Querying Genesys Cloud Analytics for Queue and Media Type Aggregations

What You Will Build

• You will build a script that submits a time-series aggregation query to Genesys Cloud CX to retrieve conversation metrics grouped by queue and media type.
• You will use the Genesys Cloud Analytics Conversations Details Query endpoint via the Python SDK.
• You will use Python 3.9+ with the genesys-cloud-sdk package.

Prerequisites

• OAuth Client: A Genesys Cloud OAuth 2.0 client with the analytics:query:read scope.
• SDK Version: genesys-cloud-sdk version 120.0.0 or higher.
• Language/Runtime: Python 3.9 or later.
• External Dependencies:
  • genesys-cloud-sdk
  • python-dotenv (for secure credential management)
  • pyyaml (optional, for configuration)

Install the dependencies using pip:

pip install genesys-cloud-sdk python-dotenv

Authentication Setup

Genesys Cloud uses OAuth 2.0 for authentication. For server-to-server integrations, you must use the Client Credentials flow. The SDK handles token acquisition and caching, but you must initialize the client correctly.

Create a .env file in your project root to store your credentials securely. Never hardcode secrets in your source code.

GENESYS_CLOUD_CLIENT_ID=your_client_id_here
GENESYS_CLOUD_CLIENT_SECRET=your_client_secret_here
GENESYS_CLOUD_REGION=us-east-1

The following code demonstrates how to initialize the PlatformClientV2 object with these credentials. The SDK automatically manages the token lifecycle, refreshing it when necessary.

import os
from dotenv import load_dotenv
from purecloudplatformclientv2 import PlatformClientV2

# Load environment variables
load_dotenv()

def init_platform_client():
    """
    Initializes and returns the Genesys Cloud Platform Client.
    """
    # Create the platform client instance
    platform_client = PlatformClientV2()

    # Get credentials from environment variables
    client_id = os.getenv("GENESYS_CLOUD_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLOUD_CLIENT_SECRET")
    region = os.getenv("GENESYS_CLOUD_REGION", "us-east-1")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET must be set in environment variables.")

    # Configure the client with credentials
    # The SDK handles the OAuth token exchange internally
    platform_client.set_credentials(
        client_id=client_id,
        client_secret=client_secret,
        region=region
    )

    return platform_client

Implementation

Step 1: Define the Query Body

The Analytics API uses a complex JSON body to define what data to retrieve. For this tutorial, you need to aggregate data by queue and mediaType.

You must define the following components in the query body:

1. timeGroup: How to group data temporally (e.g., every 1 hour).
2. groupBy: The non-temporal dimensions to group by. In this case, queue and mediaType.
3. metrics: The specific metrics to calculate (e.g., total conversations, abandoned calls).
4. interval: The start and end time for the data retrieval.

The metrics object requires you to specify the metric name and the calculation type. Common calculation types are sum, avg, min, max, and count.

def build_query_body(start_time: str, end_time: str) -> dict:
    """
    Constructs the JSON body for the Analytics Conversations Query.
    
    Args:
        start_time: ISO 8601 start time (e.g., '2023-10-01T00:00:00Z')
        end_time: ISO 8601 end time (e.g., '2023-10-02T00:00:00Z')
    
    Returns:
        dict: The query body dictionary.
    """
    query_body = {
        "timeGroup": "HOUR",  # Group data by hour
        "groupBy": [
            "queue",   # Group by Queue
            "mediaType" # Group by Media Type (voice, chat, email, etc.)
        ],
        "metrics": {
            "conversationCount": {
                "calculation": "sum"
            },
            "abandonedCount": {
                "calculation": "sum"
            },
            "avgWaitTime": {
                "calculation": "avg"
            }
        },
        "interval": f"{start_time}/{end_time}",
        "paging": {
            "pageSize": 1000  # Max page size for analytics queries
        }
    }
    
    return query_body

Note on Metrics: Ensure the metrics you request are available for the selected media types. For example, avgWaitTime is relevant for voice and chat, but may be null or irrelevant for email depending on your organization’s configuration.

Step 2: Execute the Query with Pagination

The Analytics API returns paginated results. You must handle pagination to retrieve all data within the specified interval. The response contains a nextPageId if more data is available.

The SDK method post_analytics_conversations_details_query sends the POST request to /api/v2/analytics/conversations/details/query.

from purecloudplatformclientv2 import AnalyticsApi
from purecloudplatformclientv2.rest import ApiException

def execute_analytics_query(platform_client: PlatformClientV2, start_time: str, end_time: str):
    """
    Executes the analytics query and handles pagination.
    
    Args:
        platform_client: The initialized PlatformClientV2 instance.
        start_time: ISO 8601 start time.
        end_time: ISO 8601 end time.
    
    Returns:
        list: A list of all result entities across all pages.
    """
    analytics_api = AnalyticsApi(platform_client)
    
    query_body = build_query_body(start_time, end_time)
    
    all_results = []
    page_id = None
    page_count = 0
    
    try:
        while True:
            page_count += 1
            print(f"Fetching page {page_count}...")
            
            # Execute the query
            # The SDK maps the dictionary to the required model object internally
            response = analytics_api.post_analytics_conversations_details_query(
                body=query_body,
                page_id=page_id  # Pass None for the first request
            )
            
            # Append results to the list
            if response.entities:
                all_results.extend(response.entities)
            
            # Check for more pages
            if response.next_page_id:
                page_id = response.next_page_id
            else:
                break
                
    except ApiException as e:
        print(f"Exception when calling AnalyticsApi->post_analytics_conversations_details_query: {e}")
        raise
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        raise

    print(f"Completed fetching {page_count} pages. Total entities: {len(all_results)}")
    return all_results

Step 3: Process and Format the Results

The raw response contains a list of entities. Each entity represents a unique combination of the groupBy dimensions (Queue and Media Type) for a specific time bucket.

You need to parse this data into a structured format for further use (e.g., CSV export, database insertion, or dashboarding).

import csv
from datetime import datetime

def process_results(results: list, output_file: str = "analytics_results.csv"):
    """
    Processes the raw analytics results and writes them to a CSV file.
    
    Args:
        results: List of ConversationQueryEntity objects.
        output_file: Path for the output CSV file.
    """
    if not results:
        print("No results to process.")
        return

    # Define CSV headers
    headers = [
        "Time",
        "Queue ID",
        "Queue Name",
        "Media Type",
        "Conversation Count",
        "Abandoned Count",
        "Avg Wait Time (seconds)"
    ]

    with open(output_file, 'w', newline='', encoding='utf-8') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=headers)
        writer.writeheader()

        for entity in results:
            # Extract queue information
            queue_id = entity.queue.id if entity.queue else "Unknown"
            queue_name = entity.queue.name if entity.queue else "Unknown"
            
            # Extract media type
            media_type = entity.media_type
            
            # Extract time bucket
            time_bucket = entity.time_bucket
            
            # Extract metrics
            conv_count = entity.metrics.get("conversationCount", {}).get("value", 0)
            abandoned_count = entity.metrics.get("abandonedCount", {}).get("value", 0)
            avg_wait = entity.metrics.get("avgWaitTime", {}).get("value", 0)
            
            # Format time bucket for readability
            try:
                formatted_time = datetime.fromisoformat(time_bucket.replace('Z', '+00:00')).strftime('%Y-%m-%d %H:%M:%S')
            except Exception:
                formatted_time = time_bucket

            row = {
                "Time": formatted_time,
                "Queue ID": queue_id,
                "Queue Name": queue_name,
                "Media Type": media_type,
                "Conversation Count": conv_count,
                "Abandoned Count": abandoned_count,
                "Avg Wait Time (seconds)": avg_wait
            }
            
            writer.writerow(row)

    print(f"Results written to {output_file}")

Complete Working Example

Combine the previous steps into a single executable script. This script initializes the client, runs the query for the last 24 hours, and saves the results to a CSV file.

import os
import sys
from datetime import datetime, timedelta, timezone
from dotenv import load_dotenv
from purecloudplatformclientv2 import PlatformClientV2
from purecloudplatformclientv2 import AnalyticsApi
from purecloudplatformclientv2.rest import ApiException

# Load environment variables
load_dotenv()

def init_platform_client():
    platform_client = PlatformClientV2()
    client_id = os.getenv("GENESYS_CLOUD_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLOUD_CLIENT_SECRET")
    region = os.getenv("GENESYS_CLOUD_REGION", "us-east-1")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET must be set.")

    platform_client.set_credentials(
        client_id=client_id,
        client_secret=client_secret,
        region=region
    )
    return platform_client

def build_query_body(start_time: str, end_time: str) -> dict:
    return {
        "timeGroup": "HOUR",
        "groupBy": [
            "queue",
            "mediaType"
        ],
        "metrics": {
            "conversationCount": {
                "calculation": "sum"
            },
            "abandonedCount": {
                "calculation": "sum"
            },
            "avgWaitTime": {
                "calculation": "avg"
            }
        },
        "interval": f"{start_time}/{end_time}",
        "paging": {
            "pageSize": 1000
        }
    }

def execute_analytics_query(platform_client, start_time: str, end_time: str):
    analytics_api = AnalyticsApi(platform_client)
    query_body = build_query_body(start_time, end_time)
    
    all_results = []
    page_id = None
    page_count = 0
    
    try:
        while True:
            page_count += 1
            print(f"Fetching page {page_count}...")
            
            response = analytics_api.post_analytics_conversations_details_query(
                body=query_body,
                page_id=page_id
            )
            
            if response.entities:
                all_results.extend(response.entities)
            
            if response.next_page_id:
                page_id = response.next_page_id
            else:
                break
                
    except ApiException as e:
        print(f"API Exception: {e.status} {e.reason}")
        print(f"Response body: {e.body}")
        raise

    return all_results

def process_results(results, output_file="analytics_results.csv"):
    if not results:
        print("No results to process.")
        return

    import csv
    headers = [
        "Time", "Queue ID", "Queue Name", "Media Type",
        "Conversation Count", "Abandoned Count", "Avg Wait Time (seconds)"
    ]

    with open(output_file, 'w', newline='', encoding='utf-8') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=headers)
        writer.writeheader()

        for entity in results:
            queue_id = entity.queue.id if entity.queue else "Unknown"
            queue_name = entity.queue.name if entity.queue else "Unknown"
            media_type = entity.media_type
            time_bucket = entity.time_bucket
            
            conv_count = entity.metrics.get("conversationCount", {}).get("value", 0)
            abandoned_count = entity.metrics.get("abandonedCount", {}).get("value", 0)
            avg_wait = entity.metrics.get("avgWaitTime", {}).get("value", 0)
            
            try:
                formatted_time = datetime.fromisoformat(time_bucket.replace('Z', '+00:00')).strftime('%Y-%m-%d %H:%M:%S')
            except Exception:
                formatted_time = time_bucket

            writer.writerow({
                "Time": formatted_time,
                "Queue ID": queue_id,
                "Queue Name": queue_name,
                "Media Type": media_type,
                "Conversation Count": conv_count,
                "Abandoned Count": abandoned_count,
                "Avg Wait Time (seconds)": avg_wait
            })

    print(f"Results written to {output_file}")

def main():
    # Define time interval: Last 24 hours
    end_time = datetime.now(timezone.utc)
    start_time = end_time - timedelta(hours=24)
    
    start_iso = start_time.strftime('%Y-%m-%dT%H:%M:%SZ')
    end_iso = end_time.strftime('%Y-%m-%dT%H:%M:%SZ')
    
    print(f"Querying data from {start_iso} to {end_iso}")
    
    try:
        platform_client = init_platform_client()
        results = execute_analytics_query(platform_client, start_iso, end_iso)
        process_results(results)
    except Exception as e:
        print(f"Failed to execute query: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

Cause: The OAuth token is invalid, expired, or the client credentials are incorrect.
Fix: Verify that GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET are correct in your .env file. Ensure the client has the analytics:query:read scope assigned in the Genesys Cloud Admin console under Administration > Security > OAuth Clients.

Error: 403 Forbidden

Cause: The OAuth client does not have the required scope, or the user associated with the client lacks permissions to view the specific queues.
Fix:

1. Check the OAuth Client scopes. It must include analytics:query:read.
2. Verify that the user role associated with the OAuth client has access to the Analytics module.
3. Ensure the queues queried are not restricted to specific users/roles that the OAuth client does not impersonate (if using user impersonation). For client credentials flow, the client itself must have permission.

Error: 429 Too Many Requests

Cause: You have exceeded the rate limit for the Analytics API. The Analytics API has strict rate limits to prevent database overload.
Fix: Implement exponential backoff in your retry logic. The Genesys Cloud Python SDK does not automatically retry 429s for complex queries. You must catch the ApiException with status 429 and wait before retrying.

import time

def execute_with_retry(platform_client, start_time, end_time, max_retries=3):
    analytics_api = AnalyticsApi(platform_client)
    query_body = build_query_body(start_time, end_time)
    
    for attempt in range(max_retries):
        try:
            response = analytics_api.post_analytics_conversations_details_query(
                body=query_body,
                page_id=None
            )
            return response
        except ApiException as e:
            if e.status == 429:
                wait_time = 2 ** attempt  # Exponential backoff: 2, 4, 8 seconds
                print(f"Rate limited. Waiting {wait_time} seconds before retry...")
                time.sleep(wait_time)
            else:
                raise
    raise Exception("Max retries exceeded due to rate limiting.")

Error: Empty Results

Cause: The time interval contains no data, or the filters are too restrictive.
Fix:

1. Verify the interval is in the past. The Analytics API may have a delay (usually 15-30 minutes) before data is available.
2. Check if the queues exist and have had conversations in the selected media types.
3. Remove the groupBy filters temporarily to see if any data returns at all.

Official References

• Genesys Cloud Analytics Conversations API Documentation
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow