Lead Drop-off Re-engagement Workflow

Automated re-engagement workflow for stalled deals with multi-channel approach

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Workflow Information

ID: lead_dropoff_reengagement_workflow

Namespace: default

Version: 1.0

Created: 2025-07-01

Updated: 2025-07-01

Tasks: 17

Quick Actions

Manage Secrets

Inputs

Name	Type	Required	Default
`scan_interval_hours`	integer	Optional	`4`
`stall_threshold_days`	integer	Optional	`7`

Outputs

Name	Type	Source
`stalled_deals`	object	Stalled deals identified
`escalation_setup`	object	Escalation schedules created
`reengagement_results`	object	Re-engagement campaign results
`tracking_configuration`	object	Response tracking setup

Tasks

get_stalled_deals_from_crm

script

Scan CRM for deals that have been stalled

Python Script:

#!/usr/bin/env python3
import json
from datetime import datetime
from datetime import timedelta

print("🔍 Scanning CRM for stalled deals...")

# Mock stalled deals data
stalled_deals = {
    "total_stalled_deals": 3,
    "deals": [
        {
            "deal_id": "DEAL_2025_001100",
            "customer_id": "CUST_456",
            "customer_name": "InnovateTech Corp",
            "customer_phone": "+1234567890",
            "customer_email": "contact@innovatetech.com",
            "deal_name": "Enterprise Software License",
            "estimated_value": 125000.00,
            "probability": 0.60,
            "current_stage": "negotiation",
            "assigned_officer": "OFF_001",
            "created_at": "2025-05-15T10:00:00Z",
            "last_activity": "2025-06-18T16:30:00Z",
            "days_stalled": 8,
            "preferred_channel": "whatsapp"
        },
        {
            "deal_id": "DEAL_2025_001101",
            "customer_id": "CUST_457",
            "customer_name": "TechFlow Solutions",
            "customer_phone": "+1987654321",
            "customer_email": "admin@techflow.com",
            "deal_name": "CRM Implementation",
            "estimated_value": 87500.00,
            "probability": 0.45,
            "current_stage": "proposal",
            "assigned_officer": "OFF_002",
            "created_at": "2025-06-01T14:00:00Z",
            "last_activity": "2025-06-17T11:45:00Z",
            "days_stalled": 9,
            "preferred_channel": "email"
        },
        {
            "deal_id": "DEAL_2025_001102",
            "customer_id": "CUST_458",
            "customer_name": "Digital Dynamics",
            "customer_phone": "+1555123456",
            "customer_email": "sales@digitaldynamics.com",
            "deal_name": "Analytics Platform",
            "estimated_value": 95000.00,
            "probability": 0.55,
            "current_stage": "closing",
            "assigned_officer": "OFF_001",
            "created_at": "2025-05-20T09:30:00Z",
            "last_activity": "2025-06-16T15:20:00Z",
            "days_stalled": 10,
            "preferred_channel": "phone"
        }
    ]
}

print(f"✅ Found {stalled_deals['total_stalled_deals']} stalled deals")
for deal in stalled_deals['deals']:
    print(f"   • {deal['customer_name']}: ${deal['estimated_value']:,.0f} - {deal['days_stalled']} days stalled")

print(f"__OUTPUTS__ {json.dumps(stalled_deals)}")

Task Outputs: {'name': 'stalled_deals_data', 'value': 'Stalled deals retrieved from CRM'}

check_stalled_deals_found

script

Determine if any stalled deals need processing

Task Outputs: {'name': 'deals_check_result', 'value': 'Stalled deals check completed'}

get_customer_interaction_timeline

script

Retrieve interaction history for stalled deals

Python Script:

#!/usr/bin/env python3
import json
from datetime import datetime
from datetime import timedelta

print("📊 Retrieving customer interaction timelines...")

# Mock interaction timeline data
interaction_timelines = [
    {
        "customer_id": "CUST_456",
        "customer_name": "InnovateTech Corp",
        "total_interactions": 15,
        "touchpoint_history": [
            {
                "date": "2025-06-18T16:30:00Z",
                "type": "email",
                "direction": "outbound",
                "response_received": False
            },
            {
                "date": "2025-06-15T14:20:00Z",
                "type": "phone_call",
                "direction": "outbound",
                "response_received": False
            },
            {
                "date": "2025-06-12T10:15:00Z",
                "type": "meeting",
                "direction": "scheduled",
                "response_received": True,
                "outcome": "positive"
            }
        ],
        "response_patterns": {
            "response_rate": 0.33,
            "preferred_channels": ["whatsapp", "meeting"],
            "best_contact_times": ["10:00", "14:00"],
            "avg_response_time": "4.2 hours"
        }
    },
    {
        "customer_id": "CUST_457",
        "customer_name": "TechFlow Solutions",
        "total_interactions": 12,
        "response_patterns": {
            "response_rate": 0.58,
            "preferred_channels": ["email", "phone"],
            "best_contact_times": ["09:00", "15:00"],
            "avg_response_time": "2.8 hours"
        }
    },
    {
        "customer_id": "CUST_458",
        "customer_name": "Digital Dynamics",
        "total_interactions": 18,
        "response_patterns": {
            "response_rate": 0.44,
            "preferred_channels": ["phone", "meeting"],
            "best_contact_times": ["11:00", "16:00"],
            "avg_response_time": "6.1 hours"
        }
    }
]

print(f"✅ Retrieved interaction timelines for {len(interaction_timelines)} customers")
for timeline in interaction_timelines:
    print(f"   • {timeline['customer_name']}: {timeline['response_patterns']['response_rate']*100:.0f}% response rate")

print(f"__OUTPUTS__ {json.dumps(interaction_timelines)}")

Task Outputs: {'name': 'interaction_timelines', 'value': 'Customer interaction timelines retrieved'}

get_deal_stage_history

script

Analyze deal progression through stages

Python Script:

#!/usr/bin/env python3
import json

print("📈 Analyzing deal stage progression...")

# Mock deal stage history
deal_stage_histories = [
    {
        "deal_id": "DEAL_2025_001100",
        "customer_name": "InnovateTech Corp",
        "current_stage": "negotiation",
        "stage_progression": [
            {
                "stage": "qualified",
                "duration_days": 5,
                "activities_count": 8,
                "progression_smooth": True
            },
            {
                "stage": "proposal",
                "duration_days": 12,
                "activities_count": 15,
                "progression_smooth": True
            },
            {
                "stage": "negotiation",
                "duration_days": 15,
                "activities_count": 6,
                "progression_smooth": False
            }
        ],
        "bottlenecks": ["budget_approval", "technical_concerns"],
        "time_in_current_stage": 15,
        "typical_stage_duration": 8
    },
    {
        "deal_id": "DEAL_2025_001101",
        "customer_name": "TechFlow Solutions",
        "current_stage": "proposal",
        "time_in_current_stage": 18,
        "typical_stage_duration": 10,
        "bottlenecks": ["feature_requirements", "competitor_comparison"]
    },
    {
        "deal_id": "DEAL_2025_001102",
        "customer_name": "Digital Dynamics",
        "current_stage": "closing",
        "time_in_current_stage": 12,
        "typical_stage_duration": 5,
        "bottlenecks": ["contract_terms", "legal_review"]
    }
]

print(f"✅ Analyzed stage history for {len(deal_stage_histories)} deals")
for history in deal_stage_histories:
    print(f"   • {history['customer_name']}: {history['time_in_current_stage']} days in {history['current_stage']}")

print(f"__OUTPUTS__ {json.dumps(deal_stage_histories)}")

Task Outputs: {'name': 'stage_histories', 'value': 'Deal stage histories analyzed'}

get_competitor_intelligence

script

Gather market and competitor information

Task Outputs: {'name': 'competitor_data', 'value': 'Competitor intelligence gathered'}

analyze_stall_reasons

script

AI analysis of why deals have stalled

Python Script:

#!/usr/bin/env python3
import json

print("🤖 Analyzing stall reasons with AI...")

# Mock AI stall analysis
stall_analyses = [
    {
        "deal_id": "DEAL_2025_001100",
        "customer_name": "InnovateTech Corp",
        "primary_stall_reason": "budget_approval_delay",
        "confidence_score": 0.87,
        "contributing_factors": [
            {
                "factor": "end_of_quarter_budget_constraints",
                "weight": 0.35
            },
            {
                "factor": "multiple_stakeholder_approval_needed",
                "weight": 0.25
            },
            {
                "factor": "competitor_evaluation_in_progress",
                "weight": 0.20
            }
        ],
        "risk_level": "medium",
        "probability_of_loss": 0.35
    },
    {
        "deal_id": "DEAL_2025_001101",
        "customer_name": "TechFlow Solutions",
        "primary_stall_reason": "feature_gap_concerns",
        "confidence_score": 0.92,
        "risk_level": "high",
        "probability_of_loss": 0.45
    },
    {
        "deal_id": "DEAL_2025_001102",
        "customer_name": "Digital Dynamics",
        "primary_stall_reason": "contract_negotiation_complexity",
        "confidence_score": 0.78,
        "risk_level": "medium",
        "probability_of_loss": 0.28
    }
]

print(f"✅ Analyzed stall reasons for {len(stall_analyses)} deals")
for analysis in stall_analyses:
    print(f"   • {analysis['customer_name']}: {analysis['primary_stall_reason']} (Risk: {analysis['risk_level']})")

print(f"__OUTPUTS__ {json.dumps(stall_analyses)}")

Task Outputs: {'name': 'stall_analysis_results', 'value': 'Stall reasons analyzed'}

determine_reengagement_strategy

script

AI-powered strategy selection for re-engagement

Python Script:

#!/usr/bin/env python3
import json

print("🎯 Determining re-engagement strategies...")

# Mock strategy determination
reengagement_strategies = [
    {
        "deal_id": "DEAL_2025_001100",
        "customer_name": "InnovateTech Corp",
        "recommended_strategy": "value_reinforcement_with_urgency",
        "channel_priority": ["whatsapp", "email", "phone"],
        "message_strategy": "roi_focused_with_timeline_pressure",
        "timing_recommendation": "immediate",
        "success_probability": 0.68,
        "key_messages": [
            "Emphasize ROI and cost savings",
            "Create urgency with quarter-end pricing",
            "Address budget approval process"
        ]
    },
    {
        "deal_id": "DEAL_2025_001101",
        "customer_name": "TechFlow Solutions",
        "recommended_strategy": "feature_gap_resolution",
        "channel_priority": ["email", "phone", "meeting"],
        "message_strategy": "technical_solution_focused",
        "timing_recommendation": "within_24_hours",
        "success_probability": 0.58,
        "key_messages": [
            "Address specific feature concerns",
            "Offer technical deep-dive session",
            "Highlight recent product enhancements"
        ]
    },
    {
        "deal_id": "DEAL_2025_001102",
        "customer_name": "Digital Dynamics",
        "recommended_strategy": "executive_intervention",
        "channel_priority": ["phone", "meeting", "email"],
        "message_strategy": "relationship_based_with_flexibility",
        "timing_recommendation": "within_48_hours",
        "success_probability": 0.72,
        "key_messages": [
            "Involve executive leadership",
            "Offer contract flexibility",
            "Emphasize strategic partnership"
        ]
    }
]

print(f"✅ Strategies determined for {len(reengagement_strategies)} deals")
for strategy in reengagement_strategies:
    print(f"   • {strategy['customer_name']}: {strategy['recommended_strategy']} ({strategy['success_probability']*100:.0f}% success rate)")

print(f"__OUTPUTS__ {json.dumps(reengagement_strategies)}")

Task Outputs: {'name': 'reengagement_strategies', 'value': 'Re-engagement strategies determined'}

personalize_reengagement_message

script

Generate personalized messages for each deal

Python Script:

#!/usr/bin/env python3
import json

print("✍️ Personalizing re-engagement messages...")

# Mock personalized messages
personalized_messages = [
    {
        "deal_id": "DEAL_2025_001100",
        "customer_name": "InnovateTech Corp",
        "preferred_channel": "whatsapp",
        "personalized_message": "Hi there! I wanted to follow up on our Enterprise Software proposal. I understand budget approvals can take time, especially toward quarter-end. Our solution could save InnovateTech 25% on operational costs - would a quick 15-min call help address any remaining questions? We have special Q2 pricing available until June 30th.",
        "subject_line": "Quick question about your Enterprise Software decision",
        "call_to_action": "Schedule 15-minute call",
        "personalization_elements": [
            "company_name_mention",
            "budget_approval_acknowledgment",
            "specific_roi_metric",
            "urgency_with_deadline"
        ]
    },
    {
        "deal_id": "DEAL_2025_001101",
        "customer_name": "TechFlow Solutions",
        "preferred_channel": "email",
        "personalized_message": "Hi! I've been thinking about the feature requirements we discussed for your CRM implementation. Our development team has made some exciting enhancements that directly address the workflow automation concerns you mentioned. Would you be interested in a technical demo to see these new capabilities? I believe they'll exceed your expectations.",
        "subject_line": "New CRM features that address your workflow concerns",
        "call_to_action": "Schedule technical demo"
    },
    {
        "deal_id": "DEAL_2025_001102",
        "customer_name": "Digital Dynamics",
        "preferred_channel": "phone",
        "personalized_message": "I wanted to personally reach out about the Analytics Platform contract. I understand legal reviews can be complex, and we're committed to making this work for Digital Dynamics. Our CEO would be happy to join a call to discuss how we can structure this as a strategic partnership. When would be a good time to connect?",
        "subject_line": "Strategic partnership discussion - Analytics Platform",
        "call_to_action": "Schedule executive call"
    }
]

print(f"✅ Personalized messages created for {len(personalized_messages)} deals")
for message in personalized_messages:
    print(f"   • {message['customer_name']}: {message['preferred_channel']} message ready")

print(f"__OUTPUTS__ {json.dumps(personalized_messages)}")

Task Outputs: {'name': 'personalized_messages', 'value': 'Personalized messages generated'}

send_whatsapp_reengagement

script

Send re-engagement message via WhatsApp

Task Outputs: {'name': 'whatsapp_sent', 'value': 'WhatsApp re-engagement sent'}

send_email_reengagement

script

Send re-engagement message via Email

Task Outputs: {'name': 'email_sent', 'value': 'Email re-engagement sent'}

schedule_followup_call

script

Schedule follow-up call for phone preference

Task Outputs: {'name': 'call_scheduled', 'value': 'Follow-up call scheduled'}

update_deal_with_reengagement_activity

script

Log re-engagement activities in deal records

Task Outputs: {'name': 'deal_updates', 'value': 'Deals updated with re-engagement activities'}

create_sales_alert

script

Create alerts for sales officers about re-engagement

Task Outputs: {'name': 'sales_alerts', 'value': 'Sales alerts created'}

track_engagement_response

script

Monitor and track customer responses to re-engagement

Task Outputs: {'name': 'tracking_setup', 'value': 'Engagement response tracking configured'}

update_reengagement_success_metrics

script

Update analytics with campaign performance

Task Outputs: {'name': 'success_metrics', 'value': 'Re-engagement success metrics updated'}

schedule_escalation_if_no_response

script

Set up automatic escalation for non-responsive customers

Python Script:

#!/usr/bin/env python3
import json
from datetime import datetime
from datetime import timedelta

print("⏰ Scheduling escalations for non-responsive customers...")

# Mock escalation scheduling
escalation_schedules = [
    {
        "escalation_id": "ESC_2025_006789",
        "deal_id": "DEAL_2025_001100",
        "customer_name": "InnovateTech Corp",
        "escalation_type": "no_response_followup",
        "trigger_after_hours": 48,
        "scheduled_for": (datetime.now() + timedelta(hours=48)).isoformat(),
        "escalate_to": "MANAGER_001",
        "escalation_actions": [
            "manager_personal_outreach",
            "executive_involvement_consideration",
            "account_strategy_review"
        ],
        "deal_value": 125000.00,
        "auto_cancel_conditions": ["customer_responds", "meeting_scheduled"]
    },
    {
        "escalation_id": "ESC_2025_006790",
        "deal_id": "DEAL_2025_001101",
        "customer_name": "TechFlow Solutions",
        "trigger_after_hours": 72,
        "scheduled_for": (datetime.now() + timedelta(hours=72)).isoformat(),
        "escalate_to": "VP_SALES",
        "deal_value": 87500.00
    },
    {
        "escalation_id": "ESC_2025_006791",
        "deal_id": "DEAL_2025_001102",
        "customer_name": "Digital Dynamics",
        "trigger_after_hours": 24,
        "scheduled_for": (datetime.now() + timedelta(hours=24)).isoformat(),
        "escalate_to": "MANAGER_001",
        "deal_value": 95000.00,
        "priority": "high"
    }
]

print(f"✅ Scheduled {len(escalation_schedules)} escalations")
for escalation in escalation_schedules:
    print(f"   • {escalation['customer_name']}: {escalation['trigger_after_hours']}h escalation to {escalation['escalate_to']}")

print(f"__OUTPUTS__ {json.dumps(escalation_schedules)}")

Task Outputs: {'name': 'escalation_schedules', 'value': 'Escalations scheduled for non-responsive customers'}

end_no_action_needed

script

Complete workflow when no stalled deals found

Task Outputs: {'name': 'no_action_result', 'value': 'Workflow completed - no action needed'}

Triggers

Scheduled Trigger: Scheduled Stalled Deal Scan

Manual Trigger: Manual Re-engagement Trigger

Webhook Trigger: Deal Stall Detection
POST /webhook/deal-stalled

YAML Source

id: lead_dropoff_reengagement_workflow
name: Lead Drop-off Re-engagement Workflow
tasks:
- id: get_stalled_deals_from_crm
  name: Get Stalled Deals from CRM
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\nfrom\
    \ datetime import timedelta\n\nprint(\"\U0001F50D Scanning CRM for stalled deals...\"\
    )\n\n# Mock stalled deals data\nstalled_deals = {\n    \"total_stalled_deals\"\
    : 3,\n    \"deals\": [\n        {\n            \"deal_id\": \"DEAL_2025_001100\"\
    ,\n            \"customer_id\": \"CUST_456\",\n            \"customer_name\":\
    \ \"InnovateTech Corp\",\n            \"customer_phone\": \"+1234567890\",\n \
    \           \"customer_email\": \"contact@innovatetech.com\",\n            \"\
    deal_name\": \"Enterprise Software License\",\n            \"estimated_value\"\
    : 125000.00,\n            \"probability\": 0.60,\n            \"current_stage\"\
    : \"negotiation\",\n            \"assigned_officer\": \"OFF_001\",\n         \
    \   \"created_at\": \"2025-05-15T10:00:00Z\",\n            \"last_activity\":\
    \ \"2025-06-18T16:30:00Z\",\n            \"days_stalled\": 8,\n            \"\
    preferred_channel\": \"whatsapp\"\n        },\n        {\n            \"deal_id\"\
    : \"DEAL_2025_001101\",\n            \"customer_id\": \"CUST_457\",\n        \
    \    \"customer_name\": \"TechFlow Solutions\",\n            \"customer_phone\"\
    : \"+1987654321\",\n            \"customer_email\": \"admin@techflow.com\",\n\
    \            \"deal_name\": \"CRM Implementation\",\n            \"estimated_value\"\
    : 87500.00,\n            \"probability\": 0.45,\n            \"current_stage\"\
    : \"proposal\",\n            \"assigned_officer\": \"OFF_002\",\n            \"\
    created_at\": \"2025-06-01T14:00:00Z\",\n            \"last_activity\": \"2025-06-17T11:45:00Z\"\
    ,\n            \"days_stalled\": 9,\n            \"preferred_channel\": \"email\"\
    \n        },\n        {\n            \"deal_id\": \"DEAL_2025_001102\",\n    \
    \        \"customer_id\": \"CUST_458\",\n            \"customer_name\": \"Digital\
    \ Dynamics\",\n            \"customer_phone\": \"+1555123456\",\n            \"\
    customer_email\": \"sales@digitaldynamics.com\",\n            \"deal_name\": \"\
    Analytics Platform\",\n            \"estimated_value\": 95000.00,\n          \
    \  \"probability\": 0.55,\n            \"current_stage\": \"closing\",\n     \
    \       \"assigned_officer\": \"OFF_001\",\n            \"created_at\": \"2025-05-20T09:30:00Z\"\
    ,\n            \"last_activity\": \"2025-06-16T15:20:00Z\",\n            \"days_stalled\"\
    : 10,\n            \"preferred_channel\": \"phone\"\n        }\n    ]\n}\n\nprint(f\"\
    \u2705 Found {stalled_deals['total_stalled_deals']} stalled deals\")\nfor deal\
    \ in stalled_deals['deals']:\n    print(f\"   \u2022 {deal['customer_name']}:\
    \ ${deal['estimated_value']:,.0f} - {deal['days_stalled']} days stalled\")\n\n\
    print(f\"__OUTPUTS__ {json.dumps(stalled_deals)}\")\n"
  outputs:
  - name: stalled_deals_data
    value: Stalled deals retrieved from CRM
  packages:
  - json==2.0.9
  - datetime
  description: Scan CRM for deals that have been stalled
- id: check_stalled_deals_found
  name: Check if Stalled Deals Found
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\n# Mock check for stalled deals\n\
    total_deals = 3  # From get_stalled_deals_from_crm\n\nresult = {\n    \"stalled_deals_found\"\
    : total_deals > 0,\n    \"deals_count\": total_deals,\n    \"action_needed\":\
    \ total_deals > 0\n}\n\nif result[\"stalled_deals_found\"]:\n    print(f\"\U0001F6A8\
    \ {total_deals} stalled deals require re-engagement\")\nelse:\n    print(\"\u2705\
    \ No stalled deals found - no action needed\")\n\nprint(f\"__OUTPUTS__ {json.dumps(result)}\"\
    )\n"
  outputs:
  - name: deals_check_result
    value: Stalled deals check completed
  packages:
  - json==2.0.9
  depends_on:
  - get_stalled_deals_from_crm
  description: Determine if any stalled deals need processing
- id: get_customer_interaction_timeline
  name: Get Customer Interaction Timeline
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\nfrom\
    \ datetime import timedelta\n\nprint(\"\U0001F4CA Retrieving customer interaction\
    \ timelines...\")\n\n# Mock interaction timeline data\ninteraction_timelines =\
    \ [\n    {\n        \"customer_id\": \"CUST_456\",\n        \"customer_name\"\
    : \"InnovateTech Corp\",\n        \"total_interactions\": 15,\n        \"touchpoint_history\"\
    : [\n            {\n                \"date\": \"2025-06-18T16:30:00Z\",\n    \
    \            \"type\": \"email\",\n                \"direction\": \"outbound\"\
    ,\n                \"response_received\": False\n            },\n            {\n\
    \                \"date\": \"2025-06-15T14:20:00Z\",\n                \"type\"\
    : \"phone_call\",\n                \"direction\": \"outbound\",\n            \
    \    \"response_received\": False\n            },\n            {\n           \
    \     \"date\": \"2025-06-12T10:15:00Z\",\n                \"type\": \"meeting\"\
    ,\n                \"direction\": \"scheduled\",\n                \"response_received\"\
    : True,\n                \"outcome\": \"positive\"\n            }\n        ],\n\
    \        \"response_patterns\": {\n            \"response_rate\": 0.33,\n    \
    \        \"preferred_channels\": [\"whatsapp\", \"meeting\"],\n            \"\
    best_contact_times\": [\"10:00\", \"14:00\"],\n            \"avg_response_time\"\
    : \"4.2 hours\"\n        }\n    },\n    {\n        \"customer_id\": \"CUST_457\"\
    ,\n        \"customer_name\": \"TechFlow Solutions\",\n        \"total_interactions\"\
    : 12,\n        \"response_patterns\": {\n            \"response_rate\": 0.58,\n\
    \            \"preferred_channels\": [\"email\", \"phone\"],\n            \"best_contact_times\"\
    : [\"09:00\", \"15:00\"],\n            \"avg_response_time\": \"2.8 hours\"\n\
    \        }\n    },\n    {\n        \"customer_id\": \"CUST_458\",\n        \"\
    customer_name\": \"Digital Dynamics\",\n        \"total_interactions\": 18,\n\
    \        \"response_patterns\": {\n            \"response_rate\": 0.44,\n    \
    \        \"preferred_channels\": [\"phone\", \"meeting\"],\n            \"best_contact_times\"\
    : [\"11:00\", \"16:00\"],\n            \"avg_response_time\": \"6.1 hours\"\n\
    \        }\n    }\n]\n\nprint(f\"\u2705 Retrieved interaction timelines for {len(interaction_timelines)}\
    \ customers\")\nfor timeline in interaction_timelines:\n    print(f\"   \u2022\
    \ {timeline['customer_name']}: {timeline['response_patterns']['response_rate']*100:.0f}%\
    \ response rate\")\n\nprint(f\"__OUTPUTS__ {json.dumps(interaction_timelines)}\"\
    )\n"
  outputs:
  - name: interaction_timelines
    value: Customer interaction timelines retrieved
  packages:
  - json==2.0.9
  condition: ${deals_check_result.stalled_deals_found} == true
  depends_on:
  - check_stalled_deals_found
  description: Retrieve interaction history for stalled deals
- id: get_deal_stage_history
  name: Get Deal Stage History
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F4C8 Analyzing deal\
    \ stage progression...\")\n\n# Mock deal stage history\ndeal_stage_histories =\
    \ [\n    {\n        \"deal_id\": \"DEAL_2025_001100\",\n        \"customer_name\"\
    : \"InnovateTech Corp\",\n        \"current_stage\": \"negotiation\",\n      \
    \  \"stage_progression\": [\n            {\n                \"stage\": \"qualified\"\
    ,\n                \"duration_days\": 5,\n                \"activities_count\"\
    : 8,\n                \"progression_smooth\": True\n            },\n         \
    \   {\n                \"stage\": \"proposal\",\n                \"duration_days\"\
    : 12,\n                \"activities_count\": 15,\n                \"progression_smooth\"\
    : True\n            },\n            {\n                \"stage\": \"negotiation\"\
    ,\n                \"duration_days\": 15,\n                \"activities_count\"\
    : 6,\n                \"progression_smooth\": False\n            }\n        ],\n\
    \        \"bottlenecks\": [\"budget_approval\", \"technical_concerns\"],\n   \
    \     \"time_in_current_stage\": 15,\n        \"typical_stage_duration\": 8\n\
    \    },\n    {\n        \"deal_id\": \"DEAL_2025_001101\",\n        \"customer_name\"\
    : \"TechFlow Solutions\",\n        \"current_stage\": \"proposal\",\n        \"\
    time_in_current_stage\": 18,\n        \"typical_stage_duration\": 10,\n      \
    \  \"bottlenecks\": [\"feature_requirements\", \"competitor_comparison\"]\n  \
    \  },\n    {\n        \"deal_id\": \"DEAL_2025_001102\",\n        \"customer_name\"\
    : \"Digital Dynamics\",\n        \"current_stage\": \"closing\",\n        \"time_in_current_stage\"\
    : 12,\n        \"typical_stage_duration\": 5,\n        \"bottlenecks\": [\"contract_terms\"\
    , \"legal_review\"]\n    }\n]\n\nprint(f\"\u2705 Analyzed stage history for {len(deal_stage_histories)}\
    \ deals\")\nfor history in deal_stage_histories:\n    print(f\"   \u2022 {history['customer_name']}:\
    \ {history['time_in_current_stage']} days in {history['current_stage']}\")\n\n\
    print(f\"__OUTPUTS__ {json.dumps(deal_stage_histories)}\")\n"
  outputs:
  - name: stage_histories
    value: Deal stage histories analyzed
  packages:
  - json==2.0.9
  depends_on:
  - get_customer_interaction_timeline
  description: Analyze deal progression through stages
- id: get_competitor_intelligence
  name: Get Competitor Intelligence
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F50D Gathering competitor\
    \ intelligence...\")\n\n# Mock competitor intelligence\ncompetitor_intelligence\
    \ = {\n    \"market_conditions\": {\n        \"industry_growth\": 0.15,\n    \
    \    \"competitive_intensity\": 0.73,\n        \"market_sentiment\": \"positive\"\
    ,\n        \"seasonal_factor\": 1.08\n    },\n    \"competitor_activity\": [\n\
    \        {\n            \"competitor\": \"CompetitorA\",\n            \"recent_activities\"\
    : [\"pricing_reduction\", \"feature_enhancement\"],\n            \"market_share\"\
    : 0.28,\n            \"threat_level\": \"high\"\n        },\n        {\n     \
    \       \"competitor\": \"CompetitorB\",\n            \"recent_activities\": [\"\
    new_partnership\", \"customer_wins\"],\n            \"market_share\": 0.19,\n\
    \            \"threat_level\": \"medium\"\n        }\n    ],\n    \"urgency_triggers\"\
    : [\n        \"end_of_quarter_budgets\",\n        \"competitor_aggressive_pricing\"\
    ,\n        \"new_feature_launches\"\n    ],\n    \"market_events\": [\n      \
    \  {\n            \"event\": \"industry_conference\",\n            \"date\": \"\
    2025-07-15\",\n            \"impact\": \"high_visibility_opportunity\"\n     \
    \   }\n    ]\n}\n\nprint(f\"\u2705 Competitor intelligence gathered\")\nprint(f\"\
    \   Market growth: {competitor_intelligence['market_conditions']['industry_growth']*100:.1f}%\"\
    )\nprint(f\"   Competitive intensity: {competitor_intelligence['market_conditions']['competitive_intensity']*100:.0f}%\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(competitor_intelligence)}\")\n"
  outputs:
  - name: competitor_data
    value: Competitor intelligence gathered
  packages:
  - json==2.0.9
  depends_on:
  - get_deal_stage_history
  description: Gather market and competitor information
- id: analyze_stall_reasons
  name: Analyze Stall Reasons
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F916 Analyzing stall\
    \ reasons with AI...\")\n\n# Mock AI stall analysis\nstall_analyses = [\n    {\n\
    \        \"deal_id\": \"DEAL_2025_001100\",\n        \"customer_name\": \"InnovateTech\
    \ Corp\",\n        \"primary_stall_reason\": \"budget_approval_delay\",\n    \
    \    \"confidence_score\": 0.87,\n        \"contributing_factors\": [\n      \
    \      {\n                \"factor\": \"end_of_quarter_budget_constraints\",\n\
    \                \"weight\": 0.35\n            },\n            {\n           \
    \     \"factor\": \"multiple_stakeholder_approval_needed\",\n                \"\
    weight\": 0.25\n            },\n            {\n                \"factor\": \"\
    competitor_evaluation_in_progress\",\n                \"weight\": 0.20\n     \
    \       }\n        ],\n        \"risk_level\": \"medium\",\n        \"probability_of_loss\"\
    : 0.35\n    },\n    {\n        \"deal_id\": \"DEAL_2025_001101\",\n        \"\
    customer_name\": \"TechFlow Solutions\",\n        \"primary_stall_reason\": \"\
    feature_gap_concerns\",\n        \"confidence_score\": 0.92,\n        \"risk_level\"\
    : \"high\",\n        \"probability_of_loss\": 0.45\n    },\n    {\n        \"\
    deal_id\": \"DEAL_2025_001102\",\n        \"customer_name\": \"Digital Dynamics\"\
    ,\n        \"primary_stall_reason\": \"contract_negotiation_complexity\",\n  \
    \      \"confidence_score\": 0.78,\n        \"risk_level\": \"medium\",\n    \
    \    \"probability_of_loss\": 0.28\n    }\n]\n\nprint(f\"\u2705 Analyzed stall\
    \ reasons for {len(stall_analyses)} deals\")\nfor analysis in stall_analyses:\n\
    \    print(f\"   \u2022 {analysis['customer_name']}: {analysis['primary_stall_reason']}\
    \ (Risk: {analysis['risk_level']})\")\n\nprint(f\"__OUTPUTS__ {json.dumps(stall_analyses)}\"\
    )\n"
  outputs:
  - name: stall_analysis_results
    value: Stall reasons analyzed
  packages:
  - json==2.0.9
  depends_on:
  - get_competitor_intelligence
  description: AI analysis of why deals have stalled
- id: determine_reengagement_strategy
  name: Determine Re-engagement Strategy
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F3AF Determining re-engagement\
    \ strategies...\")\n\n# Mock strategy determination\nreengagement_strategies =\
    \ [\n    {\n        \"deal_id\": \"DEAL_2025_001100\",\n        \"customer_name\"\
    : \"InnovateTech Corp\",\n        \"recommended_strategy\": \"value_reinforcement_with_urgency\"\
    ,\n        \"channel_priority\": [\"whatsapp\", \"email\", \"phone\"],\n     \
    \   \"message_strategy\": \"roi_focused_with_timeline_pressure\",\n        \"\
    timing_recommendation\": \"immediate\",\n        \"success_probability\": 0.68,\n\
    \        \"key_messages\": [\n            \"Emphasize ROI and cost savings\",\n\
    \            \"Create urgency with quarter-end pricing\",\n            \"Address\
    \ budget approval process\"\n        ]\n    },\n    {\n        \"deal_id\": \"\
    DEAL_2025_001101\",\n        \"customer_name\": \"TechFlow Solutions\",\n    \
    \    \"recommended_strategy\": \"feature_gap_resolution\",\n        \"channel_priority\"\
    : [\"email\", \"phone\", \"meeting\"],\n        \"message_strategy\": \"technical_solution_focused\"\
    ,\n        \"timing_recommendation\": \"within_24_hours\",\n        \"success_probability\"\
    : 0.58,\n        \"key_messages\": [\n            \"Address specific feature concerns\"\
    ,\n            \"Offer technical deep-dive session\",\n            \"Highlight\
    \ recent product enhancements\"\n        ]\n    },\n    {\n        \"deal_id\"\
    : \"DEAL_2025_001102\",\n        \"customer_name\": \"Digital Dynamics\",\n  \
    \      \"recommended_strategy\": \"executive_intervention\",\n        \"channel_priority\"\
    : [\"phone\", \"meeting\", \"email\"],\n        \"message_strategy\": \"relationship_based_with_flexibility\"\
    ,\n        \"timing_recommendation\": \"within_48_hours\",\n        \"success_probability\"\
    : 0.72,\n        \"key_messages\": [\n            \"Involve executive leadership\"\
    ,\n            \"Offer contract flexibility\",\n            \"Emphasize strategic\
    \ partnership\"\n        ]\n    }\n]\n\nprint(f\"\u2705 Strategies determined\
    \ for {len(reengagement_strategies)} deals\")\nfor strategy in reengagement_strategies:\n\
    \    print(f\"   \u2022 {strategy['customer_name']}: {strategy['recommended_strategy']}\
    \ ({strategy['success_probability']*100:.0f}% success rate)\")\n\nprint(f\"__OUTPUTS__\
    \ {json.dumps(reengagement_strategies)}\")\n"
  outputs:
  - name: reengagement_strategies
    value: Re-engagement strategies determined
  packages:
  - json==2.0.9
  depends_on:
  - analyze_stall_reasons
  description: AI-powered strategy selection for re-engagement
- id: personalize_reengagement_message
  name: Personalize Re-engagement Message
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\u270D\uFE0F Personalizing\
    \ re-engagement messages...\")\n\n# Mock personalized messages\npersonalized_messages\
    \ = [\n    {\n        \"deal_id\": \"DEAL_2025_001100\",\n        \"customer_name\"\
    : \"InnovateTech Corp\",\n        \"preferred_channel\": \"whatsapp\",\n     \
    \   \"personalized_message\": \"Hi there! I wanted to follow up on our Enterprise\
    \ Software proposal. I understand budget approvals can take time, especially toward\
    \ quarter-end. Our solution could save InnovateTech 25% on operational costs -\
    \ would a quick 15-min call help address any remaining questions? We have special\
    \ Q2 pricing available until June 30th.\",\n        \"subject_line\": \"Quick\
    \ question about your Enterprise Software decision\",\n        \"call_to_action\"\
    : \"Schedule 15-minute call\",\n        \"personalization_elements\": [\n    \
    \        \"company_name_mention\",\n            \"budget_approval_acknowledgment\"\
    ,\n            \"specific_roi_metric\",\n            \"urgency_with_deadline\"\
    \n        ]\n    },\n    {\n        \"deal_id\": \"DEAL_2025_001101\",\n     \
    \   \"customer_name\": \"TechFlow Solutions\",\n        \"preferred_channel\"\
    : \"email\",\n        \"personalized_message\": \"Hi! I've been thinking about\
    \ the feature requirements we discussed for your CRM implementation. Our development\
    \ team has made some exciting enhancements that directly address the workflow\
    \ automation concerns you mentioned. Would you be interested in a technical demo\
    \ to see these new capabilities? I believe they'll exceed your expectations.\"\
    ,\n        \"subject_line\": \"New CRM features that address your workflow concerns\"\
    ,\n        \"call_to_action\": \"Schedule technical demo\"\n    },\n    {\n  \
    \      \"deal_id\": \"DEAL_2025_001102\",\n        \"customer_name\": \"Digital\
    \ Dynamics\",\n        \"preferred_channel\": \"phone\",\n        \"personalized_message\"\
    : \"I wanted to personally reach out about the Analytics Platform contract. I\
    \ understand legal reviews can be complex, and we're committed to making this\
    \ work for Digital Dynamics. Our CEO would be happy to join a call to discuss\
    \ how we can structure this as a strategic partnership. When would be a good time\
    \ to connect?\",\n        \"subject_line\": \"Strategic partnership discussion\
    \ - Analytics Platform\",\n        \"call_to_action\": \"Schedule executive call\"\
    \n    }\n]\n\nprint(f\"\u2705 Personalized messages created for {len(personalized_messages)}\
    \ deals\")\nfor message in personalized_messages:\n    print(f\"   \u2022 {message['customer_name']}:\
    \ {message['preferred_channel']} message ready\")\n\nprint(f\"__OUTPUTS__ {json.dumps(personalized_messages)}\"\
    )\n"
  outputs:
  - name: personalized_messages
    value: Personalized messages generated
  packages:
  - json==2.0.9
  depends_on:
  - determine_reengagement_strategy
  description: Generate personalized messages for each deal
- id: send_whatsapp_reengagement
  name: Send WhatsApp Re-engagement
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\U0001F4F1 Sending WhatsApp re-engagement message...\")\n\n# Mock WhatsApp\
    \ sending\nwhatsapp_result = {\n    \"message_id\": \"wamid.HBgLMTIzNDU2Nzg5MDE_reeng...\"\
    ,\n    \"customer_phone\": \"+1234567890\",\n    \"customer_name\": \"InnovateTech\
    \ Corp\",\n    \"message_sent\": True,\n    \"sent_at\": datetime.now().isoformat(),\n\
    \    \"delivery_status\": \"sent\",\n    \"campaign_id\": \"REENG_WA_001\",\n\
    \    \"estimated_delivery\": \"immediate\",\n    \"tracking_enabled\": True,\n\
    \    \"response_expected\": True\n}\n\nprint(f\"\u2705 WhatsApp message sent to\
    \ {whatsapp_result['customer_name']}\")\nprint(f\"   Message ID: {whatsapp_result['message_id']}\"\
    )\nprint(f\"   Status: {whatsapp_result['delivery_status']}\")\n\nprint(f\"__OUTPUTS__\
    \ {json.dumps(whatsapp_result)}\")\n"
  outputs:
  - name: whatsapp_sent
    value: WhatsApp re-engagement sent
  packages:
  - json==2.0.9
  condition: ${personalized_messages[0].preferred_channel} == 'whatsapp'
  depends_on:
  - personalize_reengagement_message
  description: Send re-engagement message via WhatsApp
- id: send_email_reengagement
  name: Send Email Re-engagement
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\U0001F4E7 Sending Email re-engagement message...\")\n\n# Mock email sending\n\
    email_result = {\n    \"message_id\": \"MSG_2025_067891\",\n    \"customer_email\"\
    : \"admin@techflow.com\",\n    \"customer_name\": \"TechFlow Solutions\",\n  \
    \  \"subject\": \"New CRM features that address your workflow concerns\",\n  \
    \  \"sent_at\": datetime.now().isoformat(),\n    \"delivery_status\": \"sent\"\
    ,\n    \"campaign_id\": \"REENG_EMAIL_001\",\n    \"tracking_enabled\": True,\n\
    \    \"read_receipt_requested\": True,\n    \"estimated_delivery\": \"immediate\"\
    \n}\n\nprint(f\"\u2705 Email sent to {email_result['customer_name']}\")\nprint(f\"\
    \   Subject: {email_result['subject']}\")\nprint(f\"   Message ID: {email_result['message_id']}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(email_result)}\")\n"
  outputs:
  - name: email_sent
    value: Email re-engagement sent
  packages:
  - json==2.0.9
  condition: ${personalized_messages[1].preferred_channel} == 'email'
  depends_on:
  - personalize_reengagement_message
  description: Send re-engagement message via Email
- id: schedule_followup_call
  name: Schedule Follow-up Call
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\nfrom\
    \ datetime import timedelta\n\nprint(\"\U0001F4DE Scheduling follow-up call...\"\
    )\n\n# Mock call scheduling\ncall_schedule = {\n    \"task_id\": \"CALL_2025_001789\"\
    ,\n    \"customer_name\": \"Digital Dynamics\",\n    \"customer_phone\": \"+1555123456\"\
    ,\n    \"assigned_to\": \"OFF_001\",\n    \"call_type\": \"reengagement_executive\"\
    ,\n    \"scheduled_for\": (datetime.now() + timedelta(hours=24)).isoformat(),\n\
    \    \"priority\": \"high\",\n    \"call_purpose\": \"Contract negotiation resolution\"\
    ,\n    \"expected_duration\": 30,\n    \"executive_involvement\": True,\n    \"\
    preparation_notes\": \"Review contract terms, prepare flexibility options\"\n\
    }\n\nprint(f\"\u2705 Call scheduled for {call_schedule['customer_name']}\")\n\
    print(f\"   Scheduled: {call_schedule['scheduled_for']}\")\nprint(f\"   Type:\
    \ {call_schedule['call_type']}\")\n\nprint(f\"__OUTPUTS__ {json.dumps(call_schedule)}\"\
    )\n"
  outputs:
  - name: call_scheduled
    value: Follow-up call scheduled
  packages:
  - json==2.0.9
  condition: ${personalized_messages[2].preferred_channel} == 'phone'
  depends_on:
  - personalize_reengagement_message
  description: Schedule follow-up call for phone preference
- id: update_deal_with_reengagement_activity
  name: Update Deal with Re-engagement Activity
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime, timedelta\n\
    \nprint(\"\U0001F4DD Updating deals with re-engagement activities...\")\n\n# Mock\
    \ deal updates\ndeal_updates = [\n    {\n        \"deal_id\": \"DEAL_2025_001100\"\
    ,\n        \"customer_name\": \"InnovateTech Corp\",\n        \"activity_type\"\
    : \"ai_reengagement\",\n        \"channel_used\": \"whatsapp\",\n        \"activity_logged\"\
    : True,\n        \"updated_at\": datetime.now().isoformat(),\n        \"next_follow_up\"\
    : (datetime.now() + timedelta(days=3)).isoformat(),\n        \"reengagement_attempt\"\
    : 1,\n        \"campaign_id\": \"REENG_WA_001\"\n    },\n    {\n        \"deal_id\"\
    : \"DEAL_2025_001101\",\n        \"customer_name\": \"TechFlow Solutions\",\n\
    \        \"activity_type\": \"ai_reengagement\",\n        \"channel_used\": \"\
    email\",\n        \"activity_logged\": True,\n        \"updated_at\": datetime.now().isoformat(),\n\
    \        \"campaign_id\": \"REENG_EMAIL_001\"\n    },\n    {\n        \"deal_id\"\
    : \"DEAL_2025_001102\",\n        \"customer_name\": \"Digital Dynamics\",\n  \
    \      \"activity_type\": \"ai_reengagement\",\n        \"channel_used\": \"phone_scheduled\"\
    ,\n        \"activity_logged\": True,\n        \"updated_at\": datetime.now().isoformat(),\n\
    \        \"call_scheduled\": True\n    }\n]\n\nprint(f\"\u2705 Updated {len(deal_updates)}\
    \ deals with re-engagement activities\")\nfor update in deal_updates:\n    print(f\"\
    \   \u2022 {update['customer_name']}: {update['channel_used']} activity logged\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(deal_updates)}\")\n"
  outputs:
  - name: deal_updates
    value: Deals updated with re-engagement activities
  packages:
  - json==2.0.9
  depends_on:
  - send_whatsapp_reengagement
  - send_email_reengagement
  - schedule_followup_call
  description: Log re-engagement activities in deal records
- id: create_sales_alert
  name: Create Sales Alert
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\U0001F6A8 Creating sales alerts...\")\n\n# Mock sales alerts\nsales_alerts\
    \ = [\n    {\n        \"alert_id\": \"ALERT_2025_004567\",\n        \"officer_id\"\
    : \"OFF_001\",\n        \"alert_type\": \"reengagement_sent\",\n        \"severity\"\
    : \"medium\",\n        \"deal_id\": \"DEAL_2025_001100\",\n        \"customer_name\"\
    : \"InnovateTech Corp\",\n        \"message\": \"Re-engagement WhatsApp sent to\
    \ InnovateTech Corp. Deal stalled 8 days, $125K value. Monitor for response.\"\
    ,\n        \"created_at\": datetime.now().isoformat(),\n        \"auto_escalate\"\
    : True,\n        \"escalate_after_hours\": 48,\n        \"recommended_actions\"\
    : [\n            \"monitor_whatsapp_response\",\n            \"prepare_followup_call\"\
    \n        ]\n    },\n    {\n        \"alert_id\": \"ALERT_2025_004568\",\n   \
    \     \"officer_id\": \"OFF_002\",\n        \"alert_type\": \"reengagement_sent\"\
    ,\n        \"severity\": \"high\",\n        \"deal_id\": \"DEAL_2025_001101\"\
    ,\n        \"customer_name\": \"TechFlow Solutions\",\n        \"message\": \"\
    Re-engagement email sent to TechFlow Solutions. Technical demo required. $87.5K\
    \ value.\",\n        \"created_at\": datetime.now().isoformat()\n    }\n]\n\n\
    print(f\"\u2705 Created {len(sales_alerts)} sales alerts\")\nfor alert in sales_alerts:\n\
    \    print(f\"   \u2022 {alert['customer_name']}: {alert['severity']} priority\
    \ alert\")\n\nprint(f\"__OUTPUTS__ {json.dumps(sales_alerts)}\")\n"
  outputs:
  - name: sales_alerts
    value: Sales alerts created
  packages:
  - json==2.0.9
  depends_on:
  - update_deal_with_reengagement_activity
  description: Create alerts for sales officers about re-engagement
- id: track_engagement_response
  name: Track Engagement Response
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\U0001F4CA Setting up engagement response tracking...\")\n\n# Mock tracking\
    \ setup\ntracking_setup = {\n    \"tracking_active\": True,\n    \"campaigns_monitored\"\
    : 3,\n    \"tracking_configs\": [\n        {\n            \"campaign_id\": \"\
    REENG_WA_001\",\n            \"customer_id\": \"CUST_456\",\n            \"channel\"\
    : \"whatsapp\",\n            \"tracking_duration_hours\": 72,\n            \"\
    response_indicators\": [\"message_read\", \"reply_received\", \"call_requested\"\
    ],\n            \"auto_escalation\": True\n        },\n        {\n           \
    \ \"campaign_id\": \"REENG_EMAIL_001\",\n            \"customer_id\": \"CUST_457\"\
    ,\n            \"channel\": \"email\",\n            \"tracking_duration_hours\"\
    : 48,\n            \"response_indicators\": [\"email_opened\", \"link_clicked\"\
    , \"reply_received\"]\n        }\n    ],\n    \"tracking_metrics\": {\n      \
    \  \"response_rate_target\": 0.35,\n        \"engagement_score_threshold\": 0.60,\n\
    \        \"success_indicators\": [\"meeting_scheduled\", \"proposal_requested\"\
    , \"positive_sentiment\"]\n    }\n}\n\nprint(f\"\u2705 Tracking setup for {tracking_setup['campaigns_monitored']}\
    \ campaigns\")\nprint(f\"   Target response rate: {tracking_setup['tracking_metrics']['response_rate_target']*100:.0f}%\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(tracking_setup)}\")\n"
  outputs:
  - name: tracking_setup
    value: Engagement response tracking configured
  packages:
  - json==2.0.9
  depends_on:
  - create_sales_alert
  description: Monitor and track customer responses to re-engagement
- id: update_reengagement_success_metrics
  name: Update Re-engagement Success Metrics
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\U0001F4C8 Updating re-engagement success metrics...\")\n\n# Mock metrics\
    \ update\nmetrics_update = {\n    \"campaign_batch_id\": \"BATCH_2025_06_25\"\
    ,\n    \"campaigns_sent\": 3,\n    \"total_revenue_at_risk\": 307500.00,\n   \
    \ \"channels_used\": {\n        \"whatsapp\": 1,\n        \"email\": 1,\n    \
    \    \"phone_scheduled\": 1\n    },\n    \"expected_metrics\": {\n        \"response_rate\"\
    : 0.33,\n        \"conversion_rate\": 0.25,\n        \"revenue_recovery_rate\"\
    : 0.28\n    },\n    \"historical_performance\": {\n        \"campaigns_this_month\"\
    : 48,\n        \"success_rate\": 0.31,\n        \"avg_response_time\": \"3.2 hours\"\
    ,\n        \"revenue_recovered_month\": 1250000.00,\n        \"roi\": 15.2\n \
    \   },\n    \"updated_at\": datetime.now().isoformat(),\n    \"next_analysis_scheduled\"\
    : \"2025-06-28T09:00:00Z\"\n}\n\nprint(f\"\u2705 Metrics updated for batch {metrics_update['campaign_batch_id']}\"\
    )\nprint(f\"   Revenue at risk: ${metrics_update['total_revenue_at_risk']:,.0f}\"\
    )\nprint(f\"   Expected recovery rate: {metrics_update['expected_metrics']['revenue_recovery_rate']*100:.0f}%\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(metrics_update)}\")\n"
  outputs:
  - name: success_metrics
    value: Re-engagement success metrics updated
  packages:
  - json==2.0.9
  depends_on:
  - track_engagement_response
  description: Update analytics with campaign performance
- id: schedule_escalation_if_no_response
  name: Schedule Escalation if No Response
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\nfrom\
    \ datetime import timedelta\n\nprint(\"\u23F0 Scheduling escalations for non-responsive\
    \ customers...\")\n\n# Mock escalation scheduling\nescalation_schedules = [\n\
    \    {\n        \"escalation_id\": \"ESC_2025_006789\",\n        \"deal_id\":\
    \ \"DEAL_2025_001100\",\n        \"customer_name\": \"InnovateTech Corp\",\n \
    \       \"escalation_type\": \"no_response_followup\",\n        \"trigger_after_hours\"\
    : 48,\n        \"scheduled_for\": (datetime.now() + timedelta(hours=48)).isoformat(),\n\
    \        \"escalate_to\": \"MANAGER_001\",\n        \"escalation_actions\": [\n\
    \            \"manager_personal_outreach\",\n            \"executive_involvement_consideration\"\
    ,\n            \"account_strategy_review\"\n        ],\n        \"deal_value\"\
    : 125000.00,\n        \"auto_cancel_conditions\": [\"customer_responds\", \"meeting_scheduled\"\
    ]\n    },\n    {\n        \"escalation_id\": \"ESC_2025_006790\",\n        \"\
    deal_id\": \"DEAL_2025_001101\",\n        \"customer_name\": \"TechFlow Solutions\"\
    ,\n        \"trigger_after_hours\": 72,\n        \"scheduled_for\": (datetime.now()\
    \ + timedelta(hours=72)).isoformat(),\n        \"escalate_to\": \"VP_SALES\",\n\
    \        \"deal_value\": 87500.00\n    },\n    {\n        \"escalation_id\": \"\
    ESC_2025_006791\",\n        \"deal_id\": \"DEAL_2025_001102\",\n        \"customer_name\"\
    : \"Digital Dynamics\",\n        \"trigger_after_hours\": 24,\n        \"scheduled_for\"\
    : (datetime.now() + timedelta(hours=24)).isoformat(),\n        \"escalate_to\"\
    : \"MANAGER_001\",\n        \"deal_value\": 95000.00,\n        \"priority\": \"\
    high\"\n    }\n]\n\nprint(f\"\u2705 Scheduled {len(escalation_schedules)} escalations\"\
    )\nfor escalation in escalation_schedules:\n    print(f\"   \u2022 {escalation['customer_name']}:\
    \ {escalation['trigger_after_hours']}h escalation to {escalation['escalate_to']}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(escalation_schedules)}\")\n"
  outputs:
  - name: escalation_schedules
    value: Escalations scheduled for non-responsive customers
  packages:
  - json==2.0.9
  depends_on:
  - update_reengagement_success_metrics
  description: Set up automatic escalation for non-responsive customers
- id: end_no_action_needed
  name: End - No Action Needed
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\nfrom\
    \ datetime import timedelta\n\nprint(\"\u2705 No stalled deals found - No action\
    \ needed\")\n\nresult = {\n    \"workflow_outcome\": \"no_action_needed\",\n \
    \   \"stalled_deals_found\": 0,\n    \"next_scan_scheduled\": (datetime.now()\
    \ + timedelta(hours=4)).isoformat(),\n    \"completed_at\": datetime.now().isoformat(),\n\
    \    \"message\": \"All deals are progressing normally\"\n}\n\nprint(f\"Next scan\
    \ scheduled for: {result['next_scan_scheduled']}\")\nprint(f\"__OUTPUTS__ {json.dumps(result)}\"\
    )\n"
  outputs:
  - name: no_action_result
    value: Workflow completed - no action needed
  packages:
  - json==2.0.9
  condition: ${deals_check_result.stalled_deals_found} == false
  depends_on:
  - check_stalled_deals_found
  description: Complete workflow when no stalled deals found
inputs:
- name: scan_interval_hours
  type: integer
  default: 4
  description: Hours between scans for stalled deals
- name: stall_threshold_days
  type: integer
  default: 7
  description: Days of inactivity to consider deal stalled
outputs:
  stalled_deals:
    type: object
    source: get_stalled_deals_from_crm
    description: Stalled deals identified
  escalation_setup:
    type: object
    source: schedule_escalation_if_no_response
    description: Escalation schedules created
  reengagement_results:
    type: object
    source: update_deal_with_reengagement_activity
    description: Re-engagement campaign results
  tracking_configuration:
    type: object
    source: track_engagement_response
    description: Response tracking setup
version: '1.0'
triggers:
- name: Scheduled Stalled Deal Scan
  type: scheduled
  schedule: 0 */4 * * *
  description: Scan for stalled deals every 4 hours
- name: Manual Re-engagement Trigger
  type: manual
  description: Manually trigger re-engagement workflow
- name: Deal Stall Detection
  path: /webhook/deal-stalled
  type: webhook
  config:
    method: POST
    headers:
    - name: Content-Type
      value: application/json
      required: trueim
    authentication: none
  description: Triggered when deal stall is detected
description: Automated re-engagement workflow for stalled deals with multi-channel
  approach

Execution ID	Status	Started	Duration	Actions
`19e01f3e...`	COMPLETED	2025-07-01 18:21:47	N/A	View
`3f9ecdc2...`	COMPLETED	2025-07-01 18:19:40	N/A	View
`9a410720...`	COMPLETED	2025-07-01 18:11:58	N/A	View
`7252fa91...`	COMPLETED	2025-07-01 17:48:55	N/A	View