Optimizing Genesys Cloud Predictive Dial Ratios with Real-Time Disposition Analysis and KDE

What You Will Build

• A Python script that polls the Genesys Cloud Outbound Campaign API for recent call dispositions, computes agent wrap-up time distributions using kernel density estimation, and updates the predictive dial ratio to maintain a target answer rate.
• The solution uses the Genesys Cloud CX REST API endpoints /api/v2/outbound/campaigns/{campaignId}/calls and PUT /api/v2/outbound/campaigns/{campaignId}.
• The implementation is written in Python 3.9+ using httpx, scipy, and numpy.

Prerequisites

• OAuth 2.0 Client Credentials grant configured in Genesys Cloud with outbound:campaign:read and outbound:campaign:write scopes.
• Genesys Cloud REST API v2.
• Python 3.9 or newer.
• External dependencies: httpx>=0.24.0, scipy>=1.10.0, numpy>=1.24.0.
• An active outbound campaign ID with predictive dialing enabled.

Authentication Setup

Genesys Cloud uses JWT bearer tokens issued via the OAuth 2.0 client credentials flow. Tokens expire after 3600 seconds. Production scripts must cache the token and refresh it automatically when expired or when the API returns a 401 status.

The following function handles token acquisition and caching. It stores the expiration timestamp and returns a fresh token when the current one is invalid.

import httpx
import time
import logging
from typing import Optional

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

class GenesysAuth:
    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: Optional[str] = None
        self.token_expiry: float = 0.0
        self.http_client = httpx.Client(timeout=15.0)

    def _fetch_token(self) -> str:
        url = f"{self.base_url}/login/oauth2/token"
        body = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = self.http_client.post(url, data=body)
        response.raise_for_status()
        data = response.json()
        return data["access_token"], data["expires_in"]

    def get_valid_token(self) -> str:
        if time.time() >= self.token_expiry - 30.0:
            logger.info("Token expired or nearing expiry. Refreshing.")
            self.token, expires_in = self._fetch_token()
            self.token_expiry = time.time() + expires_in
        return self.token

    def close(self):
        self.http_client.close()

Implementation

Step 1: Ingest Real-Time Call Disposition Events

The Outbound Campaign API provides recent call records via GET /api/v2/outbound/campaigns/{campaignId}/calls. We poll this endpoint at a fixed interval to capture completed calls. The response includes disposition, wrap_up_time, and created_time. We filter for calls with a valid disposition and positive wrap-up time to feed into the statistical model.

def fetch_recent_calls(auth: GenesysAuth, campaign_id: str, limit: int = 100) -> list:
    url = f"{auth.base_url}/api/v2/outbound/campaigns/{campaign_id}/calls"
    params = {"limit": limit, "sort_by": "created_time:desc"}
    headers = {
        "Authorization": f"Bearer {auth.get_valid_token()}",
        "Accept": "application/json",
        "Content-Type": "application/json"
    }

    response = httpx.get(url, headers=headers, params=params)
    
    if response.status_code == 429:
        retry_after = int(response.headers.get("Retry-After", 5))
        logger.warning("Rate limited (429). Waiting %s seconds.", retry_after)
        time.sleep(retry_after)
        return fetch_recent_calls(auth, campaign_id, limit)
    
    response.raise_for_status()
    return response.json().get("calls", [])

Expected Response Snippet:

{
  "calls": [
    {
      "id": "call-uuid-1",
      "campaign_id": "camp-uuid",
      "disposition": "connected",
      "wrap_up_time": 45.2,
      "created_time": "2024-05-15T10:30:00.000Z",
      "outcome": "completed"
    },
    {
      "id": "call-uuid-2",
      "campaign_id": "camp-uuid",
      "disposition": "no_answer",
      "wrap_up_time": 0.0,
      "created_time": "2024-05-15T10:29:45.000Z",
      "outcome": "failed"
    }
  ],
  "total": 2
}

We extract only completed calls with positive wrap-up times. This dataset forms the basis for the density estimation.

Step 2: Recalculate Wrap-Up Time Distributions Using KDE

Agent wrap-up times are rarely normally distributed. They often exhibit skewness with a long tail of complex handling cases. Kernel Density Estimation (KDE) provides a non-parametric probability density function that accurately models the underlying distribution without assuming normality.

We use scipy.stats.gaussian_kde to fit the distribution. The 75th percentile of the KDE represents the time by which 75 percent of agents are ready to receive the next call. This threshold directly informs the safe upper bound for the predictive dial ratio.

import numpy as np
from scipy.stats import gaussian_kde

def calculate_wrapup_kde_statistics(wrap_up_times: list[float]) -> dict:
    if not wrap_up_times:
        return {"p75": 30.0, "density": None}
    
    data = np.array(wrap_up_times)
    kde = gaussian_kde(data)
    
    # Generate points for percentile calculation
    x_range = np.linspace(min(data), max(data), 500)
    pdf = kde.evaluate(x_range)
    
    # Calculate cumulative distribution function numerically
    cdf = np.cumsum(pdf) * (x_range[1] - x_range[0])
    cdf = cdf / cdf[-1]  # Normalize to 1.0
    
    # Find 75th percentile
    p75_idx = np.searchsorted(cdf, 0.75)
    p75_time = float(x_range[p75_idx])
    
    return {"p75": p75_time, "kde": kde}

The p75 value indicates that three out of four agents will complete their wrap-up within this duration. A lower p75 allows a higher dial ratio because agents return to the queue faster. A higher p75 requires a conservative ratio to prevent abandoned calls.

Step 3: Dynamically Adjust Predictive Dial Ratio

The predictive dial ratio controls how many calls the system initiates relative to available agents. We maintain a target answer rate (e.g., 0.85). The script calculates the current answer rate from the ingested calls, compares it to the target, and adjusts the ratio accordingly.

The adjustment logic applies a dampening factor to prevent oscillation. The KDE-derived p75 wrap-up time caps the maximum allowable ratio. Genesys Cloud enforces a minimum ratio of 0.1 and a maximum of 5.0.

def calculate_adjusted_ratio(
    current_answer_rate: float,
    target_answer_rate: float,
    current_ratio: float,
    p75_wrapup: float
) -> float:
    # Dampening factor to prevent aggressive oscillation
    adjustment_step = 0.15
    
    if current_answer_rate < target_answer_rate - 0.05:
        # Answer rate too low. Decrease ratio.
        new_ratio = current_ratio - adjustment_step
    elif current_answer_rate > target_answer_rate + 0.05:
        # Answer rate high. Increase ratio, but cap by agent readiness.
        # Shorter wrap-up times allow higher ratios.
        max_safe_ratio = max(0.5, 5.0 - (p75_wrapup / 20.0))
        new_ratio = min(current_ratio + adjustment_step, max_safe_ratio)
    else:
        # Within tolerance. Maintain ratio.
        new_ratio = current_ratio
        
    # Enforce Genesys Cloud platform bounds
    return float(np.clip(new_ratio, 0.1, 5.0))

def update_campaign_ratio(auth: GenesysAuth, campaign_id: str, new_ratio: float) -> dict:
    url = f"{auth.base_url}/api/v2/outbound/campaigns/{campaign_id}"
    headers = {
        "Authorization": f"Bearer {auth.get_valid_token()}",
        "Accept": "application/json",
        "Content-Type": "application/json"
    }
    body = {"predictive_dial_ratio": new_ratio}
    
    response = httpx.put(url, headers=headers, json=body)
    
    if response.status_code == 409:
        logger.error("Conflict (409). Campaign may be locked or invalid state.")
    elif response.status_code == 400:
        logger.error("Bad Request (400). Invalid ratio value or missing required fields.")
        logger.error("Response: %s", response.text)
    else:
        response.raise_for_status()
        
    return response.json()

Expected PUT Response:

{
  "id": "camp-uuid",
  "name": "Q3 Outbound Campaign",
  "predictive_dial_ratio": 1.35,
  "state": "running",
  "updated_time": "2024-05-15T10:35:00.000Z"
}

Complete Working Example

The following script combines authentication, polling, KDE calculation, and ratio adjustment into a production-ready loop. Replace the placeholder credentials and campaign ID before execution.

import time
import logging
import numpy as np
from scipy.stats import gaussian_kde
import httpx

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

class GenesysAuth:
    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 = None
        self.token_expiry = 0.0
        self.http_client = httpx.Client(timeout=15.0)

    def _fetch_token(self):
        url = f"{self.base_url}/login/oauth2/token"
        body = {"grant_type": "client_credentials", "client_id": self.client_id, "client_secret": self.client_secret}
        response = self.http_client.post(url, data=body)
        response.raise_for_status()
        data = response.json()
        return data["access_token"], data["expires_in"]

    def get_valid_token(self):
        if time.time() >= self.token_expiry - 30.0:
            logger.info("Refreshing OAuth token.")
            self.token, expires_in = self._fetch_token()
            self.token_expiry = time.time() + expires_in
        return self.token

    def close(self):
        self.http_client.close()

def fetch_recent_calls(auth, campaign_id, limit=100):
    url = f"{auth.base_url}/api/v2/outbound/campaigns/{campaign_id}/calls"
    params = {"limit": limit, "sort_by": "created_time:desc"}
    headers = {
        "Authorization": f"Bearer {auth.get_valid_token()}",
        "Accept": "application/json",
        "Content-Type": "application/json"
    }
    response = httpx.get(url, headers=headers, params=params)
    if response.status_code == 429:
        retry_after = int(response.headers.get("Retry-After", 5))
        logger.warning("Rate limited (429). Waiting %s seconds.", retry_after)
        time.sleep(retry_after)
        return fetch_recent_calls(auth, campaign_id, limit)
    response.raise_for_status()
    return response.json().get("calls", [])

def calculate_wrapup_kde_statistics(wrap_up_times):
    if not wrap_up_times:
        return {"p75": 30.0, "kde": None}
    data = np.array(wrap_up_times)
    kde = gaussian_kde(data)
    x_range = np.linspace(min(data), max(data), 500)
    pdf = kde.evaluate(x_range)
    cdf = np.cumsum(pdf) * (x_range[1] - x_range[0])
    cdf = cdf / cdf[-1]
    p75_idx = np.searchsorted(cdf, 0.75)
    p75_time = float(x_range[p75_idx])
    return {"p75": p75_time, "kde": kde}

def calculate_adjusted_ratio(current_answer_rate, target_answer_rate, current_ratio, p75_wrapup):
    adjustment_step = 0.15
    if current_answer_rate < target_answer_rate - 0.05:
        new_ratio = current_ratio - adjustment_step
    elif current_answer_rate > target_answer_rate + 0.05:
        max_safe_ratio = max(0.5, 5.0 - (p75_wrapup / 20.0))
        new_ratio = min(current_ratio + adjustment_step, max_safe_ratio)
    else:
        new_ratio = current_ratio
    return float(np.clip(new_ratio, 0.1, 5.0))

def update_campaign_ratio(auth, campaign_id, new_ratio):
    url = f"{auth.base_url}/api/v2/outbound/campaigns/{campaign_id}"
    headers = {
        "Authorization": f"Bearer {auth.get_valid_token()}",
        "Accept": "application/json",
        "Content-Type": "application/json"
    }
    body = {"predictive_dial_ratio": new_ratio}
    response = httpx.put(url, headers=headers, json=body)
    if response.status_code == 409:
        logger.error("Conflict (409). Campaign locked or invalid state.")
    elif response.status_code == 400:
        logger.error("Bad Request (400). Response: %s", response.text)
    else:
        response.raise_for_status()
    return response.json()

def main():
    # Configuration
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    BASE_URL = "https://api.mypurecloud.com"
    CAMPAIGN_ID = "your_campaign_uuid"
    TARGET_ANSWER_RATE = 0.85
    POLL_INTERVAL_SECONDS = 30
    WINDOW_SIZE = 100

    auth = GenesysAuth(CLIENT_ID, CLIENT_SECRET, BASE_URL)
    current_ratio = 1.0

    logger.info("Starting predictive dial ratio optimization loop.")
    
    try:
        while True:
            calls = fetch_recent_calls(auth, CAMPAIGN_ID, limit=WINDOW_SIZE)
            
            # Filter completed calls with positive wrap-up time
            completed_calls = [
                c for c in calls 
                if c.get("outcome") == "completed" and c.get("wrap_up_time", 0) > 0
            ]
            
            if not completed_calls:
                logger.info("No completed calls in window. Skipping calculation.")
                time.sleep(POLL_INTERVAL_SECONDS)
                continue

            # Calculate answer rate from window
            total_calls = len(calls)
            connected_calls = len([c for c in calls if c.get("disposition") == "connected"])
            current_answer_rate = connected_calls / total_calls if total_calls > 0 else 0.0
            
            # KDE on wrap-up times
            wrap_ups = [c["wrap_up_time"] for c in completed_calls]
            kde_stats = calculate_wrapup_kde_statistics(wrap_ups)
            p75_wrapup = kde_stats["p75"]
            
            logger.info(
                "Window stats: answer_rate=%.3f, target=%.3f, p75_wrapup=%.1fs, current_ratio=%.2f",
                current_answer_rate, TARGET_ANSWER_RATE, p75_wrapup, current_ratio
            )
            
            new_ratio = calculate_adjusted_ratio(
                current_answer_rate, TARGET_ANSWER_RATE, current_ratio, p75_wrapup
            )
            
            if abs(new_ratio - current_ratio) > 0.001:
                logger.info("Adjusting predictive dial ratio from %.2f to %.2f", current_ratio, new_ratio)
                update_campaign_ratio(auth, CAMPAIGN_ID, new_ratio)
                current_ratio = new_ratio
            else:
                logger.info("Ratio stable. No adjustment needed.")
                
            time.sleep(POLL_INTERVAL_SECONDS)
            
    except KeyboardInterrupt:
        logger.info("Interrupted by user. Shutting down.")
    except httpx.HTTPError as e:
        logger.error("HTTP Error: %s", e)
    finally:
        auth.close()

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token has expired or the client credentials are invalid.
• Fix: Verify the client ID and secret match the Genesys Cloud integration. Ensure the get_valid_token() method refreshes the token before each request. The provided implementation checks token_expiry - 30.0 to refresh proactively.
• Code Fix: The GenesysAuth class already handles automatic refresh. If you encounter repeated 401s, add a retry decorator that calls auth.get_valid_token() explicitly before the failed request.

Error: 403 Forbidden

• Cause: The OAuth client lacks the required scopes.
• Fix: Navigate to the Genesys Cloud admin console, locate the integration, and add outbound:campaign:read and outbound:campaign:write to the scope list. Save and regenerate credentials if necessary.
• Debugging: Print the token payload using a JWT decoder to verify the scp claim contains the outbound scopes.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud API rate limits (typically 100 requests per second per tenant, with burst limits).
• Fix: Implement exponential backoff. The provided fetch_recent_calls function checks for 429, reads the Retry-After header, and sleeps accordingly. For high-frequency polling, reduce POLL_INTERVAL_SECONDS to 60 or implement a sliding window cache.
• Code Fix: The 429 handler in Step 1 already implements recursive retry with Retry-After compliance.

Error: 400 Bad Request on PUT

• Cause: The predictive_dial_ratio value falls outside the platform-enforced bounds (0.1 to 5.0) or the campaign is not in a running state.
• Fix: The calculate_adjusted_ratio function uses np.clip(new_ratio, 0.1, 5.0) to enforce bounds. Verify the campaign state via GET /api/v2/outbound/campaigns/{campaignId}. Predictive ratio updates only apply to campaigns with predictive_dial_enabled: true.

Official References

• Genesys Cloud Outbound Campaign API Reference
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• Python SDK: PureCloudPlatformClientV2
• SciPy Gaussian KDE Documentation