Querying Genesys Cloud Analytics for Custom Interval Reports

What You Will Build

• This tutorial demonstrates how to construct a POST request to the Genesys Cloud Analytics API to retrieve aggregated conversation metrics for a custom time interval.
• The solution utilizes the /api/v2/analytics/conversations/aggregates/query endpoint to fetch data such as handled count, average handle time, and occupancy for a specific group of users.
• The implementation is provided in Python using the requests library, with explicit handling of pagination and exponential backoff for rate limiting.

Prerequisites

• OAuth Client: A Genesys Cloud OAuth 2.0 client with the api:query scope.
• SDK/API Version: Genesys Cloud API v2 (Rest API).
• Language/Runtime: Python 3.8+.
• External Dependencies:
  • requests: For HTTP communication.
  • python-dotenv: For managing environment variables securely.

Install the dependencies using pip:

pip install requests python-dotenv

Authentication Setup

Genesys Cloud uses OAuth 2.0 for authentication. For server-to-server communication (such as this reporting script), the Client Credentials Grant is the standard flow. You must obtain an access token before making any API calls.

The following Python class handles token retrieval and caching. It ensures that the token is only refreshed when necessary, reducing unnecessary authentication requests.

import os
import time
import requests
from dotenv import load_dotenv

load_dotenv()

class GenesysAuth:
    def __init__(self):
        self.client_id = os.getenv("GENESYS_CLIENT_ID")
        self.client_secret = os.getenv("GENESYS_CLIENT_SECRET")
        self.environment = os.getenv("GENESYS_ENV", "mygen.com")
        self.token_url = f"https://login.{self.environment}/oauth/token"
        self.access_token = None
        self.token_expiry = 0

    def get_token(self) -> str:
        """
        Retrieves a valid access token. Returns the cached token if it is still valid.
        """
        if self.access_token and time.time() < self.token_expiry:
            return self.access_token

        data = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }

        response = requests.post(self.token_url, data=data)
        response.raise_for_status()

        token_data = response.json()
        self.access_token = token_data["access_token"]
        # Subtract 60 seconds to ensure we refresh before strict expiration
        self.token_expiry = time.time() + token_data["expires_in"] - 60
        
        return self.access_token

    def get_headers(self) -> dict:
        return {
            "Authorization": f"Bearer {self.get_token()}",
            "Content-Type": "application/json"
        }

Implementation

Step 1: Constructing the Aggregates Query Payload

The core of this tutorial is the JSON payload sent to the analytics endpoint. The analytics/conversations/aggregates/query endpoint expects a specific structure that defines the groupBy dimensions, the select metrics, and the where clause.

For this example, we will build a report that groups conversations by user and calculates the handled count and average handle time for a specific date range.

Required OAuth Scope: api:query

def build_query_payload(start_date: str, end_date: str, user_ids: list[str]) -> dict:
    """
    Constructs the JSON payload for the analytics aggregates query.
    
    Args:
        start_date: ISO 8601 start datetime (e.g., "2023-10-01T00:00:00.000Z")
        end_date: ISO 8601 end datetime (e.g., "2023-10-01T23:59:59.999Z")
        user_ids: List of Genesys Cloud user IDs to filter by.
        
    Returns:
        dict: The JSON-serializable payload.
    """
    # Define the metrics we want to aggregate
    select_metrics = [
        {
            "name": "handledCount",
            "type": "count"
        },
        {
            "name": "wrapUpTime",
            "type": "sum"
        },
        {
            "name": "talkTime",
            "type": "sum"
        }
    ]

    # Define how we want to group the results
    # Grouping by 'user' allows us to see metrics per agent
    group_by = ["user"]

    # Define the filter conditions
    # We filter by date range and specific user IDs
    where_clause = {
        "predicates": [
            {
                "type": "date",
                "path": "conversation.startTime",
                "operator": "gte",
                "value": start_date
            },
            {
                "type": "date",
                "path": "conversation.startTime",
                "operator": "lt",
                "value": end_date
            },
            {
                "type": "in",
                "path": "conversation.user.id",
                "value": user_ids
            }
        ]
    }

    payload = {
        "select": select_metrics,
        "groupBy": group_by,
        "where": where_clause,
        "interval": "PT1H"  # Optional: Breaks data down by 1-hour intervals. Remove for total sum.
    }

    return payload

Note on Interval: The interval parameter is crucial for custom interval reports. If omitted, the API returns a single aggregated row for the entire date range. Setting it to PT1H (ISO 8601 duration) returns hourly buckets. You can use PT15M for 15 minutes, P1D for daily, etc.

Step 2: Executing the Query with Pagination

Genesys Cloud Analytics endpoints are paginated. A single request may not return all data if the result set is large. The response includes a nextPage link in the headers or body (depending on the specific endpoint version, but for aggregates, it is typically in the body under pageToken or similar, though the v2 aggregates query uses a nextPage field in the response body).

We must implement a loop to fetch all pages until no nextPage is present. We also implement exponential backoff to handle HTTP 429 (Too Many Requests) errors gracefully.

import json
import time
from typing import List, Dict, Any

def fetch_aggregates(auth: GenesysAuth, payload: dict, environment: str) -> List[Dict[str, Any]]:
    """
    Fetches all pages of analytics data with retry logic for rate limits.
    """
    base_url = f"https://api.{environment}/api/v2/analytics/conversations/aggregates/query"
    all_results = []
    next_page = None
    max_retries = 5

    while True:
        headers = auth.get_headers()
        
        # Prepare request body
        request_body = payload.copy()
        if next_page:
            request_body["pageToken"] = next_page

        # Exponential backoff loop
        retries = 0
        success = False
        
        while retries < max_retries and not success:
            try:
                response = requests.post(base_url, headers=headers, json=request_body)
                
                if response.status_code == 200:
                    success = True
                elif response.status_code == 429:
                    # Rate limited. Wait and retry.
                    wait_time = 2 ** retries
                    print(f"Rate limited (429). Waiting {wait_time} seconds...")
                    time.sleep(wait_time)
                    retries += 1
                else:
                    # Other error (400, 401, 500)
                    response.raise_for_status()
                    
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}")
                retries += 1
                if retries == max_retries:
                    raise Exception("Max retries exceeded for analytics query.")

        if not success:
            raise Exception("Failed to fetch data after retries.")

        data = response.json()
        
        # Extract the results
        # The structure is typically: { "entities": [ ... ], "nextPage": "..." }
        if "entities" in data:
            all_results.extend(data["entities"])
        
        # Check for pagination
        if "nextPage" in data and data["nextPage"]:
            next_page = data["nextPage"]
        else:
            next_page = None
            break

    return all_results

Step 3: Processing and Formatting the Results

The raw response from the API contains nested objects. The groupBy dimension determines the structure of the entities. Since we grouped by user, each entity will have a user object and a metrics object.

We need to flatten this data into a usable format, such as a list of dictionaries suitable for CSV export or database insertion.

def process_results(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Flattens the nested analytics response into a clean list of records.
    """
    processed_data = []

    for entity in results:
        # Handle cases where metrics might be null or missing
        metrics = entity.get("metrics", {})
        user_info = entity.get("user", {})
        
        # Extract specific metric values
        handled_count = metrics.get("handledCount", {}).get("count", 0)
        wrap_up_sum = metrics.get("wrapUpTime", {}).get("sum", 0)
        talk_time_sum = metrics.get("talkTime", {}).get("sum", 0)
        
        # Calculate Average Handle Time (AHT) if handled count > 0
        # AHT = (Talk Time + Wrap Up Time) / Handled Count
        aht_seconds = 0
        if handled_count > 0:
            aht_seconds = (talk_time_sum + wrap_up_sum) / handled_count

        # Extract interval time if present (e.g., "2023-10-01T10:00:00.000Z")
        interval_start = entity.get("interval", {}).get("startTime", "N/A")

        record = {
            "user_id": user_info.get("id", "N/A"),
            "user_name": user_info.get("name", "Unknown"),
            "interval_start": interval_start,
            "handled_count": handled_count,
            "total_talk_time_sec": talk_time_sum,
            "total_wrap_up_sec": wrap_up_sum,
            "avg_handle_time_sec": round(aht_seconds, 2)
        }
        
        processed_data.append(record)

    return processed_data

Complete Working Example

The following script combines all the previous steps into a single executable file. It loads environment variables, authenticates, builds the query, fetches all pages, processes the data, and prints the results.

import os
import time
import requests
import json
from typing import List, Dict, Any
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

class GenesysAuth:
    def __init__(self):
        self.client_id = os.getenv("GENESYS_CLIENT_ID")
        self.client_secret = os.getenv("GENESYS_CLIENT_SECRET")
        self.environment = os.getenv("GENESYS_ENV", "mygen.com")
        self.token_url = f"https://login.{self.environment}/oauth/token"
        self.access_token = None
        self.token_expiry = 0

    def get_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry:
            return self.access_token

        data = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }

        response = requests.post(self.token_url, data=data)
        response.raise_for_status()

        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"] - 60
        
        return self.access_token

    def get_headers(self) -> dict:
        return {
            "Authorization": f"Bearer {self.get_token()}",
            "Content-Type": "application/json"
        }

def build_query_payload(start_date: str, end_date: str, user_ids: list[str]) -> dict:
    select_metrics = [
        {"name": "handledCount", "type": "count"},
        {"name": "wrapUpTime", "type": "sum"},
        {"name": "talkTime", "type": "sum"}
    ]

    group_by = ["user"]

    where_clause = {
        "predicates": [
            {"type": "date", "path": "conversation.startTime", "operator": "gte", "value": start_date},
            {"type": "date", "path": "conversation.startTime", "operator": "lt", "value": end_date},
            {"type": "in", "path": "conversation.user.id", "value": user_ids}
        ]
    }

    # Using PT1H for hourly intervals. Remove this line for a single total row.
    return {
        "select": select_metrics,
        "groupBy": group_by,
        "where": where_clause,
        "interval": "PT1H"
    }

def fetch_aggregates(auth: GenesysAuth, payload: dict, environment: str) -> List[Dict[str, Any]]:
    base_url = f"https://api.{environment}/api/v2/analytics/conversations/aggregates/query"
    all_results = []
    next_page = None
    max_retries = 5

    while True:
        headers = auth.get_headers()
        request_body = payload.copy()
        if next_page:
            request_body["pageToken"] = next_page

        retries = 0
        success = False
        
        while retries < max_retries and not success:
            try:
                response = requests.post(base_url, headers=headers, json=request_body)
                
                if response.status_code == 200:
                    success = True
                elif response.status_code == 429:
                    wait_time = 2 ** retries
                    print(f"Rate limited (429). Waiting {wait_time} seconds...")
                    time.sleep(wait_time)
                    retries += 1
                else:
                    response.raise_for_status()
                    
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}")
                retries += 1
                if retries == max_retries:
                    raise Exception("Max retries exceeded.")

        if not success:
            raise Exception("Failed to fetch data after retries.")

        data = response.json()
        
        if "entities" in data:
            all_results.extend(data["entities"])
        
        if "nextPage" in data and data["nextPage"]:
            next_page = data["nextPage"]
        else:
            next_page = None
            break

    return all_results

def process_results(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    processed_data = []
    for entity in results:
        metrics = entity.get("metrics", {})
        user_info = entity.get("user", {})
        
        handled_count = metrics.get("handledCount", {}).get("count", 0)
        wrap_up_sum = metrics.get("wrapUpTime", {}).get("sum", 0)
        talk_time_sum = metrics.get("talkTime", {}).get("sum", 0)
        
        aht_seconds = 0
        if handled_count > 0:
            aht_seconds = (talk_time_sum + wrap_up_sum) / handled_count

        interval_start = entity.get("interval", {}).get("startTime", "N/A")

        record = {
            "user_id": user_info.get("id", "N/A"),
            "user_name": user_info.get("name", "Unknown"),
            "interval_start": interval_start,
            "handled_count": handled_count,
            "avg_handle_time_sec": round(aht_seconds, 2)
        }
        processed_data.append(record)
    return processed_data

def main():
    # Configuration
    # Replace these with actual values or load from env
    START_DATE = os.getenv("QUERY_START_DATE", "2023-10-01T00:00:00.000Z")
    END_DATE = os.getenv("QUERY_END_DATE", "2023-10-02T00:00:00.000Z")
    
    # Example User IDs. In production, fetch these from the Users API.
    USER_IDS = [os.getenv("GENESYS_USER_ID", "example-user-id-123")]

    if not USER_IDS or USER_IDS[0] == "example-user-id-123":
        print("Warning: Using placeholder user ID. Results may be empty.")

    auth = GenesysAuth()
    environment = auth.environment

    print(f"Building query for {START_DATE} to {END_DATE}...")
    payload = build_query_payload(START_DATE, END_DATE, USER_IDS)

    print("Fetching analytics data...")
    try:
        raw_results = fetch_aggregates(auth, payload, environment)
        print(f"Fetched {len(raw_results)} raw entities.")
        
        processed = process_results(raw_results)
        
        if not processed:
            print("No data found for the specified criteria.")
            return

        print("\n--- Report Results ---")
        print(f"{'User Name':<20} | {'Interval Start':<25} | {'Handled':<10} | {'AHT (sec)':<10}")
        print("-" * 70)
        
        for row in processed:
            print(f"{row['user_name']:<20} | {row['interval_start']:<25} | {row['handled_count']:<10} | {row['avg_handle_time_sec']:<10}")
            
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token is expired, invalid, or the client credentials are incorrect.
• Fix: Verify that GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET are correct. Ensure the GenesysAuth class is refreshing the token. Check that the client has the api:query scope assigned in the Genesys Cloud Admin Console under Organization Settings > OAuth 2.0 Clients.

Error: 400 Bad Request

• Cause: The JSON payload is malformed. Common issues include:
  • Invalid ISO 8601 date formats in the where clause.
  • Missing select or groupBy fields.
  • Using an invalid interval value (must be ISO 8601 duration, e.g., PT1H, not 1h).
• Fix: Validate the JSON payload against the OpenAPI specification. Ensure date strings end with Z for UTC.

Error: 403 Forbidden

• Cause: The OAuth client lacks the api:query scope.
• Fix: Log in to the Genesys Cloud Admin Console, navigate to Organization Settings > OAuth 2.0 Clients, select your client, and ensure api:query is checked in the Scopes list. Save and regenerate the token.

Error: Empty Results

• Cause: No conversations match the filter criteria.
• Fix:
  • Verify the user_ids are correct and active.
  • Check the date range. If querying for a future date, results will be empty.
  • Ensure the users were actually handling conversations during the interval.
  • Remove the user_ids filter temporarily to see if any data exists for the date range.

Error: 429 Too Many Requests

• Cause: You have exceeded the rate limit for the Analytics API.
• Fix: The code above includes exponential backoff. If you still hit this limit, reduce the frequency of queries or increase the wait_time multiplier in the fetch_aggregates function. Analytics queries are computationally expensive, so limits are stricter than CRUD APIs.

Official References

• Analytics Conversations Aggregates Query API Documentation
• OAuth 2.0 Client Credentials Grant
• Genesys Cloud Python SDK Reference