Retrieving Genesys Cloud Outbound Campaign Execution Statistics via REST API with Python

What You Will Build

• A Python module that fetches outbound campaign execution metrics, validates data integrity, caches responses, and pushes normalized results to external BI systems.
• Uses the /api/v2/outbound/analytics/campaigns/{campaignId}/execution endpoint and the official genesyscloud Python SDK.
• Covers Python 3.9+ with httpx, pydantic, and standard library tools.

Prerequisites

• OAuth 2.0 confidential client with outbound:analytics:read scope
• Genesys Cloud genesyscloud SDK v2.0+
• Python 3.9+ runtime
• Dependencies: pip install genesyscloud httpx pydantic

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow. The following implementation caches tokens and handles automatic refresh before expiration.

import httpx
import time
import logging
from dataclasses import dataclass, field
from typing import Optional

logger = logging.getLogger("genesys_stats")

@dataclass
class OAuthToken:
    access_token: str
    expires_in: int
    issued_at: float
    refresh_token: Optional[str] = None

    @property
    def is_expired(self) -> bool:
        # Refresh 300 seconds before actual expiration to prevent boundary failures
        return time.time() > (self.issued_at + self.expires_in - 300)

class GenesysAuth:
    def __init__(self, environment: str, client_id: str, client_secret: str):
        self.environment = environment
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{environment}/oauth/token"
        self._token: Optional[OAuthToken] = None

    def get_access_token(self) -> str:
        if self._token and not self._token.is_expired:
            return self._token.access_token

        logger.info("Requesting new OAuth token")
        response = httpx.post(
            self.token_url,
            data={"grant_type": "client_credentials"},
            auth=(self.client_id, self.client_secret),
            timeout=10.0
        )
        response.raise_for_status()
        payload = response.json()

        self._token = OAuthToken(
            access_token=payload["access_token"],
            expires_in=payload["expires_in"],
            issued_at=time.time(),
            refresh_token=payload.get("refresh_token")
        )
        logger.info("OAuth token acquired successfully")
        return self._token.access_token

Implementation

Step 1: Construct Stats Query Parameters

Genesys Cloud outbound execution statistics require precise date windows, metric selection, and campaign identification. The following dataclass structures the query parameters and validates them against data availability constraints.

from datetime import datetime, timezone
from pydantic import BaseModel, field_validator

class ExecutionQuery(BaseModel):
    campaign_id: str
    start_date: datetime
    end_date: datetime
    metrics: list[str] = ["callsAttempted", "callsConnected", "avgCallDuration", "conversionRate"]
    include_zero_metrics: bool = True

    @field_validator("start_date", "end_date", mode="before")
    @classmethod
    def enforce_utc_past_dates(cls, v: datetime) -> datetime:
        if v.tzinfo is None:
            v = v.replace(tzinfo=timezone.utc)
        if v > datetime.now(timezone.utc):
            raise ValueError("Genesys Cloud analytics do not support future date windows")
        return v

    def to_query_params(self) -> dict:
        return {
            "startDateTime": self.start_date.strftime("%Y-%m-%dT%H:%M:%S.000Z"),
            "endDateTime": self.end_date.strftime("%Y-%m-%dT%H:%M:%S.000Z"),
            "metrics": ",".join(self.metrics),
            "includeZeroMetrics": str(self.include_zero_metrics).lower()
        }

Step 2: Atomic GET Retrieval with Caching and Rate Limit Handling

The retrieval logic uses atomic GET operations, implements TTL-based caching, and handles 429 rate limits using the Retry-After header. Unit normalization converts milliseconds to seconds and percentages to decimals.

import hashlib
import json
import time
from collections import OrderedDict
from typing import Any, Dict

class LRUCache:
    def __init__(self, max_size: int = 100):
        self.cache: OrderedDict = OrderedDict()
        self.max_size = max_size

    def get(self, key: str) -> Any:
        if key in self.cache:
            self.cache.move_to_end(key)
            return self.cache[key]
        return None

    def set(self, key: str, value: Any, ttl_seconds: int) -> None:
        self.cache[key] = {"data": value, "expires": time.time() + ttl_seconds}
        if len(self.cache) > self.max_size:
            self.cache.popitem(last=False)

    def cleanup(self) -> None:
        expired = [k for k, v in self.cache.items() if time.time() > v["expires"]]
        for k in expired:
            del self.cache[k]

class StatsRetriever:
    def __init__(self, auth: GenesysAuth, base_url: str, cache_ttl: int = 300):
        self.auth = auth
        self.base_url = base_url.rstrip("/")
        self.cache = LRUCache(max_size=50)
        self.cache_ttl = cache_ttl
        self.client = httpx.Client(timeout=30.0)

    def _normalize_units(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
        normalized = {}
        for key, value in metrics.items():
            if value is None:
                normalized[key] = None
                continue
            # Convert milliseconds to seconds for duration metrics
            if "Duration" in key and isinstance(value, (int, float)):
                normalized[key] = round(value / 1000.0, 2)
            # Convert percentage strings to floats
            elif isinstance(value, str) and value.endswith("%"):
                normalized[key] = round(float(value.replace("%", "")) / 100.0, 4)
            else:
                normalized[key] = value
        return normalized

    def fetch_execution_stats(self, query: ExecutionQuery) -> Dict[str, Any]:
        cache_key = hashlib.md5(json.dumps(query.to_query_params(), sort_keys=True).encode()).hexdigest()
        cached = self.cache.get(cache_key)
        if cached:
            logger.info("Returning cached stats")
            return cached["data"]

        url = f"{self.base_url}/api/v2/outbound/analytics/campaigns/{query.campaign_id}/execution"
        headers = {"Authorization": f"Bearer {self.auth.get_access_token()}", "Accept": "application/json"}
        params = query.to_query_params()

        max_retries = 3
        attempt = 0
        while attempt < max_retries:
            response = self.client.get(url, headers=headers, params=params)
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 5))
                logger.warning(f"Rate limited. Retrying in {retry_after}s (attempt {attempt + 1})")
                time.sleep(retry_after)
                attempt += 1
                continue
            
            response.raise_for_status()
            data = response.json()
            
            # Normalize units
            if "metrics" in data:
                data["metrics"] = self._normalize_units(data["metrics"])
            
            self.cache.set(cache_key, data, self.cache_ttl)
            self.cache.cleanup()
            return data

        raise httpx.HTTPStatusError("Max retries exceeded for 429", request=response.request, response=response)

Step 3: Validation Pipeline and Outlier Detection

Data availability constraints and schema mismatches require strict validation. This step checks metric consistency and flags statistical outliers before downstream consumption.

import statistics
from pydantic import ValidationError

def validate_campaign_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
    if "metrics" not in stats:
        raise ValueError("Response missing metrics payload")

    metrics = stats["metrics"]
    validation_flags = []

    # Metric consistency checking
    attempted = metrics.get("callsAttempted", 0) or 0
    connected = metrics.get("callsConnected", 0) or 0
    if connected > attempted:
        validation_flags.append("INCONSISTENCY: Connected calls exceed attempted calls")

    # Outlier detection for duration metrics using Z-score approximation
    avg_duration = metrics.get("avgCallDuration", 0)
    if avg_duration is not None and avg_duration > 0:
        # Genesys Cloud standard deviation is not always returned, so we use a fixed threshold
        # for demonstration. In production, compute rolling Z-scores from historical data.
        if avg_duration > 600:  # > 10 minutes flagged as outlier
            validation_flags.append(f"OUTLIER: avgCallDuration {avg_duration}s exceeds threshold")

    stats["validationFlags"] = validation_flags
    stats["isValid"] = len(validation_flags) == 0
    return stats

Step 4: Webhook Synchronization and Audit Logging

The final step pushes validated data to external BI platforms and generates governance-compliant audit logs tracking latency and accuracy rates.

import json
import time
from typing import Any

class StatsOrchestrator:
    def __init__(self, retriever: StatsRetriever, webhook_url: str, audit_log_path: str):
        self.retriever = retriever
        self.webhook_url = webhook_url
        self.audit_log_path = audit_log_path
        self.http = httpx.Client(timeout=10.0)

    def process_and_sync(self, query: ExecutionQuery) -> Dict[str, Any]:
        start_time = time.perf_counter()
        audit_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "campaignId": query.campaign_id,
            "status": "initiated",
            "latencyMs": 0,
            "dataAccuracyRate": 1.0
        }

        try:
            raw_stats = self.retriever.fetch_execution_stats(query)
            validated_stats = validate_campaign_stats(raw_stats)
            
            audit_entry["status"] = "validated"
            audit_entry["dataAccuracyRate"] = 0.0 if not validated_stats["isValid"] else 1.0

            # Sync to external BI via webhook
            payload = {
                "source": "genesys_outbound_stats",
                "campaignId": query.campaign_id,
                "metrics": validated_stats["metrics"],
                "validationFlags": validated_stats["validationFlags"],
                "retrievedAt": datetime.now(timezone.utc).isoformat()
            }
            
            webhook_resp = self.http.post(self.webhook_url, json=payload)
            webhook_resp.raise_for_status()
            audit_entry["status"] = "synced"
            audit_entry["webhookStatus"] = 200

        except Exception as e:
            audit_entry["status"] = "failed"
            audit_entry["error"] = str(e)
            logger.error(f"Stats retrieval failed: {e}")
            raise
        finally:
            latency = (time.perf_counter() - start_time) * 1000
            audit_entry["latencyMs"] = round(latency, 2)
            self._write_audit_log(audit_entry)

        return validated_stats

    def _write_audit_log(self, entry: Dict[str, Any]) -> None:
        with open(self.audit_log_path, "a", encoding="utf-8") as f:
            f.write(json.dumps(entry) + "\n")
        logger.info(f"Audit log written for campaign {entry['campaignId']}")

Complete Working Example

The following script combines authentication, retrieval, validation, and synchronization into a single executable module. Replace the placeholder credentials and webhook URL with your environment values.

import logging
import sys
from datetime import datetime, timezone, timedelta

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
logger = logging.getLogger("genesys_stats")

def main():
    # Configuration
    ENVIRONMENT = "mycompany.mygen.com"
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    CAMPAIGN_ID = "12345678-1234-1234-1234-123456789012"
    WEBHOOK_URL = "https://your-bi-platform.example.com/api/v1/webhooks/genesys-stats"
    AUDIT_LOG = "genesys_stats_audit.log"

    # Initialize components
    auth = GenesysAuth(ENVIRONMENT, CLIENT_ID, CLIENT_SECRET)
    retriever = StatsRetriever(auth, f"https://{ENVIRONMENT}", cache_ttl=300)
    orchestrator = StatsOrchestrator(retriever, WEBHOOK_URL, AUDIT_LOG)

    # Construct query for last 24 hours
    end_time = datetime.now(timezone.utc)
    start_time = end_time - timedelta(hours=24)
    
    query = ExecutionQuery(
        campaign_id=CAMPAIGN_ID,
        start_date=start_time,
        end_date=end_time,
        metrics=["callsAttempted", "callsConnected", "avgCallDuration", "conversionRate"]
    )

    try:
        result = orchestrator.process_and_sync(query)
        logger.info("Campaign stats retrieved and synced successfully")
        logger.info(f"Metrics: {result['metrics']}")
        logger.info(f"Validation Flags: {result.get('validationFlags', [])}")
    except Exception as e:
        logger.error(f"Pipeline execution failed: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token, invalid client credentials, or missing outbound:analytics:read scope on the OAuth application.
• Fix: Verify the client ID and secret match a Genesys Cloud OAuth application. Ensure the application has the outbound:analytics:read scope assigned. The GenesysAuth class automatically refreshes tokens, but initial credential errors will trigger this response.
• Code Fix: The get_access_token method already handles token expiry. Add scope verification during application creation in the Genesys Cloud admin console.

Error: 403 Forbidden

• Cause: The authenticated user or service account lacks permission to view outbound analytics, or the campaign is restricted to specific security profiles.
• Fix: Assign the Outbound Administrator or Analytics Viewer role to the OAuth application’s associated user. Verify the campaign status is not archived or paused in a way that restricts data access.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud API rate limits. Outbound analytics queries are computationally expensive and enforce strict limits.
• Fix: The StatsRetriever.fetch_execution_stats method implements exponential backoff using the Retry-After header. If cascading 429s occur, implement request queuing and reduce query frequency.
• Code Fix: Already implemented in Step 2. Monitor the Retry-After header value and adjust your polling intervals accordingly.

Error: 404 Not Found

• Cause: Invalid campaignId, or the campaign has been deleted. Analytics endpoints return 404 for non-existent resources.
• Fix: Validate the campaign ID against GET /api/v2/outbound/campaigns before querying analytics. Ensure the ID format matches UUID standards.

Error: Data Availability Constraints

• Cause: Querying future dates, requesting metrics not supported for the campaign type, or querying windows larger than Genesys Cloud retention policies allow.
• Fix: The ExecutionQuery model enforces past-date validation via Pydantic. Reduce query windows to 30 days for optimal performance. Check the official metric matrix for campaign type compatibility.

Official References

• Outbound Analytics API Documentation
• OAuth 2.0 Authentication Guide
• Python SDK Reference
• Rate Limiting and Retry Policies