Building Queue and Media Type Aggregations with the Genesys Cloud Analytics API

How-To-Articles
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Building Queue and Media Type Aggregations with the Genesys Cloud Analytics API

What You Will Build

  • You will construct a programmatic query that retrieves conversation statistics grouped by specific queues and media types (voice, chat, email).
  • This tutorial uses the Genesys Cloud PureCloud Platform Client V2 SDK and the underlying REST API.
  • The code examples are provided in Python 3.9+ and JavaScript (Node.js 18+).

Prerequisites

Before writing code, you must configure your environment with the following requirements:

  • OAuth Client Credentials: You need a Genesys Cloud OAuth 2.0 client ID and secret. The client must have the analytics:query:read scope.
  • SDK Installation:
    • Python: pip install purecloud-platform-client-v2
    • Node.js: npm install @genesys/purecloud-platform-client-v2
  • Runtime: Python 3.9+ or Node.js 18+.
  • Organization ID: Your Genesys Cloud organization ID is not required for the API call itself but is implicit in the OAuth token.

Authentication Setup

The Genesys Cloud SDKs handle OAuth 2.0 Client Credentials flows automatically. You must initialize the client with your environment, client ID, and client secret. The SDK manages token caching and automatic refresh.

Python Initialization

from purecloudplatformclientv2 import PlatformClient
from purecloudplatformclientv2.analytics_api import AnalyticsApi

def init_analytics_client(client_id: str, client_secret: str, environment: str = "mypurecloud.com") -> AnalyticsApi:
    """
    Initializes the PureCloud Platform Client and returns the Analytics API instance.
    """
    platform_client = PlatformClient(
        client_id=client_id,
        client_secret=client_secret,
        environment=environment
    )
    
    # The SDK handles token acquisition and refresh internally
    return platform_client.analytics_api

JavaScript Initialization

const { PlatformClient, AnalyticsApi } = require('@genesys/purecloud-platform-client-v2');

async function initAnalyticsClient(clientId, clientSecret, environment = 'mypurecloud.com') {
    const platformClient = new PlatformClient({
        clientId,
        clientSecret,
        environment
    });

    // Initialize the client to ensure tokens are available
    await platformClient.ready();

    return new AnalyticsApi(platformClient);
}

Implementation

The core of this task is the POST /api/v2/analytics/conversations/details/query endpoint. Unlike simple GET requests, this endpoint requires a structured JSON body defining the metrics, dimensions, and time window.

Step 1: Constructing the Query Body

The query body consists of three main sections: interval, metrics, and groupBys.

  1. Interval: Defines the start and end time of the data window. It uses ISO 8601 format with timezone offsets.
  2. Metrics: An array of metric names. For queue analytics, common metrics include offerCount, answerCount, abandonCount, and waitTime.
  3. GroupBys: An array of dimension names. To group by queue and media type, you must include queue and mediaType in this array.

Important: The mediaType dimension is only valid if the query scope includes media types that support it. If you query only voice, mediaType will always be “voice”. To see varied data, ensure your date range captures activity across voice, chat, and email.

Python Query Construction

from datetime import datetime, timezone, timedelta
import json

def build_query_body(start_time: datetime, end_time: datetime) -> dict:
    """
    Constructs the JSON body for the analytics query.
    
    Args:
        start_time: Start of the reporting period (UTC).
        end_time: End of the reporting period (UTC).
        
    Returns:
        A dictionary representing the query payload.
    """
    # Ensure times are in UTC with timezone info
    if start_time.tzinfo is None:
        start_time = start_time.replace(tzinfo=timezone.utc)
    if end_time.tzinfo is None:
        end_time = end_time.replace(tzinfo=timezone.utc)

    query = {
        "interval": f"{start_time.isoformat()}/{end_time.isoformat()}",
        "metrics": [
            "offerCount",
            "answerCount",
            "abandonCount",
            "waitTime",
            "handleTime"
        ],
        "groupBys": [
            "queue",
            "mediaType"
        ],
        "view": "realtime" # Use "standard" for historical data
    }
    
    return query

JavaScript Query Construction

function buildQueryBody(startTime, endTime) {
    // Ensure ISO strings include timezone offset
    const startIso = startTime.toISOString();
    const endIso = endTime.toISOString();

    return {
        interval: `${startIso}/${endIso}`,
        metrics: [
            "offerCount",
            "answerCount",
            "abandonCount",
            "waitTime",
            "handleTime"
        ],
        groupBys: [
            "queue",
            "mediaType"
        ],
        view: "standard" // Use "standard" for historical data
    };
}

Step 2: Executing the Query

Once the body is constructed, you send it to the queryConversationsDetails method. The response is a ConversationsDetailsQueryResponse object.

Python Execution

def execute_query(analytics_api: AnalyticsApi, query_body: dict):
    """
    Executes the analytics query and handles potential errors.
    """
    try:
        # The SDK method name corresponds to the API endpoint
        response = analytics_api.query_conversations_details(body=query_body)
        
        if response.body:
            return response.body
        else:
            print("No data returned in response body.")
            return None
            
    except Exception as e:
        # The SDK wraps HTTP errors. Check for 400 (Bad Request) or 401/403 (Auth)
        print(f"Error executing query: {e}")
        if hasattr(e, 'status'):
            print(f"HTTP Status Code: {e.status}")
        raise

JavaScript Execution

async function executeQuery(analyticsApi, queryBody) {
    try {
        const response = await analyticsApi.queryConversationsDetails({
            body: queryBody
        });

        if (response.body) {
            return response.body;
        } else {
            console.log("No data returned in response body.");
            return null;
        }
    } catch (error) {
        console.error("Error executing query:", error);
        if (error.statusCode) {
            console.error(`HTTP Status Code: ${error.statusCode}`);
        }
        throw error;
    }
}

Step 3: Processing the Results

The response object contains an entities array. Each entity represents a unique combination of the groupBys dimensions (Queue ID/Name and Media Type).

Key Fields in the Response Entity:

  • queue: An object containing id and name of the queue.
  • mediaType: A string representing the media type (e.g., “voice”, “chat”, “email”).
  • metrics: An object where keys are metric names and values are the aggregated numbers.

Python Result Processing

def process_results(response_body: dict):
    """
    Iterates through the response entities and prints formatted data.
    """
    if not response_body or 'entities' not in response_body:
        print("No entities found in response.")
        return

    entities = response_body['entities']
    
    print(f"Total entities returned: {len(entities)}")
    print(f"{'Queue Name':<25} | {'Media Type':<10} | {'Offers':<8} | {'Answers':<8} | {'Abandons':<8}")
    print("-" * 70)

    for entity in entities:
        queue_name = entity.get('queue', {}).get('name', 'Unknown Queue')
        media_type = entity.get('mediaType', 'Unknown')
        
        metrics = entity.get('metrics', {})
        offers = metrics.get('offerCount', 0)
        answers = metrics.get('answerCount', 0)
        abandons = metrics.get('abandonCount', 0)
        
        print(f"{queue_name:<25} | {media_type:<10} | {offers:<8} | {answers:<8} | {abandons:<8}")

JavaScript Result Processing

function processResults(responseBody) {
    if (!responseBody || !responseBody.entities) {
        console.log("No entities found in response.");
        return;
    }

    const entities = responseBody.entities;
    console.log(`Total entities returned: ${entities.length}`);
    console.log(`Queue Name                    | Media Type | Offers   | Answers  | Abandons`);
    console.log("-".repeat(70));

    for (const entity of entities) {
        const queueName = entity.queue?.name || 'Unknown Queue';
        const mediaType = entity.mediaType || 'Unknown';
        
        const metrics = entity.metrics || {};
        const offers = metrics.offerCount || 0;
        const answers = metrics.answerCount || 0;
        const abandons = metrics.abandonCount || 0;
        
        console.log(`${padLeft(queueName, 25)} | ${padLeft(mediaType, 10)} | ${padLeft(offers, 8)} | ${padLeft(answers, 8)} | ${padLeft(abandons, 8)}`);
    }
}

function padLeft(str, len) {
    return String(str).padStart(len, ' ');
}

Complete Working Example

Below is the complete, runnable Python script. Replace the placeholders YOUR_CLIENT_ID, YOUR_CLIENT_SECRET, and YOUR_ENVIRONMENT with your actual credentials.

import sys
import os
from datetime import datetime, timezone, timedelta
from purecloudplatformclientv2 import PlatformClient
from purecloudplatformclientv2.analytics_api import AnalyticsApi

# Configuration
CLIENT_ID = os.getenv("GENESYS_CLIENT_ID", "YOUR_CLIENT_ID")
CLIENT_SECRET = os.getenv("GENESYS_CLIENT_SECRET", "YOUR_CLIENT_SECRET")
ENVIRONMENT = os.getenv("GENESYS_ENVIRONMENT", "mypurecloud.com")

def main():
    # 1. Initialize the Client
    print("Initializing PureCloud Platform Client...")
    try:
        platform_client = PlatformClient(
            client_id=CLIENT_ID,
            client_secret=CLIENT_SECRET,
            environment=ENVIRONMENT
        )
        analytics_api = platform_client.analytics_api
    except Exception as e:
        print(f"Failed to initialize client: {e}")
        sys.exit(1)

    # 2. Define Time Window
    # Query the last 24 hours
    end_time = datetime.now(timezone.utc)
    start_time = end_time - timedelta(hours=24)
    
    print(f"Querying data from {start_time.isoformat()} to {end_time.isoformat()}")

    # 3. Construct Query Body
    query_body = {
        "interval": f"{start_time.isoformat()}/{end_time.isoformat()}",
        "metrics": [
            "offerCount",
            "answerCount",
            "abandonCount",
            "waitTime",
            "handleTime"
        ],
        "groupBys": [
            "queue",
            "mediaType"
        ],
        "view": "standard"
    }

    # 4. Execute Query
    print("Executing analytics query...")
    try:
        response = analytics_api.query_conversations_details(body=query_body)
        
        if response.body:
            process_results(response.body)
        else:
            print("No data returned. Check your date range and permissions.")
            
    except Exception as e:
        print(f"Error executing query: {e}")
        if hasattr(e, 'status'):
            print(f"HTTP Status Code: {e.status}")
        if hasattr(e, 'body'):
            print(f"Response Body: {e.body}")

def process_results(response_body: dict):
    if not response_body or 'entities' not in response_body:
        print("No entities found in response.")
        return

    entities = response_body['entities']
    
    print(f"Total entities returned: {len(entities)}")
    print(f"{'Queue Name':<25} | {'Media Type':<10} | {'Offers':<8} | {'Answers':<8} | {'Abandons':<8}")
    print("-" * 70)

    for entity in entities:
        # Safely access nested fields
        queue_info = entity.get('queue', {})
        queue_name = queue_info.get('name', 'Unknown Queue') if queue_info else 'Unknown Queue'
        
        media_type = entity.get('mediaType', 'Unknown')
        
        metrics = entity.get('metrics', {})
        offers = metrics.get('offerCount', 0)
        answers = metrics.get('answerCount', 0)
        abandons = metrics.get('abandonCount', 0)
        
        # Format output
        print(f"{queue_name:<25} | {media_type:<10} | {offers:<8} | {answers:<8} | {abandons:<8}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 400 Bad Request - Invalid Interval

Cause: The interval parameter is malformed or the time range is too large. The Genesys Cloud Analytics API has limits on the time window size for certain views. For the standard view, the maximum interval is typically 30 days. For realtime, it is much smaller.

Fix: Ensure your start and end times are in valid ISO 8601 format with timezone offsets. Verify the duration is within limits.

# Correct format
"interval": "2023-10-01T00:00:00+00:00/2023-10-02T00:00:00+00:00"

Error: 401 Unauthorized or 403 Forbidden

Cause: The OAuth token is expired, invalid, or lacks the analytics:query:read scope.

Fix:

  1. Verify your client ID and secret are correct.
  2. Ensure the OAuth application in Genesys Cloud has the analytics:query:read scope assigned.
  3. If using the SDK, the token refresh is automatic. If you are managing tokens manually, implement a refresh loop.

Error: Empty Entities Array

Cause: No conversation data exists for the specified queues and media types in the given time window.

Fix:

  1. Broaden the time window.
  2. Verify that the queues included in your organization actually had activity during the selected period.
  3. Check if mediaType filtering is inadvertently excluding all data. If you group by mediaType, ensure you are not also filtering by a specific media type in a where clause that conflicts with your expectations.

Error: 429 Too Many Requests

Cause: You have exceeded the rate limit for the Analytics API.

Fix: Implement exponential backoff. The Genesys Cloud SDK does not automatically retry 429s in all languages. You must catch the error and retry after a delay.

import time

def execute_query_with_retry(analytics_api, query_body, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = analytics_api.query_conversations_details(body=query_body)
            return response.body
        except Exception as e:
            if hasattr(e, 'status') and e.status == 429:
                wait_time = 2 ** attempt  # Exponential backoff: 2s, 4s, 8s
                print(f"Rate limited. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                raise
    raise Exception("Max retries exceeded for 429 error")

Official References