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Webex Calling CDR Call Reporting Interpreter by Metropolis

★ ★ ★ ★ ★ 5 - (3) Reviews - Created on April 11, 2024
Last updated on August 09, 2024 Engagement: Over 100 Conversations

Expert in interpreting Webex Calling Call Detail Records

Author
Metropolis View Author GPTs
Author website
https://metropolis.com
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Prompt Starters

  • `TOP 5 EXAMPLES` ➤ Common Webex Calling Reports and Visuals
  • `CORRELATION ID` ➤ Abandoned vs answered calls
  • `HELP` ➤ Interpret Webex Calling CDR
  • `SAMPLE DATA` ➤ # Generate sample cdr using`Python Tool` and ``` import pandas as pd import numpy as np from faker import Faker import uuid from datetime import datetime, timedelta fake = Faker() # Constants for dataset num_records = 1000 max_legs_per_session = 4 states = ['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', 'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ'] departments = ['Cardiology', 'Emergency', 'General Surgery', 'Pediatrics', 'Radiology', 'Administration'] call_types = ['SIP_INBOUND', 'SIP_OUTBOUND', 'SIP_INTERNATIONAL', 'SIP_MEETING', 'SIP_NATIONAL', 'SIP_SHORTCODE', 'SIP_EMERGENCY', 'SIP_PREMIUM', 'SIP_ENTERPRISE', 'SIP_TOLLFREE', 'SIP_MOBILE', 'UNKNOWN'] call_results = ['Success', 'Failure', 'Refusal'] user_types = ['Doctor', 'Nurse', 'Administrative Staff', 'Patient', 'Emergency Response Team'] client_types = ['Desktop', 'Mobile', 'Web'] organizations = ['Org1', 'Org2', 'Org3'] sites = ['Site1', 'Site2', 'Site3'] client_versions = [str(fake.random_number(digits=4, fix_len=True)) for _ in range(5)] os_types = ['Windows', 'MacOS', 'Linux', 'iOS', 'Android'] models = ['8865-3PCC', 'IOS', 'Cisco-Board70', 'ATA192-XX', 'DBS-210-3PC'] # Generate fake dataset data = [] for _ in range(num_records): start_time = fake.date_time_this_month() duration = np.random.randint(1, 3600) # Duration in seconds call_type = np.random.choice(call_types) call_result = np.random.choice(call_results) user_type = np.random.choice(user_types) department = np.random.choice(departments) state = np.random.choice(states) client_type = np.random.choice(client_types) organization = np.random.choice(organizations) site = np.random.choice(sites) client_version = np.random.choice(client_versions) os_type = np.random.choice(os_types) model = np.random.choice(models) # Simulating realistic phone numbers calling_number = fake.msisdn() called_number = fake.msisdn() answered = call_result == 'Success' ring_duration = np.random.randint(1, 30) if answered else None answer_time = start_time + timedelta(seconds=ring_duration) if answered else None release_time = start_time + timedelta(seconds=duration) # Generate a Correlation ID for the call session correlation_id = str(uuid.uuid4()) # Determine the number of call legs for this session num_legs = np.random.randint(1, max_legs_per_session + 1) for leg in range(num_legs): data.append({ 'Start Time': start_time.strftime('%Y-%m-%d %H:%M:%S'), 'Answer Time': answer_time.strftime('%Y-%m-%d %H:%M:%S') if answered else None, 'Duration': duration, 'Calling Number': calling_number, 'Called Number': called_number, 'User': str(uuid.uuid4()), 'Calling Line ID': calling_number, 'Called Line ID': called_number, 'Correlation ID': correlation_id, 'Location': state, 'Inbound Trunk': str(uuid.uuid4()) if call_type in ['SIP_INBOUND', 'SIP_TOLLFREE'] else None, 'Outbound Trunk': str(uuid.uuid4()) if call_type in ['SIP_OUTBOUND', 'SIP_NATIONAL'] else None, 'Route Group': str(uuid.uuid4()) if call_type in ['SIP_OUTBOUND', 'SIP_NATIONAL'] else None, 'Direction': 'INBOUND' if call_type == 'SIP_INBOUND' else 'OUTBOUND', 'Call Type': call_type, 'Client Type': client_type, 'Client Version': client_version, 'OS Type': os_type, 'Device MAC': fake.mac_address(), 'Model': model, 'Answered': answered, 'International Country': fake.country_code() if call_type == 'SIP_INTERNATIONAL' else None, 'Original Reason': np.random.choice(['Unconditional', 'NoAnswer', 'Deflection', 'TimeOfDay', 'UserBusy', 'FollowMe', 'CallQueue', 'HuntGroup', 'ExplicitIdxxx', 'ImplicitId', 'Unavailable', 'Unrecognized', 'Unknown']), 'Related Reason': np.random.choice(['Deflection', 'ConsultativeTransfer', 'CallForwardSelective', 'CallForwardAlways', 'CallForwardNoAnswer', 'CallForwardBusy', 'CallForwardNotReachable', 'CallRetrieve', 'CallRecording', 'DirectedCallPickup', 'Executive', 'ExecutiveAssistantInitiateCall', 'ExecutiveAssistantDivert', 'ExecutiveForward', 'ExecutiveAssistantCallPush', 'Remote Office', 'RoutePoint', 'SequentialRing', 'SimultaneousRingPersonal', 'CCMonitoringBI', 'CallQueue', 'HuntGroup', 'CallPickup', 'CallPark', 'CallParkRetrieve', 'FaxDeposit', 'PushNotificationRetrieval', 'BargeIn', 'VoiceXMLScriptTermination', 'AnywhereLocation', 'AnywherePortal', 'Unrecognized']), 'Redirect Reason': np.random.choice(['Unconditional', 'NoAnswer', 'Deflection', 'TimeOfDay', 'UserBusy', 'FollowMe', 'CallQueue', 'HuntGroup', 'ExplicitIdxxx', 'ImplicitId', 'Unavailable', 'Unrecognized', 'Unknown']), 'Site Main Number': fake.msisdn(), 'Site Timezone': fake.timezone(), 'User Type': user_type, 'Call ID': str(uuid.uuid4()), 'Local Session ID': str(uuid.uuid4()), 'Remote Session ID': str(uuid.uuid4()), 'Final Local Session ID': str(uuid.uuid4()), 'Final Remote Session ID': str(uuid.uuid4()), 'User UUID': str(uuid.uuid4()), 'Org UUID': organization, 'Report ID': str(uuid.uuid4()), 'Department ID': department, 'Site UUID': str(uuid.uuid4()), 'Releasing Party': np.random.choice(['Local', 'Remote', 'Unknown']), 'Redirecting Number': fake.msisdn(), 'Transfer Related Call ID': str(uuid.uuid4()) if np.random.rand() > 0.5 else None, 'Dialed Digits': ''.join([str(np.random.randint(0, 9)) for _ in range(10)]), 'Authorization Code': ''.join([str(np.random.randint(0, 9)) for _ in range(6)]), 'Call Transfer Time': (start_time + timedelta(seconds=np.random.randint(1, duration))).strftime('%Y-%m-%d %H:%M:%S') if call_result == 'Success' else None, 'User Number': fake.msisdn(), 'Local Call ID': str(uuid.uuid4()), 'Remote Call ID': str(uuid.uuid4()), 'Network Call ID': str(uuid.uuid4()), 'Related Call ID': str(uuid.uuid4()) if np.random.rand() > 0.5 else None, 'Call Outcome': call_result, 'Call Outcome Reason': np.random.choice(['Normal', 'UserBusy', 'NoAnswer', 'CallRejected', 'UnassignedNumber', 'SIP408', 'InternalRequestTimeout', 'Q850102ServerTimeout', 'NoUserResponse', 'NoAnswerFromUser', 'SIP480', 'SIP487', 'TemporarilyUnavailable', 'AdminCallBlock', 'UserCallBlock', 'Unreachable', 'DestinationOutOfOrder', 'SIP501', 'SIP503', 'ProtocolError', 'SIP606', 'NoRouteToDestination', 'Internal']), 'Answer Indicator': np.random.choice(['Yes', 'No', 'Yes-PostRedirection']), 'Ring Duration': ring_duration, 'Release Time': release_time.strftime('%Y-%m-%d %H:%M:%S'), 'Report Time': datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S') }) # Create DataFrame df = pd.DataFrame(data) # Display the first few records print(df.head()) # Save DataFrame to CSV for further analysis df.to_csv('webex_calling_simulated_cdr.csv', index=False) ``` Then I'll commence analysis of the records presenting the top 5 insights uncovered from the Webex Calling call detail records and present them to the user 'User' Unlocking Actionable Insights using my expert agents and Python Tool for visualizations. THEN`PRESENT TOP FIVE EXAMPLES` ➤ REPORTS AND VISUALS FOR WEBEX CALLING CALL DETAIL RECORDS - Help 'User' Unlocking Actionable Insights from Webex Calling Data

Features and Functions

  • Browser: This tool enables ChatGPT to perform web searches, access and summarize information from web pages in real-time, and provide up-to-date answers to questions about current events, weather, sports scores, and more.
  • Python: The GPT can write and run Python code in a stateful Jupyter notebook environment. It supports file uploads, performs advanced data analysis, handles image conversions, and can execute Python scripts with a timeout for long-running operations.
  • DALL·E: This tool generates images from textual descriptions, providing a creative way to visualize concepts, ideas, or detailed scenes. It can produce images in various styles and formats, based on specific prompts provided by the user.
  • Knowledge file: This GPT includes data from 10 files.

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