Lead Management & Qualification Workflow

id: lead_qualification_workflow
name: Lead Management & Qualification Workflow
tasks:
- id: get_lead_data_from_crm
  name: Get Lead Data from CRM
  type: script
  script: "#!/usr/bin/env python3\nimport json\nimport os\n\nprint(\"\U0001F50D Retrieving\
    \ lead data from CRM...\")\n\n# Mock CRM API response\nlead_data = {\n    \"lead_id\"\
    : \"LEAD_12345\",\n    \"name\": \"John Smith\",\n    \"email\": \"john.smith@techcorp.com\"\
    ,\n    \"phone\": \"+1234567890\",\n    \"company\": \"TechCorp Industries\",\n\
    \    \"industry\": \"Technology\",\n    \"company_size\": \"500-1000\",\n    \"\
    inquiry_source\": \"website_form\",\n    \"inquiry_details\": \"Interested in\
    \ CRM solutions for 500+ employees. Looking for implementation timeline and pricing\"\
    ,\n    \"created_at\": \"2025-06-25T14:30:00Z\",\n    \"budget_range\": \"50k-100k\"\
    ,\n    \"decision_timeline\": \"Q3_2025\",\n    \"contact_level\": \"director\"\
    \n}\n\nprint(f\"\u2705 Lead data retrieved: {lead_data['name']} from {lead_data['company']}\"\
    )\nprint(f\"   Industry: {lead_data['industry']}\")\nprint(f\"   Company Size:\
    \ {lead_data['company_size']}\")\nprint(f\"   Budget Range: {lead_data['budget_range']}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(lead_data)}\")\n"
  outputs:
  - name: lead_info
    value: Lead data retrieved from CRM
  packages:
  - requests==2.31.0
  - json==2.0.9
  description: Retrieve lead information from CRM system
- id: get_historical_data_from_bigquery
  name: Get Historical Data from BigQuery
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F4CA Querying historical\
    \ data from BigQuery...\")\n\n# Mock BigQuery response\nhistorical_data = [\n\
    \    {\n        \"customer_id\": \"CUST_001\",\n        \"company_name\": \"Similar\
    \ Tech Corp\",\n        \"total_revenue\": 125000.00,\n        \"engagement_score\"\
    : 0.85,\n        \"conversion_outcome\": \"won\",\n        \"industry\": \"Technology\"\
    ,\n        \"company_size\": \"500-1000\",\n        \"sales_cycle_days\": 45\n\
    \    },\n    {\n        \"customer_id\": \"CUST_002\", \n        \"company_name\"\
    : \"Tech Solutions Inc\",\n        \"total_revenue\": 95000.00,\n        \"engagement_score\"\
    : 0.78,\n        \"conversion_outcome\": \"won\",\n        \"industry\": \"Technology\"\
    ,\n        \"company_size\": \"500-1000\",\n        \"sales_cycle_days\": 52\n\
    \    }\n]\n\nprint(f\"\u2705 Found {len(historical_data)} similar customers\"\
    )\nprint(f\"   Average deal size: ${sum(c['total_revenue'] for c in historical_data)\
    \ / len(historical_data):,.2f}\")\n\nprint(f\"__OUTPUTS__ {json.dumps(historical_data)}\"\
    )\n"
  outputs:
  - name: historical_data
    value: Historical customer data retrieved
  packages:
  - json==2.0.9
  depends_on:
  - get_lead_data_from_crm
  description: Query historical customer data for analysis
- id: get_similar_leads_from_dynamics
  name: Get Similar Leads from Dynamics
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F504 Retrieving similar\
    \ leads from Dynamics 365...\")\n\n# Mock Dynamics 365 response\nsimilar_leads\
    \ = {\n    \"total_similar_leads\": 25,\n    \"conversion_rate\": 0.68,\n    \"\
    average_deal_size\": 78500.00,\n    \"average_sales_cycle\": 52,\n    \"leads\"\
    : [\n        {\n            \"leadid\": \"12345678-1234-1234-1234-123456789abc\"\
    ,\n            \"companyname\": \"Enterprise Tech Solutions\",\n            \"\
    industry\": \"Technology\",\n            \"company_size\": \"500-1000\",\n   \
    \         \"conversion_result\": \"won\",\n            \"deal_value\": 85000.00,\n\
    \            \"days_to_conversion\": 45\n        }\n    ],\n    \"success_factors\"\
    : [\n        \"enterprise_focus\",\n        \"technology_industry\", \n      \
    \  \"qualified_budget\"\n    ]\n}\n\nprint(f\"\u2705 Found {similar_leads['total_similar_leads']}\
    \ similar leads\")\nprint(f\"   Conversion rate: {similar_leads['conversion_rate']*100:.1f}%\"\
    )\nprint(f\"   Average deal size: ${similar_leads['average_deal_size']:,.2f}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(similar_leads)}\")\n"
  outputs:
  - name: similar_leads_data
    value: Similar leads data retrieved from Dynamics
  packages:
  - json==2.0.9
  depends_on:
  - get_historical_data_from_bigquery
  description: Retrieve similar leads data for benchmarking
- id: get_sales_officer_workload
  name: Get Sales Officer Workload
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F465 Checking sales\
    \ officers workload...\")\n\n# Mock sales officers data\nofficers_workload = [\n\
    \    {\n        \"officer_id\": \"OFF_001\",\n        \"name\": \"Sarah Johnson\"\
    ,\n        \"active_deals\": 15,\n        \"pipeline_value\": 850000.00,\n   \
    \     \"capacity_utilization\": 0.60,\n        \"success_rate\": 0.72,\n     \
    \   \"specialization\": [\"technology\", \"enterprise\"],\n        \"availability_score\"\
    : 0.85\n    },\n    {\n        \"officer_id\": \"OFF_002\",\n        \"name\"\
    : \"Mike Chen\",\n        \"active_deals\": 12,\n        \"pipeline_value\": 650000.00,\n\
    \        \"capacity_utilization\": 0.48,\n        \"success_rate\": 0.68,\n  \
    \      \"specialization\": [\"technology\", \"mid-market\"],\n        \"availability_score\"\
    : 0.92\n    }\n]\n\nprint(f\"\u2705 Retrieved workload for {len(officers_workload)}\
    \ sales officers\")\nfor officer in officers_workload:\n    print(f\"   {officer['name']}:\
    \ {officer['capacity_utilization']*100:.0f}% capacity, {officer['success_rate']*100:.0f}%\
    \ success rate\")\n\nprint(f\"__OUTPUTS__ {json.dumps(officers_workload)}\")\n"
  outputs:
  - name: officers_data
    value: Sales officers workload data retrieved
  packages:
  - json==2.0.9
  depends_on:
  - get_similar_leads_from_dynamics
  description: Check sales officers availability and capacity
- id: calculate_lead_score
  name: Calculate Lead Score
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F916 Calculating AI\
    \ lead score...\")\n\n# Mock AI scoring calculation\nlead_scoring = {\n    \"\
    lead_score\": 85.7,\n    \"confidence_level\": 0.92,\n    \"score_grade\": \"\
    A\",\n    \"score_percentile\": 88,\n    \"scoring_breakdown\": {\n        \"\
    company_fit\": 22.5,\n        \"intent_signals\": 20.0,\n        \"contact_quality\"\
    : 18.5,\n        \"historical_performance\": 15.2,\n        \"market_timing\"\
    : 9.5\n    },\n    \"predictions\": {\n        \"conversion_probability\": 0.73,\n\
    \        \"estimated_deal_value\": 87500.00,\n        \"predicted_sales_cycle\"\
    : 48\n    },\n    \"qualification_factors\": [\n        \"budget_confirmed\",\n\
    \        \"authority_verified\", \n        \"need_established\",\n        \"timeline_defined\"\
    \n    ]\n}\n\nprint(f\"\u2705 Lead score calculated: {lead_scoring['lead_score']:.1f}/100\"\
    )\nprint(f\"   Grade: {lead_scoring['score_grade']}\")\nprint(f\"   Conversion\
    \ probability: {lead_scoring['predictions']['conversion_probability']*100:.0f}%\"\
    )\nprint(f\"   Estimated deal value: ${lead_scoring['predictions']['estimated_deal_value']:,.2f}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(lead_scoring)}\")\n"
  outputs:
  - name: lead_score_result
    value: Lead scoring completed
  packages:
  - json==2.0.9
  depends_on:
  - get_sales_officer_workload
  description: AI-powered lead scoring based on collected data
- id: determine_qualification_status
  name: Determine Qualification Status
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F3AF Determining qualification\
    \ status...\")\n\n# Get lead score from previous task\nlead_score = 85.7  # From\
    \ calculate_lead_score task\nqualification_threshold = 70.0\n\nqualification_result\
    \ = {\n    \"is_qualified\": lead_score >= qualification_threshold,\n    \"qualification_status\"\
    : \"qualified\" if lead_score >= qualification_threshold else \"unqualified\"\
    ,\n    \"threshold_used\": qualification_threshold,\n    \"score_achieved\": lead_score,\n\
    \    \"qualification_confidence\": 0.92,\n    \"qualification_reasons\": [\n \
    \       \"Score exceeds threshold\",\n        \"Strong budget indicators\",\n\
    \        \"Clear timeline established\",\n        \"Decision authority confirmed\"\
    \n    ]\n}\n\nstatus = qualification_result[\"qualification_status\"]\nprint(f\"\
    \u2705 Qualification determined: {status.upper()}\")\nprint(f\"   Score: {lead_score:.1f}\
    \ (Threshold: {qualification_threshold})\")\nprint(f\"   Confidence: {qualification_result['qualification_confidence']*100:.0f}%\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(qualification_result)}\")\n"
  outputs:
  - name: qualification_result
    value: Qualification status determined
  packages:
  - json==2.0.9
  depends_on:
  - calculate_lead_score
  description: Determine if lead is qualified based on score
- id: recommend_sales_officer
  name: Recommend Sales Officer
  type: script
  script: "#!/usr/bin/env python3\nimport json\n\nprint(\"\U0001F464 Recommending\
    \ sales officer assignment...\")\n\n# Mock recommendation logic\nrecommendation\
    \ = {\n    \"recommended_officer\": \"OFF_002\",\n    \"officer_name\": \"Mike\
    \ Chen\",\n    \"match_confidence\": 0.89,\n    \"assignment_reasons\": [\n  \
    \      \"Lower current workload (48% vs 60%)\",\n        \"Technology specialization\
    \ match\",\n        \"Higher availability score (92%)\",\n        \"Good success\
    \ rate (68%)\"\n    ],\n    \"match_score\": 87.5,\n    \"expected_performance\"\
    : {\n        \"success_probability\": 0.71,\n        \"estimated_close_timeline\"\
    : \"45-52 days\"\n    }\n}\n\nprint(f\"\u2705 Recommended officer: {recommendation['officer_name']}\"\
    )\nprint(f\"   Match confidence: {recommendation['match_confidence']*100:.0f}%\"\
    )\nprint(f\"   Assignment reasons: {', '.join(recommendation['assignment_reasons'][:2])}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(recommendation)}\")\n"
  outputs:
  - name: officer_recommendation
    value: Sales officer recommendation completed
  packages:
  - json==2.0.9
  condition: ${qualification_result.is_qualified} == true
  depends_on:
  - determine_qualification_status
  description: Recommend best sales officer for qualified leads
- id: create_deal_in_crm
  name: Create Deal in CRM
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime, timedelta\n\
    \nprint(\"\U0001F4BC Creating deal in CRM...\")\n\n# Mock deal creation\ndeal_data\
    \ = {\n    \"deal_id\": \"DEAL_2025_001234\",\n    \"lead_id\": \"LEAD_12345\"\
    ,\n    \"customer_name\": \"TechCorp Industries\",\n    \"deal_name\": \"TechCorp\
    \ CRM Implementation\",\n    \"estimated_value\": 87500.00,\n    \"probability\"\
    : 0.73,\n    \"expected_close_date\": \"2025-08-15\",\n    \"assigned_officer\"\
    : \"OFF_002\",\n    \"deal_stage\": \"qualified\",\n    \"created_at\": datetime.now().isoformat(),\n\
    \    \"ai_generated\": True,\n    \"source\": \"ai_qualification\"\n}\n\nprint(f\"\
    \u2705 Deal created: {deal_data['deal_name']}\")\nprint(f\"   Deal ID: {deal_data['deal_id']}\"\
    )\nprint(f\"   Value: ${deal_data['estimated_value']:,.2f}\")\nprint(f\"   Assigned\
    \ to: {deal_data['assigned_officer']}\")\n\nprint(f\"__OUTPUTS__ {json.dumps(deal_data)}\"\
    )\n"
  outputs:
  - name: deal_created
    value: Deal created in CRM
  packages:
  - json==2.0.9
  condition: ${qualification_result.is_qualified} == true
  depends_on:
  - recommend_sales_officer
  description: Create new deal for qualified lead
- id: update_crm_lead_status_qualified
  name: Update CRM Lead Status - Qualified
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\u2705 Updating lead status to QUALIFIED...\")\n\n# Mock CRM update\n\
    update_result = {\n    \"lead_id\": \"LEAD_12345\",\n    \"previous_status\":\
    \ \"new\",\n    \"new_status\": \"qualified\",\n    \"updated_at\": datetime.now().isoformat(),\n\
    \    \"updated_by\": \"ai_agent\",\n    \"score\": 85.7,\n    \"assigned_officer\"\
    : \"OFF_002\",\n    \"next_actions\": [\n        {\n            \"action\": \"\
    schedule_discovery_call\",\n            \"due_date\": \"2025-06-27T10:00:00Z\"\
    ,\n            \"priority\": \"high\"\n        }\n    ]\n}\n\nprint(f\"\u2705\
    \ Lead status updated to: {update_result['new_status'].upper()}\")\nprint(f\"\
    \   Score: {update_result['score']}\")\nprint(f\"   Assigned to: {update_result['assigned_officer']}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(update_result)}\")\n"
  outputs:
  - name: lead_update_qualified
    value: Lead status updated to qualified
  packages:
  - json==2.0.9
  condition: ${qualification_result.is_qualified} == true
  depends_on:
  - create_deal_in_crm
  description: Update lead status to qualified in CRM
- id: update_crm_lead_status_unqualified
  name: Update CRM Lead Status - Unqualified
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\u274C Updating lead status to UNQUALIFIED...\")\n\n# Mock CRM update\
    \ for unqualified lead\nupdate_result = {\n    \"lead_id\": \"LEAD_12345\",\n\
    \    \"previous_status\": \"new\",\n    \"new_status\": \"unqualified\",\n   \
    \ \"updated_at\": datetime.now().isoformat(),\n    \"updated_by\": \"ai_agent\"\
    ,\n    \"score\": 45.2,  # Mock low score\n    \"unqualification_reasons\": [\n\
    \        \"Score below threshold\",\n        \"Budget not confirmed\",\n     \
    \   \"No clear timeline\"\n    ],\n    \"recommended_actions\": [\n        \"\
    nurture_campaign\",\n        \"follow_up_in_90_days\"\n    ]\n}\n\nprint(f\"\u274C\
    \ Lead status updated to: {update_result['new_status'].upper()}\")\nprint(f\"\
    \   Score: {update_result['score']}\")\nprint(f\"   Reasons: {', '.join(update_result['unqualification_reasons'])}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(update_result)}\")\n"
  outputs:
  - name: lead_update_unqualified
    value: Lead status updated to unqualified
  packages:
  - json==2.0.9
  condition: ${qualification_result.is_qualified} == false
  depends_on:
  - determine_qualification_status
  description: Update lead status to unqualified in CRM
- id: log_activity_in_dynamics
  name: Log Activity in Dynamics
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\U0001F4DD Logging activity in Dynamics 365...\")\n\n# Mock Dynamics activity\
    \ creation\nactivity_result = {\n    \"activity_id\": \"99887766-5544-3322-1100-ffeeddccbbaa\"\
    ,\n    \"subject\": \"AI Lead Qualification Completed\",\n    \"regardingobjectid\"\
    : \"12345678-1234-1234-1234-123456789abc\",\n    \"activity_type\": \"ai_qualification\"\
    ,\n    \"created_at\": datetime.now().isoformat(),\n    \"ai_confidence_score\"\
    : 0.92,\n    \"qualification_score\": 85.7,\n    \"processing_time_seconds\":\
    \ 12.5,\n    \"qualification_factors\": [\n        \"budget_verified\",\n    \
    \    \"authority_confirmed\", \n        \"need_established\"\n    ],\n    \"next_steps\"\
    : [\n        \"schedule_discovery_call\",\n        \"send_welcome_email\"\n  \
    \  ]\n}\n\nprint(f\"\u2705 Activity logged in Dynamics 365\")\nprint(f\"   Activity\
    \ ID: {activity_result['activity_id']}\")\nprint(f\"   Subject: {activity_result['subject']}\"\
    )\nprint(f\"   AI Confidence: {activity_result['ai_confidence_score']*100:.0f}%\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(activity_result)}\")\n"
  outputs:
  - name: dynamics_activity
    value: Activity logged in Dynamics 365
  packages:
  - json==2.0.9
  condition: ${qualification_result.is_qualified} == true
  depends_on:
  - update_crm_lead_status_qualified
  description: Log AI qualification activity in Dynamics 365
- id: notify_sales_officer
  name: Notify Sales Officer
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime\n\n\
    print(\"\U0001F514 Sending notification to sales officer...\")\n\n# Mock notification\n\
    notification_result = {\n    \"notification_id\": \"NOTIF_2025_009876\",\n   \
    \ \"recipient_id\": \"OFF_002\",\n    \"recipient_name\": \"Mike Chen\",\n   \
    \ \"notification_type\": \"lead_assignment\",\n    \"priority\": \"high\",\n \
    \   \"sent_at\": datetime.now().isoformat(),\n    \"channels_used\": [\"email\"\
    , \"slack\", \"push\"],\n    \"message\": \"New qualified lead assigned - TechCorp\
    \ Industries. Score: 85.7/100, Value: $87,500\",\n    \"action_buttons\": [\n\
    \        \"View Lead Details\",\n        \"Schedule Call\"\n    ],\n    \"delivery_status\"\
    : {\n        \"email\": \"delivered\",\n        \"slack\": \"delivered\", \n \
    \       \"push\": \"delivered\"\n    }\n}\n\nprint(f\"\u2705 Notification sent\
    \ to {notification_result['recipient_name']}\")\nprint(f\"   Channels: {', '.join(notification_result['channels_used'])}\"\
    )\nprint(f\"   Priority: {notification_result['priority'].upper()}\")\n\nprint(f\"\
    __OUTPUTS__ {json.dumps(notification_result)}\")\n"
  outputs:
  - name: notification_sent
    value: Sales officer notification sent
  packages:
  - json==2.0.9
  condition: ${qualification_result.is_qualified} == true
  depends_on:
  - log_activity_in_dynamics
  description: Send notification to assigned sales officer
- id: schedule_followup
  name: Schedule Follow-up
  type: script
  script: "#!/usr/bin/env python3\nimport json\nfrom datetime import datetime, timedelta\n\
    \nprint(\"\U0001F4C5 Scheduling follow-up for unqualified lead...\")\n\n# Mock\
    \ follow-up scheduling\nfollowup_result = {\n    \"task_id\": \"TASK_2025_012345\"\
    ,\n    \"lead_id\": \"LEAD_12345\",\n    \"task_type\": \"nurture_followup\",\n\
    \    \"scheduled_for\": (datetime.now() + timedelta(days=90)).isoformat(),\n \
    \   \"actions\": [\n        {\n            \"action\": \"send_nurture_email\"\
    ,\n            \"scheduled_date\": (datetime.now() + timedelta(days=30)).isoformat()\n\
    \        },\n        {\n            \"action\": \"qualification_retry\",\n   \
    \         \"scheduled_date\": (datetime.now() + timedelta(days=90)).isoformat()\n\
    \        }\n    ],\n    \"campaign_type\": \"unqualified_nurture\",\n    \"expected_outcome\"\
    : \"re_evaluation\"\n}\n\nprint(f\"\u2705 Follow-up scheduled for unqualified\
    \ lead\")\nprint(f\"   Next action: {followup_result['actions'][0]['action']}\"\
    )\nprint(f\"   Scheduled for: {followup_result['actions'][0]['scheduled_date']}\"\
    )\n\nprint(f\"__OUTPUTS__ {json.dumps(followup_result)}\")\n"
  outputs:
  - name: followup_scheduled
    value: Follow-up scheduled for unqualified lead
  packages:
  - json==2.0.9
  condition: ${qualification_result.is_qualified} == false
  depends_on:
  - update_crm_lead_status_unqualified
  description: Schedule follow-up actions for unqualified leads
inputs:
- name: lead_id
  type: string
  default: LEAD_12345
  description: Lead ID from CRM webhook
outputs:
  officer_assignment:
    type: object
    source: recommend_sales_officer
    description: Sales officer assignment details
  notification_status:
    type: object
    source: notify_sales_officer
    description: Notification delivery status
  deal_creation_result:
    type: object
    source: create_deal_in_crm
    description: Deal creation details for qualified leads
  lead_qualification_result:
    type: object
    source: determine_qualification_status
    description: Final lead qualification outcome
version: '1.0'
triggers:
- name: New Lead Created
  path: /webhook/new-lead
  type: webhook
  config:
    method: POST
    headers:
    - name: Content-Type
      value: application/json
      required: true
    authentication: none
  description: Triggered when new lead is created in CRM
  input_mapping:
  - webhook_field: lead_id
    workflow_input: lead_id
- name: Manual Lead Qualification
  type: manual
  description: Manually trigger lead qualification process
description: Automated lead qualification workflow with AI scoring and sales officer
  assignment