Forecasting Genesys Cloud Queue Volumes with Python and Prophet

What You Will Build

• This script downloads historical queue interval metrics from Genesys Cloud, trains a Prophet time series model to predict future contact volumes, and automatically generates outbound campaign schedule requests aligned with forecasted peaks.
• The implementation relies on the Genesys Cloud Analytics API and Outbound API for data retrieval and campaign provisioning.
• The code is written in Python 3.9 using requests, pandas, and prophet.

Prerequisites

• OAuth 2.0 Client Credentials grant with scopes: analytics:report:read, outbound:campaign:readwrite
• Genesys Cloud REST API v2
• Python 3.9 or higher
• External dependencies: pip install requests pandas prophet numpy

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow for machine-to-machine authentication. The token expires after one hour, so the implementation includes a cache with automatic refresh logic.

import requests
import time
import json
from typing import Optional, Dict, Any

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, environment: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = f"https://{environment}.mygen.com"
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0

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

        url = f"{self.base_url}/oauth/token"
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "analytics:report:read outbound:campaign:readwrite"
        }

        response = requests.post(url, headers=headers, data=payload)
        response.raise_for_status()
        data = response.json()

        self.token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"] - 30
        return self.token

    def get_headers(self) -> Dict[str, str]:
        return {
            "Authorization": f"Bearer {self.get_token()}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

The get_token method checks the local cache before making a network call. The token_expiry subtracts thirty seconds to prevent boundary failures during long-running analytics queries. The get_headers method returns a complete header dictionary ready for downstream API calls.

Implementation

Step 1: Download Historical Interval Metrics

The Analytics API returns paginated interval data. The endpoint POST /api/v2/analytics/queues/details/query supports the interval query type, which aggregates metrics by time window. Genesys Cloud enforces strict rate limits on analytics queries, so the implementation includes exponential backoff for 429 responses.

import requests
from typing import List, Dict, Any

def fetch_interval_metrics(
    auth: GenesysAuth,
    queue_id: str,
    date_from: str,
    date_to: str,
    interval: str = "PT1H",
    metrics: List[str] = None
) -> List[Dict[str, Any]]:
    if metrics is None:
        metrics = ["contactCount", "handleCount"]

    url = f"https://{auth.base_url.split('/')[2]}/api/v2/analytics/queues/details/query"
    headers = auth.get_headers()
    
    base_payload = {
        "dateFrom": date_from,
        "dateTo": date_to,
        "interval": interval,
        "metrics": metrics,
        "groupBy": ["queueId"],
        "queryType": "interval",
        "pageSize": 2000,
        "filters": {"queueId": {"values": [queue_id]}}
    }

    all_results = []
    next_page_token = None
    max_retries = 4

    while True:
        payload = base_payload.copy()
        if next_page_token:
            payload["nextPageToken"] = next_page_token

        for attempt in range(max_retries):
            response = requests.post(url, headers=headers, json=payload)
            
            if response.status_code == 429:
                wait_time = 2 ** attempt
                print(f"Rate limited (429). Retrying in {wait_time}s...")
                time.sleep(wait_time)
                continue
            
            response.raise_for_status()
            break
        else:
            raise RuntimeError("Max retries exceeded for analytics query.")

        data = response.json()
        all_results.extend(data.get("entities", []))
        
        next_page_token = data.get("nextPageToken")
        if not next_page_token:
            break

    return all_results

Required Scope: analytics:report:read
Expected Response Structure: The API returns an entities array containing interval objects. Each object includes intervalFrom, intervalTo, and metric values like contactCount. The nextPageToken field drives pagination. The retry loop handles 429 rate limits by doubling the wait time on each attempt.

Step 2: Preprocess Time Series Data

Prophet requires a DataFrame with exactly two columns: ds (datetime) and y (numeric value). The raw Analytics API response contains multiple metrics and nested structures. This step flattens the data, handles missing intervals, and applies forward filling to maintain continuous time series.

import pandas as pd
import numpy as np

def preprocess_analytics_data(raw_entities: List[Dict[str, Any]]) -> pd.DataFrame:
    records = []
    for entity in raw_entities:
        interval_from = entity.get("intervalFrom", entity.get("dateFrom"))
        contact_count = entity.get("contactCount", 0)
        
        records.append({
            "ds": pd.to_datetime(interval_from),
            "y": contact_count
        })

    df = pd.DataFrame(records)
    
    if df.empty:
        raise ValueError("No data returned from Analytics API.")

    df = df.drop_duplicates(subset=["ds"])
    df = df.sort_values("ds").reset_index(drop=True)

    # Create a complete hourly index to handle missing intervals
    complete_index = pd.date_range(start=df["ds"].min(), end=df["ds"].max(), freq="H")
    df = df.set_index("ds").reindex(complete_index).fillna(method="ffill").reset_index()
    df.columns = ["ds", "y"]
    
    # Ensure y is numeric and replace any remaining NaN with 0
    df["y"] = pd.to_numeric(df["y"], errors="coerce").fillna(0).astype(int)
    
    return df

The reindex operation with freq="H" guarantees a continuous hourly series. Prophet cannot handle gaps in the ds column, so forward filling preserves the trend without introducing artificial zeros. The y column is explicitly cast to integers to prevent Prophet from misinterpreting floating-point precision noise.

Step 3: Train Prophet Model for Volume Prediction

Prophet automatically detects daily and weekly seasonality. The configuration explicitly sets seasonality_mode to additive to prevent multiplicative scaling from distorting low-volume periods. The model generates a future DataFrame and returns predictions with confidence intervals.

from prophet import Prophet
from datetime import timedelta

def train_prophet_model(
    df: pd.DataFrame,
    forecast_days: int = 7,
    confidence_interval: float = 0.95
) -> pd.DataFrame:
    model = Prophet(
        daily_seasonality=True,
        weekly_seasonality=True,
        seasonality_mode="additive",
        interval_width=confidence_interval,
        changepoint_prior_scale=0.05
    )
    
    model.fit(df)
    
    future = model.make_future_dataframe(periods=forecast_days * 24, freq="H")
    forecast = model.predict(future)
    
    return forecast

Parameter Explanation: changepoint_prior_scale controls flexibility. A value of 0.05 prevents overfitting to short-term spikes while allowing the model to adapt to gradual volume shifts. The periods parameter uses hourly frequency to match the input data granularity.

Step 4: Construct Outbound Campaign API Requests

The Outbound API allows programmatic campaign creation. This step extracts forecasted peak hours and constructs a POST /api/v2/outbound/campaigns payload that schedules wrap-up tasks during predicted low-volume windows. Genesys Cloud validates campaign schedules strictly, so the payload includes explicit startDateTime and endDateTime fields.

def construct_campaign_request(
    forecast: pd.DataFrame,
    auth: GenesysAuth,
    campaign_name: str = "Forecast-Driven Wrap-Up Campaign",
    max_calls_per_hour: int = 50
) -> Dict[str, Any]:
    # Identify low-volume windows (bottom 25th percentile)
    threshold = forecast["yhat"].quantile(0.25)
    low_volume_periods = forecast[forecast["yhat"] < threshold]
    
    if low_volume_periods.empty:
        raise ValueError("Forecast contains no low-volume windows for scheduling.")

    start_time = low_volume_periods["ds"].iloc[0]
    end_time = low_volume_periods["ds"].iloc[-1] + timedelta(hours=1)

    campaign_payload = {
        "name": campaign_name,
        "description": f"Auto-scheduled based on Prophet forecast. Threshold: {threshold:.1f}",
        "type": "OUTBOUND",
        "status": "DRAFT",
        "maxCallsPerHour": max_calls_per_hour,
        "schedule": [
            {
                "startDateTime": start_time.isoformat(),
                "endDateTime": end_time.isoformat(),
                "repeat": "NONE"
            }
        ],
        "dialingMode": "PREDICTIVE",
        "callCenter": {
            "id": auth.base_url.split(".")[0].split("//")[1] 
        }
    }

    return campaign_payload

Required Scope: outbound:campaign:readwrite
Expected Response: A 201 Created response containing the campaign id, version, and createdBy. The payload uses DRAFT status to allow validation before activation. The schedule array aligns campaign execution with forecasted troughs to minimize queue contention.

Step 5: Implement Retraining Triggers Based on Forecast Error

Model drift occurs when actual volumes deviate significantly from predictions. This function calculates Mean Absolute Percentage Error (MAPE) and returns a boolean flag when the error exceeds a configurable threshold.

def check_retraining_trigger(
    actuals: pd.DataFrame,
    forecast: pd.DataFrame,
    threshold_mape: float = 0.15
) -> bool:
    # Merge actuals and forecast on datetime
    merged = pd.merge(
        actuals.rename(columns={"y": "actual"}),
        forecast[["ds", "yhat"]].rename(columns={"yhat": "predicted"}),
        on="ds",
        how="inner"
    )
    
    if merged.empty:
        return False

    # Calculate MAPE
    merged["ape"] = np.abs((merged["actual"] - merged["predicted"]) / merged["actual"].replace(0, 1))
    mape = merged["ape"].mean()

    print(f"Current MAPE: {mape:.4f} | Threshold: {threshold_mape:.4f}")
    return mape > threshold_mape

The replace(0, 1) operation prevents division by zero during percentage error calculation. When mape exceeds 0.15 (15%), the function returns True, signaling that the pipeline should fetch fresh Analytics data and retrain the Prophet model.

Step 6: Export Confidence Intervals to JSON Configuration

Downstream systems require structured confidence intervals for capacity planning. This step filters the forecast for future dates and exports yhat, yhat_lower, and yhat_upper to a JSON file.

import json
from datetime import datetime

def export_forecast_config(
    forecast: pd.DataFrame,
    output_path: str = "forecast_config.json"
) -> str:
    cutoff = datetime.utcnow()
    future_forecast = forecast[forecast["ds"] > cutoff].copy()
    
    future_forecast["ds"] = future_forecast["ds"].astype(str)
    
    config = {
        "generatedAt": datetime.utcnow().isoformat(),
        "confidenceLevel": 0.95,
        "intervals": future_forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]].to_dict(orient="records")
    }

    with open(output_path, "w") as f:
        json.dump(config, f, indent=2)

    print(f"Forecast configuration exported to {output_path}")
    return output_path

The orient="records" parameter produces an array of objects, which matches standard configuration file schemas. The generatedAt timestamp enables cache invalidation logic in consuming services.

Complete Working Example

import requests
import time
import json
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from prophet import Prophet
from typing import List, Dict, Any, Optional

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, environment: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = f"https://{environment}.mygen.com"
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_token(self) -> str:
        if self.token and time.time() < self.token_expiry:
            return self.token
        url = f"{self.base_url}/oauth/token"
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "analytics:report:read outbound:campaign:readwrite"
        }
        response = requests.post(url, headers=headers, data=payload)
        response.raise_for_status()
        data = response.json()
        self.token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"] - 30
        return self.token

    def get_headers(self) -> Dict[str, str]:
        return {
            "Authorization": f"Bearer {self.get_token()}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

def fetch_interval_metrics(auth: GenesysAuth, queue_id: str, date_from: str, date_to: str) -> List[Dict[str, Any]]:
    url = f"https://{auth.base_url.split('/')[2]}/api/v2/analytics/queues/details/query"
    headers = auth.get_headers()
    base_payload = {
        "dateFrom": date_from,
        "dateTo": date_to,
        "interval": "PT1H",
        "metrics": ["contactCount"],
        "groupBy": ["queueId"],
        "queryType": "interval",
        "pageSize": 2000,
        "filters": {"queueId": {"values": [queue_id]}}
    }
    all_results = []
    next_page_token = None
    max_retries = 4
    while True:
        payload = base_payload.copy()
        if next_page_token:
            payload["nextPageToken"] = next_page_token
        for attempt in range(max_retries):
            response = requests.post(url, headers=headers, json=payload)
            if response.status_code == 429:
                time.sleep(2 ** attempt)
                continue
            response.raise_for_status()
            break
        else:
            raise RuntimeError("Max retries exceeded.")
        all_results.extend(response.json().get("entities", []))
        next_page_token = response.json().get("nextPageToken")
        if not next_page_token:
            break
    return all_results

def preprocess_analytics_data(raw_entities: List[Dict[str, Any]]) -> pd.DataFrame:
    records = [{"ds": pd.to_datetime(e.get("intervalFrom")), "y": e.get("contactCount", 0)} for e in raw_entities]
    df = pd.DataFrame(records).drop_duplicates(subset=["ds"]).sort_values("ds").reset_index(drop=True)
    complete_index = pd.date_range(start=df["ds"].min(), end=df["ds"].max(), freq="H")
    df = df.set_index("ds").reindex(complete_index).fillna(method="ffill").reset_index()
    df.columns = ["ds", "y"]
    df["y"] = pd.to_numeric(df["y"], errors="coerce").fillna(0).astype(int)
    return df

def train_prophet_model(df: pd.DataFrame, forecast_days: int = 7) -> pd.DataFrame:
    model = Prophet(daily_seasonality=True, weekly_seasonality=True, seasonality_mode="additive", interval_width=0.95, changepoint_prior_scale=0.05)
    model.fit(df)
    future = model.make_future_dataframe(periods=forecast_days * 24, freq="H")
    return model.predict(future)

def construct_campaign_request(forecast: pd.DataFrame, auth: GenesysAuth) -> Dict[str, Any]:
    threshold = forecast["yhat"].quantile(0.25)
    low_vol = forecast[forecast["yhat"] < threshold]
    start_time = low_vol["ds"].iloc[0]
    end_time = low_vol["ds"].iloc[-1] + timedelta(hours=1)
    return {
        "name": "Forecast-Driven Wrap-Up Campaign",
        "type": "OUTBOUND",
        "status": "DRAFT",
        "maxCallsPerHour": 50,
        "schedule": [{"startDateTime": start_time.isoformat(), "endDateTime": end_time.isoformat(), "repeat": "NONE"}],
        "dialingMode": "PREDICTIVE",
        "callCenter": {"id": auth.base_url.split(".")[0].split("//")[1]}
    }

def check_retraining_trigger(actuals: pd.DataFrame, forecast: pd.DataFrame, threshold: float = 0.15) -> bool:
    merged = pd.merge(actuals.rename(columns={"y": "actual"}), forecast[["ds", "yhat"]].rename(columns={"yhat": "pred"}), on="ds", how="inner")
    if merged.empty: return False
    mape = np.abs((merged["actual"] - merged["pred"]) / merged["actual"].replace(0, 1)).mean()
    return mape > threshold

def export_forecast_config(forecast: pd.DataFrame, output_path: str = "forecast_config.json") -> str:
    future = forecast[forecast["ds"] > datetime.utcnow()].copy()
    future["ds"] = future["ds"].astype(str)
    config = {"generatedAt": datetime.utcnow().isoformat(), "confidenceLevel": 0.95, "intervals": future[["ds", "yhat", "yhat_lower", "yhat_upper"]].to_dict(orient="records")}
    with open(output_path, "w") as f:
        json.dump(config, f, indent=2)
    return output_path

if __name__ == "__main__":
    # Replace with actual credentials
    auth = GenesysAuth(client_id="your_client_id", client_secret="your_client_secret", environment="your_env")
    queue_id = "your_queue_id"
    date_from = (datetime.utcnow() - timedelta(days=90)).isoformat()
    date_to = datetime.utcnow().isoformat()

    raw_data = fetch_interval_metrics(auth, queue_id, date_from, date_to)
    df = preprocess_analytics_data(raw_data)
    forecast = train_prophet_model(df)
    
    campaign_payload = construct_campaign_request(forecast, auth)
    print("Campaign Payload:", json.dumps(campaign_payload, indent=2))
    
    needs_retrain = check_retraining_trigger(df, forecast)
    print(f"Retraining required: {needs_retrain}")
    
    export_forecast_config(forecast)

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token or missing analytics:report:read scope.
• Fix: Verify the client credentials match a registered OAuth client. Ensure the scope string in the token request exactly matches analytics:report:read outbound:campaign:readwrite. The GenesysAuth class automatically refreshes tokens, but manual credential rotation in the admin console will invalidate cached tokens immediately.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud rate limits on the Analytics API. Interval queries consume higher quota due to server-side aggregation.
• Fix: The implementation includes exponential backoff. If failures persist, reduce the pageSize to 1000 and increase the query frequency. Implement request throttling by adding time.sleep(1) between pagination loops.

Error: 400 Bad Request on Outbound Campaign Creation

• Cause: Invalid date format or missing required campaign fields. Genesys Cloud requires ISO 8601 timestamps with timezone offsets.
• Fix: Ensure startDateTime and endDateTime include Z or +00:00 suffix. The isoformat() method produces valid strings, but verify the callCenter.id matches your organization ID. Use the GET /api/v2/outbound/campaigns endpoint to validate payload structure before posting.

Error: Prophet Warning “Data contains fewer than 2 periods”

• Cause: Insufficient historical data for seasonality detection. Prophet requires at least two full seasonal cycles (e.g., 14 days for weekly seasonality).
• Fix: Extend date_from to cover at least 60 days. If historical data is sparse, disable daily seasonality by setting daily_seasonality=False in the Prophet constructor.

Official References

• Analytics Queues Details Query API
• Outbound Campaigns API
• Python SDK Reference
• Prophet Documentation