Aggregating Genesys Cloud Summary Metrics with Python SDK

How-To-Articles
1

Aggregating Genesys Cloud Summary Metrics with Python SDK

What You Will Build

You will build a Python pipeline that queries the Genesys Cloud Analytics API for daily and weekly conversation summaries, flattens deeply nested metric objects into tabular records, calculates custom key performance indicators, enforces API throttle limits with a token bucket algorithm, backfills temporal gaps using linear interpolation, and writes the final dataset to a Parquet file for data warehouse ingestion.

Prerequisites

  • OAuth 2.0 Service Account or Client Credentials grant
  • Required scope: analytics:query:read
  • Python 3.9 or higher
  • pip install genesyscloud httpx pandas pyarrow numpy
  • A valid Genesys Cloud organization with queue data

Authentication Setup

The Genesys Cloud Python SDK handles token acquisition and automatic refresh when you initialize the client with client credentials. You must pass the API base URL, client ID, and client secret. The SDK caches the access token and refreshes it silently before expiration.

from genesyscloud import PureCloudPlatformClientV2

# Replace with your environment values
GENESYS_BASE_URL = "https://api.mypurecloud.com"
CLIENT_ID = "your_client_id"
CLIENT_SECRET = "your_client_secret"

# Initialize the platform client with client credentials
client = PureCloudPlatformClientV2(
    base_url=GENESYS_BASE_URL,
    client_id=CLIENT_ID,
    client_secret=CLIENT_SECRET
)

# Verify authentication by fetching a minimal resource
try:
    user_api = client.user_api
    me = user_api.get_user_me()
    print(f"Authenticated as: {me.email}")
except Exception as e:
    print(f"Authentication failed: {e}")
    raise SystemExit(1)

The analytics:query:read scope grants permission to execute summary queries. If your service account lacks this scope, the Analytics API returns a 403 Forbidden response.

Implementation

Step 1: Initialize Client and Configure Token Bucket Rate Limiter

Genesys Cloud enforces strict rate limits on the Analytics API. Exceeding the limit returns HTTP 429 with a Retry-After header. A token bucket algorithm provides deterministic pacing without relying on polling. The implementation below controls request frequency at the application layer before invoking the SDK.

import time
from typing import Callable, Any

class TokenBucketRateLimiter:
    """Synchronous token bucket that blocks until a token is available."""
    def __init__(self, rate: float, capacity: float):
        self.rate = rate          # Tokens added per second
        self.capacity = capacity  # Maximum tokens
        self.tokens = capacity
        self.last_refill = time.time()

    def _refill(self) -> None:
        now = time.time()
        elapsed = now - self.last_refill
        self.tokens = min(self.capacity, self.tokens + (elapsed * self.rate))
        self.last_refill = now

    def acquire(self, tokens: int = 1) -> None:
        while True:
            self._refill()
            if self.tokens >= tokens:
                self.tokens -= tokens
                return
            # Calculate sleep duration to wait until enough tokens are available
            deficit = tokens - self.tokens
            sleep_time = deficit / self.rate
            time.sleep(sleep_time)

def rate_limited_call(limiter: TokenBucketRateLimiter, func: Callable[..., Any], *args, **kwargs) -> Any:
    """Wrapper that acquires a token before executing the API call."""
    limiter.acquire()
    return func(*args, **kwargs)

Configure the limiter with a conservative rate. The Analytics summary endpoint typically allows ten requests per second per tenant, but downstream aggregation pipelines benefit from five requests per second to avoid cascade throttling.

# 5 tokens per second, burst capacity of 10
rate_limiter = TokenBucketRateLimiter(rate=5.0, capacity=10.0)

Step 2: Query Analytics API for Daily and Weekly Summaries

The Analytics summary endpoint accepts a JSON payload defining the time window, grouping dimensions, and requested metrics. You must handle pagination manually using the continuationToken field in the response. The code below queries daily intervals for a seven-day window, then weekly intervals for a four-week window.

import httpx
from datetime import datetime, timedelta

def build_summary_query(interval: str, start_date: str, end_date: str, queue_id: str) -> dict:
    """Constructs the payload for POST /api/v2/analytics/conversations/summary/query"""
    return {
        "interval": interval,
        "dateFrom": f"{start_date}T00:00:00Z",
        "dateTo": f"{end_date}T23:59:59Z",
        "groupBy": ["queue"],
        "metrics": ["conversations", "abandoned", "talk", "hold", "work"],
        "select": ["id", "name"],
        "filters": [
            {
                "type": "queue",
                "path": "queue",
                "op": "in",
                "values": [queue_id]
            }
        ]
    }

def fetch_analytics_summaries(client, queue_id: str, interval: str, start: str, end: str) -> list[dict]:
    """Paginates through the Analytics summary endpoint."""
    api = client.analytics_api
    all_results = []
    continuation_token = None

    while True:
        payload = build_summary_query(interval, start, end, queue_id)
        
        # Execute with rate limiting
        response = rate_limited_call(
            rate_limiter,
            api.post_analytics_conversations_summary_query,
            body=payload,
            continuation_token=continuation_token
        )
        
        # Accumulate results
        if hasattr(response, 'results') and response.results:
            all_results.extend(response.results)
        
        # Check for pagination
        if hasattr(response, 'continuation_token') and response.continuation_token:
            continuation_token = response.continuation_token
        else:
            break

    return all_results

# Example execution parameters
QUEUE_ID = "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
DAILY_START = "2024-01-01"
DAILY_END = "2024-01-07"
WEEKLY_START = "2023-12-01"
WEEKLY_END = "2024-01-07"

daily_data = fetch_analytics_summaries(client, QUEUE_ID, "P1D", DAILY_START, DAILY_END)
weekly_data = fetch_analytics_summaries(client, QUEUE_ID, "P1W", WEEKLY_START, WEEKLY_END)

Expected Response Structure (simplified):

{
  "results": [
    {
      "groupBy": "queue",
      "groups": [
        {
          "group": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
          "name": "Sales Support",
          "intervals": [
            {
              "interval": "2024-01-01T00:00:00Z",
              "metrics": {
                "conversations": {"value": 1250, "unit": "count"},
                "abandoned": {"value": 45, "unit": "count"},
                "talk": {"value": 45000, "unit": "second"},
                "hold": {"value": 3200, "unit": "second"},
                "work": {"value": 12500, "unit": "second"}
              }
            }
          ]
        }
      ]
    }
  ],
  "continuationToken": null
}

Step 3: Flatten Nested Metric Structures and Calculate Derived KPIs

The Analytics API returns a three-level hierarchy: results → groups → intervals. Database ingestion requires a flat row-per-record format. The flattening function extracts the date, queue identifiers, and raw metric values. It then calculates Average Handle Time (AHT) and a custom Service Level ratio.

def flatten_and_calculate_kpis(raw_results: list) -> list[dict]:
    """
    Transforms nested API response into flat records and computes derived KPIs.
    AHT = (talk + hold + work) / conversations
    Service Level = (conversations - abandoned) / conversations
    """
    flat_records = []
    
    for result in raw_results:
        if not hasattr(result, 'groups') or not result.groups:
            continue
            
        for group in result.groups:
            queue_id = group.group if hasattr(group, 'group') else None
            queue_name = group.name if hasattr(group, 'name') else None
            
            if not hasattr(group, 'intervals') or not group.intervals:
                continue
                
            for interval in group.intervals:
                interval_date = interval.interval if hasattr(interval, 'interval') else None
                metrics = interval.metrics if hasattr(interval, 'metrics') else {}
                
                # Extract raw values safely
                conv = metrics.get('conversations', {}).get('value', 0) or 0
                abandoned = metrics.get('abandoned', {}).get('value', 0) or 0
                talk = metrics.get('talk', {}).get('value', 0) or 0
                hold = metrics.get('hold', {}).get('value', 0) or 0
                work = metrics.get('work', {}).get('value', 0) or 0
                
                # Calculate derived KPIs
                aht_seconds = (talk + hold + work) / conv if conv > 0 else 0.0
                service_level = (conv - abandoned) / conv if conv > 0 else 0.0
                
                flat_records.append({
                    "date": interval_date,
                    "queue_id": queue_id,
                    "queue_name": queue_name,
                    "conversations": conv,
                    "abandoned": abandoned,
                    "talk_seconds": talk,
                    "hold_seconds": hold,
                    "work_seconds": work,
                    "aht_seconds": round(aht_seconds, 2),
                    "service_level": round(service_level, 4)
                })
                
    return flat_records

daily_flat = flatten_and_calculate_kpis(daily_data)
weekly_flat = flatten_and_calculate_kpis(weekly_data)

The flattening logic guards against missing metric keys and division by zero. The derived KPIs align with standard contact center mathematics. You can adjust the service level formula to incorporate specific SLA thresholds if your organization tracks answered-within-20-seconds metrics.

Step 4: Backfill Missing Data Points via Interpolation

Contact center data often contains gaps due to low volume days, system outages, or reporting delays. Linear interpolation fills temporal gaps without distorting trend lines. The code below uses pandas to set a datetime index, resample to daily frequency, interpolate missing values, and forward-fill edge cases.

import pandas as pd
import numpy as np

def backfill_missing_data(records: list[dict], granularity: str = "D") -> pd.DataFrame:
    """
    Converts records to DataFrame, sets datetime index, resamples, interpolates, and fills edges.
    granularity: 'D' for daily, 'W' for weekly
    """
    df = pd.DataFrame(records)
    if df.empty:
        return df
        
    # Convert date strings to datetime
    df["date"] = pd.to_datetime(df["date"])
    df = df.set_index("date").sort_index()
    
    # Define numeric columns for interpolation
    numeric_cols = ["conversations", "abandoned", "talk_seconds", "hold_seconds", 
                    "work_seconds", "aht_seconds", "service_level"]
    
    # Resample to requested granularity
    resampled = df[numeric_cols].resample(granularity).sum()
    
    # Linear interpolation for interior gaps
    resampled = resampled.interpolate(method="linear")
    
    # Forward fill and backward fill for leading/trailing NaNs
    resampled = resampled.ffill().bfill()
    
    # Reattach non-numeric columns (queue metadata)
    metadata = df[["queue_id", "queue_name"]].iloc[0]
    resampled["queue_id"] = metadata["queue_id"]
    resampled["queue_name"] = metadata["queue_name"]
    
    # Reset index to make date a column again
    resampled = resampled.reset_index().rename(columns={"index": "date"})
    
    return resampled

daily_df = backfill_missing_data(daily_flat, granularity="D")
weekly_df = backfill_missing_data(weekly_flat, granularity="W")

The resample method aligns data to calendar boundaries. The interpolate method calculates intermediate values based on surrounding points. The ffill and bfill methods prevent NaN propagation at the start and end of the time series.

Step 5: Export Aggregated Datasets to Parquet

Parquet provides columnar storage with built-in compression and schema enforcement. It is the standard format for loading into Snowflake, BigQuery, Redshift, or Databricks. The export step writes the DataFrames to disk with explicit type casting.

def export_to_parquet(df: pd.DataFrame, filename: str) -> None:
    """Casts numeric types and writes DataFrame to Parquet."""
    # Ensure consistent types for data warehouse compatibility
    df["date"] = df["date"].dt.strftime("%Y-%m-%d")
    df["conversations"] = df["conversations"].astype("Int64")
    df["abandoned"] = df["abandoned"].astype("Int64")
    df["talk_seconds"] = df["talk_seconds"].astype("Int64")
    df["hold_seconds"] = df["hold_seconds"].astype("Int64")
    df["work_seconds"] = df["work_seconds"].astype("Int64")
    df["aht_seconds"] = df["aht_seconds"].astype("float64")
    df["service_level"] = df["service_level"].astype("float64")
    
    df.to_parquet(
        filename,
        engine="pyarrow",
        index=False,
        compression="snappy",
        schema=df.to_parquet.__globals__["pd"].ArrowDtype  # Fallback to default
    )
    print(f"Exported {len(df)} records to {filename}")

export_to_parquet(daily_df, "genesys_daily_metrics.parquet")
export_to_parquet(weekly_df, "genesys_weekly_metrics.parquet")

The snappy compression algorithm balances CPU usage and file size. The explicit type casting prevents schema drift during incremental warehouse loads.

Complete Working Example

The following script combines all components into a single executable module. Replace the credential placeholders and queue ID before running.

import time
import httpx
import pandas as pd
import numpy as np
from typing import Callable, Any
from datetime import datetime, timedelta
from genesyscloud import PureCloudPlatformClientV2

# ==============================================================================
# Configuration
# ==============================================================================
GENESYS_BASE_URL = "https://api.mypurecloud.com"
CLIENT_ID = "your_client_id"
CLIENT_SECRET = "your_client_secret"
QUEUE_ID = "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
DAILY_START = "2024-01-01"
DAILY_END = "2024-01-07"
WEEKLY_START = "2023-12-01"
WEEKLY_END = "2024-01-07"

# ==============================================================================
# Rate Limiter
# ==============================================================================
class TokenBucketRateLimiter:
    def __init__(self, rate: float, capacity: float):
        self.rate = rate
        self.capacity = capacity
        self.tokens = capacity
        self.last_refill = time.time()

    def _refill(self) -> None:
        now = time.time()
        elapsed = now - self.last_refill
        self.tokens = min(self.capacity, self.tokens + (elapsed * self.rate))
        self.last_refill = now

    def acquire(self, tokens: int = 1) -> None:
        while True:
            self._refill()
            if self.tokens >= tokens:
                self.tokens -= tokens
                return
            deficit = tokens - self.tokens
            sleep_time = deficit / self.rate
            time.sleep(sleep_time)

def rate_limited_call(limiter, func, *args, **kwargs):
    limiter.acquire()
    return func(*args, **kwargs)

# ==============================================================================
# Analytics Query Logic
# ==============================================================================
def build_summary_query(interval: str, start_date: str, end_date: str, queue_id: str) -> dict:
    return {
        "interval": interval,
        "dateFrom": f"{start_date}T00:00:00Z",
        "dateTo": f"{end_date}T23:59:59Z",
        "groupBy": ["queue"],
        "metrics": ["conversations", "abandoned", "talk", "hold", "work"],
        "select": ["id", "name"],
        "filters": [{"type": "queue", "path": "queue", "op": "in", "values": [queue_id]}]
    }

def fetch_analytics_summaries(client, queue_id: str, interval: str, start: str, end: str) -> list:
    api = client.analytics_api
    all_results = []
    continuation_token = None

    while True:
        payload = build_summary_query(interval, start, end, queue_id)
        response = rate_limited_call(
            rate_limiter,
            api.post_analytics_conversations_summary_query,
            body=payload,
            continuation_token=continuation_token
        )
        if hasattr(response, 'results') and response.results:
            all_results.extend(response.results)
        if hasattr(response, 'continuation_token') and response.continuation_token:
            continuation_token = response.continuation_token
        else:
            break
    return all_results

# ==============================================================================
# Data Transformation
# ==============================================================================
def flatten_and_calculate_kpis(raw_results: list) -> list[dict]:
    flat_records = []
    for result in raw_results:
        if not hasattr(result, 'groups') or not result.groups:
            continue
        for group in result.groups:
            queue_id = group.group if hasattr(group, 'group') else None
            queue_name = group.name if hasattr(group, 'name') else None
            if not hasattr(group, 'intervals') or not group.intervals:
                continue
            for interval in group.intervals:
                interval_date = interval.interval if hasattr(interval, 'interval') else None
                metrics = interval.metrics if hasattr(interval, 'metrics') else {}
                conv = metrics.get('conversations', {}).get('value', 0) or 0
                abandoned = metrics.get('abandoned', {}).get('value', 0) or 0
                talk = metrics.get('talk', {}).get('value', 0) or 0
                hold = metrics.get('hold', {}).get('value', 0) or 0
                work = metrics.get('work', {}).get('value', 0) or 0
                aht_seconds = (talk + hold + work) / conv if conv > 0 else 0.0
                service_level = (conv - abandoned) / conv if conv > 0 else 0.0
                flat_records.append({
                    "date": interval_date,
                    "queue_id": queue_id,
                    "queue_name": queue_name,
                    "conversations": conv,
                    "abandoned": abandoned,
                    "talk_seconds": talk,
                    "hold_seconds": hold,
                    "work_seconds": work,
                    "aht_seconds": round(aht_seconds, 2),
                    "service_level": round(service_level, 4)
                })
    return flat_records

def backfill_missing_data(records: list[dict], granularity: str = "D") -> pd.DataFrame:
    df = pd.DataFrame(records)
    if df.empty:
        return df
    df["date"] = pd.to_datetime(df["date"])
    df = df.set_index("date").sort_index()
    numeric_cols = ["conversations", "abandoned", "talk_seconds", "hold_seconds", 
                    "work_seconds", "aht_seconds", "service_level"]
    resampled = df[numeric_cols].resample(granularity).sum()
    resampled = resampled.interpolate(method="linear").ffill().bfill()
    metadata = df[["queue_id", "queue_name"]].iloc[0]
    resampled["queue_id"] = metadata["queue_id"]
    resampled["queue_name"] = metadata["queue_name"]
    return resampled.reset_index().rename(columns={"index": "date"})

def export_to_parquet(df: pd.DataFrame, filename: str) -> None:
    df["date"] = df["date"].dt.strftime("%Y-%m-%d")
    for col in ["conversations", "abandoned", "talk_seconds", "hold_seconds", "work_seconds"]:
        df[col] = df[col].astype("Int64")
    df["aht_seconds"] = df["aht_seconds"].astype("float64")
    df["service_level"] = df["service_level"].astype("float64")
    df.to_parquet(filename, engine="pyarrow", index=False, compression="snappy")
    print(f"Exported {len(df)} records to {filename}")

# ==============================================================================
# Execution
# ==============================================================================
if __name__ == "__main__":
    # 1. Authentication
    client = PureCloudPlatformClientV2(
        base_url=GENESYS_BASE_URL,
        client_id=CLIENT_ID,
        client_secret=CLIENT_SECRET
    )
    try:
        me = client.user_api.get_user_me()
        print(f"Authenticated as: {me.email}")
    except Exception as e:
        print(f"Authentication failed: {e}")
        raise SystemExit(1)

    # 2. Rate Limiter Setup
    rate_limiter = TokenBucketRateLimiter(rate=5.0, capacity=10.0)

    # 3. Fetch Data
    print("Fetching daily summaries...")
    daily_data = fetch_analytics_summaries(client, QUEUE_ID, "P1D", DAILY_START, DAILY_END)
    print("Fetching weekly summaries...")
    weekly_data = fetch_analytics_summaries(client, QUEUE_ID, "P1W", WEEKLY_START, WEEKLY_END)

    # 4. Transform & Backfill
    daily_flat = flatten_and_calculate_kpis(daily_data)
    weekly_flat = flatten_and_calculate_kpis(weekly_data)
    
    daily_df = backfill_missing_data(daily_flat, granularity="D")
    weekly_df = backfill_missing_data(weekly_flat, granularity="W")

    # 5. Export
    export_to_parquet(daily_df, "genesys_daily_metrics.parquet")
    export_to_parquet(weekly_df, "genesys_weekly_metrics.parquet")
    print("Pipeline complete.")

Common Errors & Debugging

Error: 401 Unauthorized

  • Cause: Invalid client ID, expired client secret, or mismatched base URL.
  • Fix: Verify credentials in the Genesys Cloud Admin console under Platform Apps. Ensure the base URL matches your environment region.
  • Code Fix: Add explicit credential validation before initialization.

Error: 403 Forbidden

  • Cause: The service account lacks the analytics:query:read OAuth scope.
  • Fix: Navigate to Admin > Platform Apps > Your App > OAuth. Add the analytics:query:read scope and save. The SDK automatically picks up the new scope on the next token refresh.

Error: 429 Too Many Requests

  • Cause: Exceeding the tenant-level Analytics API rate limit. The token bucket may be misconfigured or the pipeline is running concurrent threads.
  • Fix: Reduce the token bucket rate to 3.0 or lower. Ensure the pipeline runs single-threaded for this endpoint. The Retry-After header indicates seconds to wait, but the token bucket preempts the error.

Error: 400 Bad Request (Invalid Query)

  • Cause: Malformed groupBy, unsupported metrics, or date range exceeding the tenant retention policy.
  • Fix: Validate the JSON payload against the OpenAPI specification. Ensure dateFrom is not earlier than your organization’s data retention window. Use interval: "P1D" for daily and "P1W" for weekly.

Error: Interpolation Warning (No Numerical Data)

  • Cause: The DataFrame contains non-numeric types in the interpolation columns, or all values are NaN.
  • Fix: Explicitly cast metric columns to float64 before calling interpolate(). The complete example handles this via astype() before export.

Official References