Generating Custom Genesys Cloud Analytics Reports with Python

What You Will Build

A Python scheduler that retrieves historical handle time metrics from Genesys Cloud, smooths daily variance using pandas rolling windows, caches payloads in Memcached, generates formatted Excel workbooks, and distributes compressed reports via SMTP.

This tutorial uses the Genesys Cloud Analytics Reports API (/api/v2/analytics/report/execute) and the official genesyscloud Python SDK.

The implementation covers Python 3.10+ with pandas, pymemcache, openpyxl, and schedule.

Prerequisites

• OAuth2 Client Credentials grant type configured in Genesys Cloud
• Required OAuth scope: analytics:report:execute
• genesyscloud>=3.0.0, pandas>=2.0.0, pymemcache>=4.0.0, openpyxl>=3.1.0, schedule>=1.2.0, requests>=2.31.0
• Running Memcached instance accessible on localhost:11211 (default)
• SMTP server credentials with TLS support
• Python 3.10 or newer

Authentication Setup

Genesys Cloud uses OAuth2 Client Credentials flow for server-to-server integrations. The SDK handles token management, but production systems require explicit control over token caching and refresh logic to avoid unnecessary network calls and respect rate limits.

The following class fetches tokens using requests, caches them in Memcached with a configurable time-to-live, and validates expiry before each API call.

import requests
import time
import json
from typing import Optional
import pymemcache.client.base as memcache_client

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, memcache_host: str = "localhost", memcache_port: int = 11211):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = "https://api.mypurecloud.com/oauth/token"
        self.mc = memcache_client.Client((memcache_host, memcache_port))
        self.token_cache_key = "genesys_auth_token"
        self.current_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_access_token(self) -> str:
        if self.current_token and time.time() < self.token_expiry - 60:
            return self.current_token

        try:
            cached = self.mc.get(self.token_cache_key)
            if cached:
                token_data = json.loads(cached)
                if time.time() < token_data["expires_at"] - 60:
                    self.current_token = token_data["access_token"]
                    self.token_expiry = token_data["expires_at"]
                    return self.current_token
        except Exception as e:
            print(f"Memcached cache miss or error: {e}")

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "analytics:report:execute"
        }

        response = requests.post(self.token_url, data=payload)
        response.raise_for_status()
        token_json = response.json()

        self.current_token = token_json["access_token"]
        self.token_expiry = time.time() + token_json["expires_in"]

        cache_payload = json.dumps({
            "access_token": self.current_token,
            "expires_at": self.token_expiry
        })
        self.mc.set(self.token_cache_key, cache_payload, time=int(token_json["expires_in"]))

        return self.current_token

The scope analytics:report:execute grants permission to run ad-hoc report queries. The cache stores the token for the duration of the expires_in window returned by the OAuth server. A sixty-second buffer prevents edge-case expiry during active requests.

Implementation

Step 1: Query Reports API with Pagination and 429 Retry

The Analytics Reports API returns paginated results. Each response includes a nextPageToken when additional data exists. Genesys Cloud enforces strict rate limits per tenant and per client. A 429 response requires exponential backoff to avoid cascading failures.

The following function constructs a report specification for daily handle time metrics, executes it through the SDK, and handles pagination and rate limiting.

import time
from genesyscloud.rest.client import PureCloudPlatformClientV2
from genesyscloud.analytics.api import AnalyticsApi
from genesyscloud.analytics.models import ExecuteReportRequest, ReportSpecification

def fetch_handle_time_report(auth: GenesysAuth, start_date: str, end_date: str) -> list[dict]:
    client = PureCloudPlatformClientV2()
    client.set_access_token(auth.get_access_token())
    analytics_api = AnalyticsApi(client)

    spec = ReportSpecification(
        type="group",
        metrics=["handleTime"],
        dimension="group",
        interval="daily",
        date_from=start_date,
        date_to=end_date
    )
    body = ExecuteReportRequest(report_specification=spec)

    all_results = []
    page_token = None
    max_retries = 5

    while True:
        retries = 0
        while retries < max_retries:
            try:
                response = analytics_api.execute_report(body=body, next_page_token=page_token)
                if hasattr(response, 'results') and response.results:
                    all_results.extend(response.results)
                page_token = getattr(response, 'next_page_token', None)
                if not page_token:
                    break
                break
            except Exception as e:
                status_code = getattr(e, 'status', 0)
                if status_code == 429:
                    wait_time = 2 ** retries
                    print(f"Rate limited (429). Retrying in {wait_time}s...")
                    time.sleep(wait_time)
                    retries += 1
                else:
                    raise
        if retries >= max_retries:
            raise RuntimeError("Max retries exceeded for 429 responses.")
        if not page_token:
            break

    return all_results

The ReportSpecification targets the group report type with handleTime as the metric. The interval parameter set to daily aggregates metrics per calendar day. The pagination loop continues until next_page_token returns None. The retry block catches HTTP 429 errors and applies exponential backoff. Other exceptions propagate immediately for transparent failure handling.

Step 2: Cache Results and Apply Pandas Rolling Window Aggregations

Raw handle time data contains daily spikes caused by staffing changes, campaign launches, or system incidents. A rolling window aggregation smooths these fluctuations while preserving the underlying trend. The following function checks Memcached for cached query results, processes the data with pandas, and stores the transformed dataframe.

import pandas as pd
import json
import io

def process_and_cache_report(auth: GenesysAuth, start_date: str, end_date: str, window_size: int = 7) -> pd.DataFrame:
    cache_key = f"handle_time_report_{start_date}_{end_date}"
    raw_data = None

    try:
        cached_bytes = auth.mc.get(cache_key)
        if cached_bytes:
            raw_data = json.loads(cached_bytes)
    except Exception as e:
        print(f"Cache miss or read error: {e}")

    if not raw_data:
        print("Fetching report from Genesys Cloud...")
        raw_data = fetch_handle_time_report(auth, start_date, end_date)
        try:
            auth.mc.set(cache_key, json.dumps(raw_data), time=86400)
        except Exception as e:
            print(f"Cache write failed: {e}")

    df = pd.DataFrame(raw_data)
    if df.empty:
        raise ValueError("No data returned from the report query.")

    df["date"] = pd.to_datetime(df["date"])
    df["handle_time_seconds"] = df["handleTime"].astype(float)
    df = df.sort_values("date")
    df.set_index("date", inplace=True)

    df["handle_time_smoothed"] = df["handle_time_seconds"].rolling(window=window_size, min_periods=1).mean()
    df["handle_time_minutes"] = (df["handle_time_smoothed"] / 60).round(2)

    return df

The cache key includes the date range to prevent stale data collisions. The TTL is set to 86400 seconds (24 hours), which aligns with daily report refresh cycles. The pandas rolling(window=7, min_periods=1) calculation computes a seven-day moving average. The min_periods=1 parameter ensures the first six rows return a valid average instead of NaN. The smoothed metric is divided by 60 to convert seconds to minutes for human-readable reporting.

Step 3: Export to Excel with Conditional Formatting

Excel exports require structured styling for executive consumption. The following function writes the dataframe to an openpyxl workbook, applies data bars for trend visualization, and adds threshold-based color rules.

from openpyxl import Workbook
from openpyxl.styles import Font, Alignment, PatternFill
from openpyxl.formatting.rule import CellIsRule
from io import BytesIO

def export_to_excel(df: pd.DataFrame) -> BytesIO:
    wb = Workbook()
    ws = wb.active
    ws.title = "Handle Time Report"

    header_font = Font(bold=True, size=12)
    header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
    header_font_white = Font(bold=True, size=12, color="FFFFFF")

    headers = ["Date", "Raw Handle Time (s)", "Smoothed Handle Time (s)", "Smoothed (min)"]
    ws.append(headers)
    for cell in ws[1]:
        cell.font = header_font_white
        cell.fill = header_fill
        cell.alignment = Alignment(horizontal="center")

    for _, row in df.iterrows():
        ws.append([
            row.name.strftime("%Y-%m-%d"),
            row["handle_time_seconds"],
            row["handle_time_smoothed"],
            row["handle_time_minutes"]
        ])

    ws.column_dimensions['A'].width = 15
    ws.column_dimensions['B'].width = 20
    ws.column_dimensions['C'].width = 25
    ws.column_dimensions['D'].width = 18

    green_fill = PatternFill(start_color="C6EFCE", end_color="C6EFCE", fill_type="solid")
    red_fill = PatternFill(start_color="FFC7CE", end_color="FFC7CE", fill_type="solid")

    ws.conditional_formatting.add(
        "D2:D1000",
        CellIsRule(operator="lessThanOrEqual", formula=["180"], fill=green_fill)
    )
    ws.conditional_formatting.add(
        "D2:D1000",
        CellIsRule(operator="greaterThanOrEqual", formula=["300"], fill=red_fill)
    )

    output = BytesIO()
    wb.save(output)
    output.seek(0)
    return output

The conditional formatting rules target column D (smoothed minutes). Values less than or equal to 180 minutes receive a green fill. Values greater than or equal to 300 minutes receive a red fill. The BytesIO buffer keeps the file in memory, eliminating disk I/O overhead before compression.

Step 4: Compress Attachment and Distribute via SMTP

Email systems impose attachment size limits. Compressing the Excel workbook reduces payload size and prevents delivery failures. The following function zips the buffer, constructs a MIME message, and transmits it through an SMTP server.

import smtplib
import zipfile
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email import encoders

def send_report_email(df: pd.DataFrame, smtp_server: str, smtp_port: int, sender: str, password: str, recipients: list[str]):
    excel_buffer = export_to_excel(df)
    zip_buffer = BytesIO()

    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zf:
        zf.writestr("handle_time_report.xlsx", excel_buffer.read())

    zip_buffer.seek(0)
    msg = MIMEMultipart()
    msg['From'] = sender
    msg['To'] = ", ".join(recipients)
    msg['Subject'] = "Genesys Cloud Handle Time Report"

    msg.attach(MIMEBase('application', 'zip', fp=zip_buffer))
    encoders.encode_base64(msg[-1])

    try:
        with smtplib.SMTP(smtp_server, smtp_port) as server:
            server.starttls()
            server.login(sender, password)
            server.sendmail(sender, recipients, msg.as_string())
        print("Report email dispatched successfully.")
    except smtplib.SMTPException as e:
        print(f"SMTP transmission failed: {e}")
        raise

The zipfile module compresses the Excel buffer using ZIP_DEFLATED. The MIMEBase attachment wraps the zip stream. The smtplib context manager ensures the connection closes properly. TLS encryption is enforced via starttls().

Complete Working Example

The following script integrates all components into a production-ready scheduler. It runs daily at 08:00 server time and logs execution status.

import schedule
import time
import sys
from datetime import datetime, timedelta

def main():
    auth = GenesysAuth(
        client_id="YOUR_CLIENT_ID",
        client_secret="YOUR_CLIENT_SECRET"
    )

    end_date = datetime.utcnow().strftime("%Y-%m-%dT23:59:59.999Z")
    start_date = (datetime.utcnow() - timedelta(days=30)).strftime("%Y-%m-%dT00:00:00.000Z")

    def run_daily_report():
        try:
            print(f"Starting report generation at {datetime.utcnow().isoformat()}")
            df = process_and_cache_report(auth, start_date, end_date, window_size=7)
            send_report_email(
                df=df,
                smtp_server="smtp.yourdomain.com",
                smtp_port=587,
                sender="reports@yourdomain.com",
                password="YOUR_SMTP_PASSWORD",
                recipients=["manager@yourdomain.com", "ops@yourdomain.com"]
            )
            print("Daily report cycle completed.")
        except Exception as e:
            print(f"Report cycle failed: {e}")
            sys.exit(1)

    schedule.every().day.at("08:00").do(run_daily_report)
    print("Scheduler initialized. Waiting for next execution...")

    while True:
        schedule.run_pending()
        time.sleep(60)

if __name__ == "__main__":
    main()

The schedule library blocks the main thread and evaluates pending jobs every sixty seconds. The date range queries the previous thirty days. Replace placeholder credentials with environment variables or a secrets manager in production.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Invalid client credentials or expired token. The OAuth server rejects the client_id or client_secret.
• Fix: Verify credentials in the Genesys Cloud admin console under Platform Apps. Ensure the token cache does not store stale tokens.
• Code fix: The GenesysAuth.get_access_token() method automatically re-fetches when time.time() > self.token_expiry - 60.

Error: 403 Forbidden

• Cause: Missing analytics:report:execute scope or insufficient user permissions.
• Fix: Add the exact scope to the OAuth client configuration. Assign the service account the Analytics Administrator or Reporting Analyst role.
• Code fix: Update the scope field in the GenesysAuth.__init__ payload if multiple scopes are required.

Error: 429 Too Many Requests

• Cause: Exceeding tenant-level or endpoint-level rate limits. Genesys Cloud returns Retry-After headers.
• Fix: Implement exponential backoff. The fetch_handle_time_report function retries up to five times with 2 ** retries second delays.
• Code fix: Increase max_retries or parse the Retry-After header if the SDK exposes it.

Error: Memcached Connection Refused

• Cause: Memcached service is down or bound to 127.0.0.1 while the script runs in a container.
• Fix: Verify netstat -tuln | grep 11211. Update memcache_host to the correct IP or DNS name.
• Code fix: Wrap memcache_client.Client initialization in a try-except block and fall back to in-memory caching if unavailable.

Error: Pandas Rolling Window Returns NaN

• Cause: Insufficient data points or unsorted index. Rolling calculations require a monotonic datetime index.
• Fix: Ensure df.sort_values("date") executes before setting the index. Use min_periods=1 to allow partial windows.
• Code fix: The process_and_cache_report function enforces sorting and min_periods=1.

Official References

• Genesys Cloud Analytics Reports API Documentation
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Grant
• Pandas Rolling Window Documentation
• Openpyxl Conditional Formatting Guide