Building a custom interval report using the Analytics Conversations Aggregates query

What You Will Build

• A Python script that queries Genesys Cloud for aggregated conversation metrics across configurable time intervals, processes paginated results, and exports the data to a structured dictionary.
• This implementation uses the POST /api/v2/analytics/conversations/aggregates/query endpoint.
• The tutorial covers Python 3.9+ with httpx and modern type hinting.

Prerequisites

• OAuth Confidential Client configured in Genesys Cloud Admin Console
• Required scope: analytics:conversation:view
• Runtime: Python 3.9 or higher
• Dependencies: httpx==0.27.0, pydantic==2.6.0 (optional but recommended for validation)
• Base URL: https://mycompany.mygenesyscloud.com (replace with your environment)

Authentication Setup

Genesys Cloud uses OAuth 2.0 Client Credentials flow for server-to-server analytics queries. You must cache the access token and handle expiration gracefully. The analytics API rejects requests with expired tokens, returning a 401 status.

import httpx
import time
import json
from typing import Optional

class GenesysOAuthClient:
    def __init__(self, client_id: str, client_secret: str, base_url: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"{base_url}/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0
        self.http_client = httpx.Client(timeout=30.0)

    def _fetch_token(self) -> dict:
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "analytics:conversation:view"
        }
        response = self.http_client.post(
            self.token_url,
            data=payload,
            headers={"Content-Type": "application/x-www-form-urlencoded"}
        )
        response.raise_for_status()
        return response.json()

    def get_access_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 30:
            return self.access_token

        token_data = self._fetch_token()
        self.access_token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"]
        return self.access_token

The get_access_token method checks the cached token against the expiry timestamp minus a thirty-second buffer. This prevents race conditions where a token expires mid-request. The analytics:conversation:view scope is strictly enforced by the analytics service.

Implementation

Step 1: Construct the Aggregates Query Payload

The analytics aggregates endpoint accepts a JSON body conforming to the AnalyticsConversationAggregatesQuery schema. You must specify dateFrom, dateTo, interval, groupBy, and metrics. The interval field uses ISO 8601 duration format. Grouping by date is required for interval reports.

HTTP Request Cycle Example:

POST /api/v2/analytics/conversations/aggregates/query HTTP/1.1
Host: mycompany.mygenesyscloud.com
Authorization: Bearer <access_token>
Content-Type: application/json

{
  "dateFrom": "2024-01-01T00:00:00Z",
  "dateTo": "2024-01-02T00:00:00Z",
  "interval": "PT15M",
  "groupBy": ["date"],
  "metrics": [
    "conversationCount",
    "handledCount",
    "abandonedCount",
    "averageHandleTime"
  ]
}

Realistic Response Body:

{
  "page": 1,
  "pageSize": 100,
  "nextPage": "/api/v2/analytics/conversations/aggregates/query?page=2",
  "entities": [
    {
      "date": "2024-01-01T00:00:00.000Z",
      "metrics": {
        "conversationCount": 142,
        "handledCount": 138,
        "abandonedCount": 4,
        "averageHandleTime": 34520
      }
    }
  ]
}

The nextPage field drives pagination. The analytics service returns a maximum of 100 entities per page by default. You must follow nextPage until it resolves to null.

from typing import Dict, Any

def build_aggregates_query(
    date_from: str,
    date_to: str,
    interval: str,
    metrics: list[str]
) -> Dict[str, Any]:
    query_payload: Dict[str, Any] = {
        "dateFrom": date_from,
        "dateTo": date_to,
        "interval": interval,
        "groupBy": ["date"],
        "metrics": metrics
    }
    return query_payload

Step 2: Execute the Query with Retry and Pagination

The analytics API enforces strict rate limits. A 429 Too Many Requests response includes a Retry-After header. You must implement exponential backoff with jitter. The following function handles initial execution, pagination, and retry logic for transient failures.

import httpx
import time
import random
from typing import List, Dict, Any, Optional

def execute_aggregates_query(
    client: httpx.Client,
    base_url: str,
    token: str,
    payload: Dict[str, Any]
) -> List[Dict[str, Any]]:
    all_entities: List[Dict[str, Any]] = []
    next_page_url: Optional[str] = f"{base_url}/api/v2/analytics/conversations/aggregates/query"
    
    max_retries = 4
    base_delay = 2.0
    
    while next_page_url:
        for attempt in range(max_retries):
            try:
                response = client.post(
                    next_page_url,
                    json=payload,
                    headers={
                        "Authorization": f"Bearer {token}",
                        "Content-Type": "application/json"
                    }
                )
                
                if response.status_code == 200:
                    data = response.json()
                    entities = data.get("entities", [])
                    all_entities.extend(entities)
                    next_page_url = data.get("nextPage")
                    
                    # Convert relative nextPage to absolute URL if necessary
                    if next_page_url and next_page_url.startswith("/"):
                        next_page_url = f"{base_url}{next_page_url}"
                    
                    break  # Success, proceed to next page
                    
                elif response.status_code == 429:
                    retry_after = float(response.headers.get("Retry-After", base_delay * (2 ** attempt)))
                    jitter = random.uniform(0, 0.5)
                    wait_time = retry_after + jitter
                    print(f"Rate limited (429). Retrying in {wait_time:.2f}s...")
                    time.sleep(wait_time)
                    continue
                    
                elif response.status_code == 401:
                    raise httpx.HTTPStatusError("Token expired. Refresh required.", request=response.request, response=response)
                    
                else:
                    response.raise_for_status()
                    
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 429:
                    continue
                raise
                
    return all_entities

The retry loop catches 429 responses, reads the Retry-After header, applies exponential backoff, and adds random jitter to prevent thundering herd scenarios. If the token expires during pagination, the function raises an exception so the caller can refresh the token and restart the query.

Step 3: Flatten and Export Interval Metrics

The raw response nests metrics inside each entity. You must flatten the structure for downstream consumption. The following function transforms the paginated list into a clean tabular format.

def flatten_interval_results(entities: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    flattened: List[Dict[str, Any]] = []
    
    for entity in entities:
        row: Dict[str, Any] = {"interval_start": entity["date"]}
        metrics = entity.get("metrics", {})
        
        for metric_name, metric_value in metrics.items():
            # Convert handle time from milliseconds to seconds for readability
            if metric_name == "averageHandleTime" and metric_value is not None:
                row[metric_name] = round(metric_value / 1000, 2)
            else:
                row[metric_name] = metric_value
                
        flattened.append(row)
        
    return flattened

This transformation preserves null values, converts averageHandleTime from milliseconds to seconds, and aligns all metrics into a single dictionary per interval. You can pipe this output directly into pandas.DataFrame, a CSV writer, or a database bulk insert.

Complete Working Example

The following script combines authentication, query construction, execution, and result flattening into a single runnable module. Replace the placeholder credentials with your environment values.

import httpx
import time
import random
import json
from typing import Optional, List, Dict, Any

class GenesysAnalyticsReporter:
    def __init__(self, client_id: str, client_secret: str, base_url: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = base_url.rstrip("/")
        self.token_url = f"{self.base_url}/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0
        self.http_client = httpx.Client(timeout=30.0)

    def get_access_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 30:
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "analytics:conversation:view"
        }
        response = self.http_client.post(
            self.token_url,
            data=payload,
            headers={"Content-Type": "application/x-www-form-urlencoded"}
        )
        response.raise_for_status()
        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"]
        return self.access_token

    def fetch_interval_report(self, date_from: str, date_to: str, interval: str) -> List[Dict[str, Any]]:
        token = self.get_access_token()
        
        payload = {
            "dateFrom": date_from,
            "dateTo": date_to,
            "interval": interval,
            "groupBy": ["date"],
            "metrics": ["conversationCount", "handledCount", "abandonedCount", "averageHandleTime"]
        }
        
        all_entities: List[Dict[str, Any]] = []
        next_page_url: Optional[str] = f"{self.base_url}/api/v2/analytics/conversations/aggregates/query"
        max_retries = 4
        
        while next_page_url:
            for attempt in range(max_retries):
                try:
                    response = self.http_client.post(
                        next_page_url,
                        json=payload,
                        headers={
                            "Authorization": f"Bearer {token}",
                            "Content-Type": "application/json"
                        }
                    )
                    
                    if response.status_code == 200:
                        data = response.json()
                        all_entities.extend(data.get("entities", []))
                        next_page_url = data.get("nextPage")
                        if next_page_url and next_page_url.startswith("/"):
                            next_page_url = f"{self.base_url}{next_page_url}"
                        break
                        
                    elif response.status_code == 429:
                        retry_after = float(response.headers.get("Retry-After", 2.0 * (2 ** attempt)))
                        time.sleep(retry_after + random.uniform(0, 0.5))
                        continue
                    else:
                        response.raise_for_status()
                        
                except httpx.HTTPStatusError as e:
                    if e.response.status_code == 429:
                        continue
                    raise
                    
        return self._flatten_results(all_entities)

    def _flatten_results(self, entities: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        flattened: List[Dict[str, Any]] = []
        for entity in entities:
            row: Dict[str, Any] = {"interval_start": entity["date"]}
            metrics = entity.get("metrics", {})
            for name, value in metrics.items():
                if name == "averageHandleTime" and value is not None:
                    row[name] = round(value / 1000, 2)
                else:
                    row[name] = value
            flattened.append(row)
        return flattened

if __name__ == "__main__":
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    BASE_URL = "https://mycompany.mygenesyscloud.com"
    
    reporter = GenesysAnalyticsReporter(CLIENT_ID, CLIENT_SECRET, BASE_URL)
    
    report_data = reporter.fetch_interval_report(
        date_from="2024-01-01T00:00:00Z",
        date_to="2024-01-02T00:00:00Z",
        interval="PT15M"
    )
    
    print(json.dumps(report_data, indent=2))

Common Errors & Debugging

Error: 400 Bad Request

• Cause: The query payload violates schema constraints. Common triggers include dateTo being earlier than dateFrom, an invalid ISO 8601 interval string, or requesting a metric that does not exist in the groupBy context.
• Fix: Validate the interval string against PT15M, PT1H, or P1D. Ensure dateFrom and dateTo use strict ISO 8601 format with Z suffix. Verify that all requested metrics are supported for conversation aggregates.
• Code showing the fix: Add a validation step before execution.

if date_from >= date_to:
    raise ValueError("dateFrom must be strictly earlier than dateTo")
if interval not in ["PT15M", "PT30M", "PT1H", "P1D"]:
    raise ValueError("Invalid interval format. Use ISO 8601 duration.")

Error: 403 Forbidden

• Cause: The OAuth token lacks the analytics:conversation:view scope, or the associated user account has insufficient platform permissions.
• Fix: Verify the client credentials scope in the Admin Console under Applications > API Keys. Confirm the service user has the Analytics > View Conversations permission assigned.
• Code showing the fix: Explicitly request the correct scope during token acquisition.

payload["scope"] = "analytics:conversation:view"

Error: 429 Too Many Requests

• Cause: You exceeded the analytics query rate limit. Genesys Cloud enforces per-tenant and per-endpoint throttling. The analytics service returns Retry-After in seconds.
• Fix: Implement exponential backoff with jitter. Do not retry synchronously in tight loops. Cache results when possible.
• Code showing the fix: The retry loop in Step 2 already implements this pattern. Ensure you read the Retry-After header instead of using a static sleep value.

Error: 503 Service Unavailable

• Cause: The analytics warehouse is undergoing maintenance or experiencing high query load. Aggregates queries hit the data warehouse, which has separate availability windows from the real-time APIs.
• Fix: Implement a circuit breaker pattern. Retry after a minimum of thirty seconds. If the 503 persists for more than five minutes, defer the query and alert operations staff.
• Code showing the fix: Add a 503 handler to the retry loop.

elif response.status_code == 503:
    time.sleep(30 + random.uniform(0, 10))
    continue

Official References

• Analytics Conversations Aggregates Query API Documentation
• OAuth Client Credentials Flow Documentation
• Genesys Cloud Analytics Scopes Reference
• Rate Limiting and Retry Guidelines