Implementing Interaction Volume Forecasting using Prophet and Historical Analytics Exports

How-To-Articles
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Implementing Interaction Volume Forecasting using Prophet and Historical Analytics Exports

What This Guide Covers

You are implementing an advanced time-series forecasting model to predict interaction volumes (Voice, Chat, Email) using Facebook’s Prophet library and historical data exported from the Genesys Cloud Analytics API. Standard WFM forecasting often struggles with complex seasonality (holidays, monthly billing cycles, marketing events). By building a custom forecasting pipeline, you can ingest multi-year historical trends, account for specific business holidays, and generate a 30-day forecast with confidence intervals to drive more accurate staffing requirements.

Prerequisites, Roles & Licensing

  • Genesys Cloud: Any tier (to export data).
  • Tooling:
    • Python 3.9+.
    • prophet library (pip install prophet).
    • pandas for data manipulation.
  • Data Source: At least 12-24 months of daily interaction volume data (Conversation Detail Query exports).

The Implementation Deep-Dive

1. Data Extraction and Preparation

Prophet requires a two-column DataFrame with columns ds (datestamp) and y (the numeric value to forecast).

Python Data Prep:

import pandas as pd

# Load historical daily volumes (Exported from Genesys Analytics)
# Expects a CSV with 'date' and 'total_calls'
df = pd.read_csv('historical_volumes.csv')

# Rename for Prophet compliance
df = df.rename(columns={'date': 'ds', 'total_calls': 'y'})

# Ensure ds is datetime
df['ds'] = pd.to_datetime(df['ds'])

2. Building the Prophet Model

Configure the model to handle weekly and yearly seasonality, and add custom “Holiday” effects for your specific business (e.g., “Annual Sale Week”).

from prophet import Prophet

# Define custom holidays
holidays = pd.DataFrame({
  'holiday': 'annual_sale',
  'ds': pd.to_datetime(['2024-11-25', '2025-11-24']),
  'lower_window': 0,
  'upper_window': 5, # Impact lasts for 5 days after
})

# Initialize model with seasonality settings
m = Prophet(
    holidays=holidays,
    yearly_seasonality=True,
    weekly_seasonality=True,
    daily_seasonality=False, # We are forecasting at the daily level
    seasonality_mode='multiplicative' # Good for growing businesses
)

m.fit(df)

3. Generating the Forecast

Create a “future” dataframe for the next 30 days and run the prediction.

# Create 30-day future dataframe
future = m.make_future_dataframe(periods=30)

# Predict
forecast = m.predict(future)

# Inspect the forecast
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())

4. Accounting for Outliers

Spikes caused by system outages or abnormal events can skew the forecast. Prophet allows you to treat these as “missing data” or mask them.

# Masking a specific outage day (Prophet ignores NaNs during fitting)
df.loc[df['ds'] == '2025-02-14', 'y'] = None
m.fit(df)

5. Exporting to Genesys Cloud WFM

The generated forecast (CSV or JSON) can be uploaded to Genesys Cloud WFM to replace or supplement the automatic forecast.

  • WFM Integration: Use the POST /api/v2/workforcemanagement/businessunits/{muId}/weeks/{weekDate}/forecasts endpoint to push custom data.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Insufficient History

If you only have 3 months of data, Prophet cannot accurately calculate yearly seasonality.
Solution: Disable yearly_seasonality=False until you have 12+ months of data. Rely on weekly patterns and external “regressors” (like marketing spend) to provide context.

Edge Case 2: Marketing Campaigns

A sudden 20% increase in volume due to a TV ad will look like an anomaly to the model.
Solution: Use “Extra Regressors”. If you know the marketing spend per day, add it to the model: m.add_regressor('ad_spend').

Edge Case 3: Over-fitting

Setting too many “change points” can make the model chase noise rather than signal.
Solution: Keep changepoint_prior_scale at the default (0.05) unless you have a very stable environment and want the model to be more flexible.

Official References