker

Social Media Sentiment Tracker

Personalised Product Recommendation

Personalised Product Recommendation

Call Transcript Analysis

Call Transcript Analysis

Customer Feedback Analysis

Customer Feedback Analysis

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Brand Voice Consistency Check

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RFP Analysis

CRM

Customer Churn Predictor

This customer retention agent task predicts and prevents customer churn through advanced behavioural analysis. The task configuration outlines how to:

  • Analyse historical customer interaction patterns and engagement metrics

  • Calculate individual customer churn probability scores

  • Identify early warning signals and risk indicators

  • Segment customers based on churn likelihood

  • Generate personalised retention recommendations


As a modular task, it integrates with CRM systems and analytics platforms, enabling AI agents to execute systematic churn prediction within existing customer success workflows. The task specification includes outputs for risk assessment, customer segmentation, and intervention planning, adaptable across various subscription-based services and recurring revenue business models.

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Example outputs:


{
  "churn_risk_summary": "Analysis indicates a moderate overall churn risk, with 15% of customers showing high risk of churning in the next 3 months.",
  "high_risk_customers": [
    "CUS001234",
    "CUS005678",
    "CUS009876",
    "CUS004321",
    "CUS007890"
  ],
  "risk_factors": [
    "Decreased product usage in the last 30 days",
    "Missed payments or billing issues",
    "Low engagement with customer support",
    "Negative feedback on recent interactions",
    "Lack of feature adoption"
  ],
  "engagement_metrics": "{\"login_frequency\": \"high impact\", \"feature_utilization\": \"medium impact\", \"support_ticket_resolution\": \"high impact\", \"email_open_rate\": \"low impact\"}",
  "behaviour_patterns": [
    "Sudden drop in login frequency",
    "Decreased usage of key features",
    "Increased number of support tickets",
    "Failure to engage with new product updates",
    "Reduced responsiveness to communications"
  ],
  "retention_strategies": [
    "Personalised re-engagement campaigns",
    "Proactive outreach from customer success team",
    "Offer of complimentary training or onboarding refresh",
    "Targeted discounts or promotions",
    "Solicitation of feedback and addressal of concerns"
  ],
  "churn_probability_distribution": "{\"0-20%\": \"45%\", \"21-40%\": \"25%\", \"41-60%\": \"15%\", \"61-80%\": \"10%\", \"81-100%\": \"5%\"}",
  "key_retention_drivers": [
    "Regular product usage",
    "Adoption of multiple features",
    "Positive customer support experiences",
    "Engagement with educational content",
    "Participation in community or events"
  ],
  "segment_analysis": "{\"New Customers\": \"Low churn risk\", \"Power Users\": \"Very low churn risk\", \"Infrequent Users\": \"High churn risk\", \"Enterprise Accounts\": \"Moderate churn risk\"}",
  "prediction_confidence": "High"

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Customer Segmentation Analysis

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Social Media Sentiment Tracker

Social Media Sentiment Tracker