Querying Genesys Cloud Analytics: Aggregating by Queue and Media Type

What You Will Build

• This tutorial builds a Python script that retrieves historical conversation metrics from Genesys Cloud, specifically aggregating data by queue and media type.
• It utilizes the Genesys Cloud Analytics API v2 endpoint POST /api/v2/analytics/conversations/details/query.
• The implementation uses Python 3.9+ with the official genesyscloud SDK and the requests library for fallback HTTP handling.

Prerequisites

• OAuth Client Type: You need a Genesys Cloud OAuth Client with the client_credentials grant type.
• Required Scopes: The client must have the analytics:conversation:read scope. Without this, the API returns a 403 Forbidden error.
• SDK Version: This tutorial assumes Genesys Cloud Python SDK version 132.0.0 or higher.
• Runtime: Python 3.9 or higher.
• Dependencies:
  • genesyscloud: The official Genesys Cloud Python SDK.
  • python-dotenv: For managing environment variables securely.

Install the dependencies using pip:

pip install genesyscloud python-dotenv

Authentication Setup

Genesys Cloud uses OAuth 2.0 for authentication. The most robust method for server-to-server integrations is the client_credentials flow. The official SDK handles token acquisition and refresh automatically when you initialize the PlatformClient.

Create a .env file in your project root with the following variables:

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

Initialize the SDK client in your Python script. This step establishes the connection to the correct region and loads the credentials.

import os
from dotenv import load_dotenv
from purecloudplatformclientv2 import PlatformClient

# Load environment variables
load_dotenv()

def get_platform_client() -> PlatformClient:
    """
    Initializes and returns the Genesys Cloud Platform Client.
    """
    pc = PlatformClient()
    
    # Set the region explicitly to avoid defaulting to us-east-1 if incorrect
    region = os.getenv("GENESYS_CLOUD_REGION", "us-east-1")
    pc.set_region(region)
    
    # Configure OAuth
    pc.set_oauth_client_credentials(
        os.getenv("GENESYS_CLOUD_CLIENT_ID"),
        os.getenv("GENESYS_CLOUD_CLIENT_SECRET")
    )
    
    return pc

Implementation

Step 1: Constructing the Analytics Query Body

The Analytics API in Genesys Cloud is query-based. You do not simply request “all queues.” You submit a JSON body that defines the time range, the metrics you want, and the grouping dimensions.

For this tutorial, we want to group by queue and mediaType. This means the response will contain a hierarchy where each queue is broken down by voice, chat, email, etc.

Key parameters in the query body:

• timeRange: An ISO 8601 time interval. Use a relative time like now-7d/now for the last 7 days.
• groupBy: An array of strings. Here, we use ["queue", "mediaType"]. The order matters for the nested structure of the response.
• metrics: An array of metric objects. We will request conversationCount and handleTimeAvg.
• interval: Optional. If omitted, it returns a single aggregate for the entire time range. If provided (e.g., 1d), it breaks the data down by day. For this example, we will omit it to get a total summary.

from datetime import datetime, timedelta
from purecloudplatformclientv2 import AnalyticsQueryBody, Metric

def build_analytics_query() -> AnalyticsQueryBody:
    """
    Constructs the AnalyticsQueryBody object required for the API call.
    """
    # Define the time range: Last 7 days
    end_time = datetime.utcnow()
    start_time = end_time - timedelta(days=7)
    
    # Format as ISO 8601 strings
    time_range = f"{start_time.isoformat()}Z/{end_time.isoformat()}Z"
    
    # Define the metrics to retrieve
    # We want the total number of conversations and the average handle time
    metrics = [
        Metric(name="conversationCount"),
        Metric(name="handleTimeAvg")
    ]
    
    # Define the grouping dimensions
    # Order: First by Queue, then by Media Type
    group_by = ["queue", "mediaType"]
    
    # Construct the query body
    query_body = AnalyticsQueryBody(
        time_range=time_range,
        group_by=group_by,
        metrics=metrics,
        # interval="1d" # Uncomment this if you want daily breakdowns
    )
    
    return query_body

Step 2: Executing the Query via SDK

With the query body constructed, we call the post_analytics_conversations_details_query method. This method is part of the AnalyticsApi module within the SDK.

The SDK method returns an AnalyticsQueryResponse object. This object contains the partitions array, which holds the aggregated data.

from purecloudplatformclientv2 import AnalyticsApi, AnalyticsQueryResponse

def fetch_queue_media_analytics(pc: PlatformClient) -> AnalyticsQueryResponse:
    """
    Executes the analytics query using the Genesys Cloud SDK.
    """
    analytics_api = AnalyticsApi(pc)
    
    query_body = build_analytics_query()
    
    try:
        # Execute the POST request
        response = analytics_api.post_analytics_conversations_details_query(body=query_body)
        return response
    except Exception as e:
        print(f"Error fetching analytics: {e}")
        raise

Step 3: Processing the Nested Response Structure

The response from the Analytics API is hierarchical. When you group by ["queue", "mediaType"], the partitions array contains one object per queue. Inside each queue object, there is a partitions array containing one object per media type.

This nesting can be confusing. You must iterate through the outer loop (queues) and then the inner loop (media types) to extract the flat data you likely need for reporting or database storage.

Each partition object contains:

• entityId: The ID of the queue or media type.
• entityName: The human-readable name.
• metrics: A dictionary of metric results.

def process_analytics_response(response: AnalyticsQueryResponse):
    """
    Iterates through the nested partitions to extract flat data.
    """
    if not response or not response.partitions:
        print("No data returned from the analytics query.")
        return []

    results = []
    
    # Outer loop: Iterate over Queues
    for queue_partition in response.partitions:
        queue_id = queue_partition.entity_id
        queue_name = queue_partition.entity_name
        
        # Check if there are nested partitions (Media Types)
        # If groupBy was only ["queue"], media partitions would be empty/null
        if queue_partition.partitions:
            # Inner loop: Iterate over Media Types within the Queue
            for media_partition in queue_partition.partitions:
                media_type = media_partition.entity_name
                media_id = media_partition.entity_id
                
                # Extract metrics
                metrics = media_partition.metrics
                
                # Access specific metrics safely
                conversation_count = metrics.get("conversationCount", {}).get("value", 0)
                handle_time_avg = metrics.get("handleTimeAvg", {}).get("value", 0)
                
                # Create a flat record
                record = {
                    "queue_id": queue_id,
                    "queue_name": queue_name,
                    "media_type": media_type,
                    "media_id": media_id,
                    "conversation_count": conversation_count,
                    "handle_time_avg_seconds": handle_time_avg # Handle time is in seconds
                }
                results.append(record)
        else:
            # Handle case where there is no further grouping (e.g., total for queue)
            metrics = queue_partition.metrics
            conversation_count = metrics.get("conversationCount", {}).get("value", 0)
            
            record = {
                "queue_id": queue_id,
                "queue_name": queue_name,
                "media_type": "All",
                "conversation_count": conversation_count,
                "handle_time_avg_seconds": 0
            }
            results.append(record)
            
    return results

Complete Working Example

Below is the complete, runnable Python script. Save this as analytics_query.py. Ensure your .env file is in the same directory.

import os
import json
from dotenv import load_dotenv
from datetime import datetime, timedelta
from purecloudplatformclientv2 import (
    PlatformClient,
    AnalyticsApi,
    AnalyticsQueryBody,
    Metric,
    AnalyticsQueryResponse
)

def load_config():
    """Loads environment variables from .env file."""
    load_dotenv()
    return {
        "region": os.getenv("GENESYS_CLOUD_REGION", "us-east-1"),
        "client_id": os.getenv("GENESYS_CLOUD_CLIENT_ID"),
        "client_secret": os.getenv("GENESYS_CLOUD_CLIENT_SECRET")
    }

def get_platform_client(config: dict) -> PlatformClient:
    """
    Initializes and returns the Genesys Cloud Platform Client.
    """
    pc = PlatformClient()
    pc.set_region(config["region"])
    
    if not config["client_id"] or not config["client_secret"]:
        raise ValueError("GENESYS_CLOUD_CLIENT_ID and GENESYS_CLOUD_CLIENT_SECRET must be set in .env")
        
    pc.set_oauth_client_credentials(
        config["client_id"],
        config["client_secret"]
    )
    return pc

def build_analytics_query() -> AnalyticsQueryBody:
    """
    Constructs the AnalyticsQueryBody object required for the API call.
    Groups by Queue and Media Type for the last 7 days.
    """
    end_time = datetime.utcnow()
    start_time = end_time - timedelta(days=7)
    
    # ISO 8601 format with Z suffix for UTC
    time_range = f"{start_time.isoformat()}Z/{end_time.isoformat()}Z"
    
    # Define metrics: Conversation Count and Average Handle Time
    metrics = [
        Metric(name="conversationCount"),
        Metric(name="handleTimeAvg")
    ]
    
    # Grouping dimensions
    group_by = ["queue", "mediaType"]
    
    query_body = AnalyticsQueryBody(
        time_range=time_range,
        group_by=group_by,
        metrics=metrics
    )
    
    return query_body

def fetch_and_process_analytics(pc: PlatformClient):
    """
    Executes the query and processes the nested response into a flat list.
    """
    analytics_api = AnalyticsApi(pc)
    query_body = build_analytics_query()
    
    try:
        print("Executing Analytics Query...")
        response = analytics_api.post_analytics_conversations_details_query(body=query_body)
        
        if not response or not response.partitions:
            print("No data found for the specified time range and groups.")
            return []

        results = []
        
        # Iterate through Queues
        for queue_partition in response.partitions:
            queue_name = queue_partition.entity_name
            queue_id = queue_partition.entity_id
            
            # Iterate through Media Types within each Queue
            if queue_partition.partitions:
                for media_partition in queue_partition.partitions:
                    media_type = media_partition.entity_name
                    
                    # Extract metric values safely
                    conv_count_data = media_partition.metrics.get("conversationCount", {})
                    handle_time_data = media_partition.metrics.get("handleTimeAvg", {})
                    
                    conv_count = conv_count_data.get("value", 0)
                    handle_time = handle_time_data.get("value", 0)
                    
                    results.append({
                        "Queue Name": queue_name,
                        "Queue ID": queue_id,
                        "Media Type": media_type,
                        "Conversation Count": conv_count,
                        "Avg Handle Time (seconds)": handle_time
                    })
            else:
                # Fallback if no media type breakdown exists
                conv_count_data = queue_partition.metrics.get("conversationCount", {})
                results.append({
                    "Queue Name": queue_name,
                    "Queue ID": queue_id,
                    "Media Type": "All",
                    "Conversation Count": conv_count_data.get("value", 0),
                    "Avg Handle Time (seconds)": 0
                })
                
        return results

    except Exception as e:
        print(f"An error occurred: {e}")
        raise

def main():
    """
    Main execution block.
    """
    try:
        config = load_config()
        pc = get_platform_client(config)
        
        # Fetch data
        data = fetch_and_process_analytics(pc)
        
        # Output results
        if data:
            print(f"\nRetrieved {len(data)} records.\n")
            print(json.dumps(data, indent=2))
        else:
            print("No data retrieved.")
            
    except Exception as e:
        print(f"Fatal error: {e}")
        exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 403 Forbidden - analytics:conversation:read

Cause: The OAuth client used in the .env file does not have the required scope.
Fix:

1. Log in to the Genesys Cloud Admin portal.
2. Navigate to Applications > OAuth 2.0 Clients.
3. Select your client.
4. Go to the Scopes tab.
5. Search for analytics:conversation:read and add it.
6. Save the client. Note: You may need to wait a few minutes for the scope to propagate.

Error: 429 Too Many Requests

Cause: Genesys Cloud enforces rate limits on the Analytics API. High-volume queries or rapid successive calls will trigger this.
Fix: Implement exponential backoff. The SDK does not handle this automatically for all methods, so you may need to wrap the call in a retry logic loop.

import time

def fetch_with_retry(pc: PlatformClient, max_retries=3):
    analytics_api = AnalyticsApi(pc)
    query_body = build_analytics_query()
    
    for attempt in range(max_retries):
        try:
            return analytics_api.post_analytics_conversations_details_query(body=query_body)
        except Exception as e:
            if "429" in str(e) and attempt < max_retries - 1:
                wait_time = 2 ** attempt
                print(f"Rate limited. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                raise

Error: Empty Partitions Array

Cause: The time range specified has no conversation data, or the queues queried do not exist.
Fix:

1. Verify the timeRange is in the past. Genesys Cloud analytics data has a latency of approximately 5-15 minutes. Querying now/now will often return empty results.
2. Ensure the queues in your organization have processed conversations in the selected media types.
3. Check if the groupBy dimensions are valid. queue and mediaType are standard, but ensure no typos exist in the string array.

Error: Metric Value is Null

Cause: A specific metric is not applicable to a media type. For example, handleTimeAvg might be null for certain chat metrics if the data model does not support it, or if no conversations occurred.
Fix: Always use .get("value", 0) or similar null-safe access patterns when extracting metric values from the response object, as shown in the complete example.

Official References

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