Implementing Intraday Reforecast Engines that Adjust Predictions Based on Live Volume Trends
What This Guide Covers
This guide details the architectural implementation of an intraday reforecasting engine within Genesys Cloud CX that dynamically adjusts staffing predictions based on real-time volume trends. The end result is a system that monitors live call arrival rates, compares them against historical baselines, and updates the forecasted volume for the remainder of the day in real time, enabling immediate adjustment to adherence targets and staffing recommendations.
Prerequisites, Roles & Licensing
- Licensing: Genesys Cloud CX 3 or CX 3 with Workforce Management (WFM) add-on. Intraday features require the WFM module.
- Permissions:
Workforce Management > Forecast > ReadWorkforce Management > Schedule > ReadWorkforce Management > Intraday > ReadWorkforce Management > Intraday > Write(for programmatic adjustments)API > OAuth > ReadandWrite(for custom integration endpoints)
- OAuth Scopes:
wfm:intraday:readwfm:intraday:writewfm:forecast:read
- External Dependencies:
- A data processing engine (e.g., AWS Lambda, Azure Functions, or a Node.js microservice) capable of executing HTTP requests at configurable intervals.
- Access to the Genesys Cloud REST API gateway.
The Implementation Deep-Dive
1. Establishing the Baseline and Historical Context
Before implementing dynamic adjustments, the system must understand what “normal” looks like. The reforecast engine relies on the existing daily forecast generated by Genesys Cloud’s statistical modeling. This forecast serves as the baseline against which live deviations are measured.
The first step is to retrieve the current day’s forecasted volume by time interval. Genesys Cloud provides the GET /api/v2/wfm/forecast endpoint. You must request the forecast for the specific group (e.g., a skill or queue) and the current date.
The Trap: Retrieving the forecast without specifying the correct granularity parameter. If you request hourly granularity but your intraday engine operates on 15-minute intervals, you will lose the ability to detect short-term spikes or drops. Always align the API request granularity with your monitoring interval.
GET /api/v2/wfm/forecast?groupId=<group_id>&date=<current_date>&granularity=15min
Authorization: Bearer <access_token>
The response returns a JSON object containing intervals, each with predictedVolume. Store this baseline in memory or a temporary database table. This baseline represents the expected volume under standard conditions.
2. Capturing Live Volume Trends
The core of the reforecast engine is the comparison between predicted volume and actual volume. Genesys Cloud provides real-time interaction data via the GET /api/v2/interaction/config/realtime or, more commonly for historical intraday analysis, the GET /api/v2/wfm/intraday endpoints.
For a robust reforecast engine, you should use the GET /api/v2/wfm/intraday endpoint to retrieve the actual volume that has occurred up to the current minute. This endpoint aggregates interactions that have already completed or are in progress, providing a clean count of handled interactions.
The Trap: Using real-time queue metrics (/api/v2/interaction/config/realtime) to calculate actual volume for reforecasting. Real-time metrics reflect calls currently in the queue or being handled, but they do not provide a historical count of completed calls for the day in a format suitable for statistical comparison. The wfm/intraday endpoint is designed specifically for this purpose, providing a time-series of actuals that can be directly compared to the forecast.
GET /api/v2/wfm/intraday?groupId=<group_id>&date=<current_date>&granularity=15min
Authorization: Bearer <access_token>
The response includes actualVolume for each interval. Your engine must iterate through the intervals from the start of the business day up to the current time, summing the actualVolume to determine the total actuals to date.
3. Calculating the Deviation Factor
With the baseline forecast and the actual volume in hand, the engine must calculate a deviation factor. This factor represents the ratio between actual and predicted performance.
The formula is:
$$ \text{Deviation Factor} = \frac{\text{Total Actual Volume to Date}}{\text{Total Predicted Volume to Date}} $$
The Trap: Applying the deviation factor to the entire day’s forecast. If the deviation is calculated only for the morning hours, applying it to the afternoon hours assumes that the trend will persist linearly, which is often incorrect. Volume patterns are not always linear; they may spike at lunch or drop in the late afternoon. A sophisticated engine applies the deviation factor only to the remaining intervals of the day, or uses a weighted average that decays the influence of the deviation factor as time progresses.
For example, if the actual volume is 20% higher than predicted in the morning, the engine should increase the forecast for the remaining hours by 20%. However, if the deviation is extreme (e.g., >50%), it may indicate an anomaly (such as a marketing campaign launch or a system outage) rather than a sustainable trend. Implement a threshold check: only apply the deviation factor if it falls within a reasonable range (e.g., 0.8 to 1.2).
4. Updating the Intraday Forecast
Once the deviation factor is calculated, the engine must update the forecast for the remaining intervals. Genesys Cloud allows programmatic updates to the intraday forecast via the PUT /api/v2/wfm/intraday endpoint.
The payload must include the groupId, date, and an array of intervals with the new predictedVolume values. The new predicted volume is calculated as:
$$ \text{New Predicted Volume} = \text{Original Predicted Volume} \times \text{Deviation Factor} $$
The Trap: Overwriting the entire day’s forecast. The PUT endpoint replaces the forecast data for the specified intervals. If you attempt to update intervals that have already passed, the API may reject the request or cause inconsistencies in reporting. Only update intervals that are in the future or currently in progress.
PUT /api/v2/wfm/intraday
Content-Type: application/json
Authorization: Bearer <access_token>
{
"groupId": "<group_id>",
"date": "<current_date>",
"intervals": [
{
"startTime": "2023-10-27T13:00:00.000Z",
"endTime": "2023-10-27T13:15:00.000Z",
"predictedVolume": 12.5
},
{
"startTime": "2023-10-27T13:15:00.000Z",
"endTime": "2023-10-27T13:30:00.000Z",
"predictedVolume": 11.8
}
]
}
5. Triggering Staffing Adjustments
The updated forecast is useless if it does not trigger a response. The intraday module in Genesys Cloud uses the forecast to calculate adherence targets. When the forecast is updated, the adherence targets for agents in the group should automatically adjust to reflect the new volume expectations.
However, the engine can also push alerts to supervisors or trigger automated actions. For example, if the deviation factor indicates a significant drop in volume, the engine can trigger a “wrap-up” campaign, encouraging agents to complete their current tasks and log off early if staffing is over-provisioned. Conversely, if volume spikes, the engine can trigger a “recall” campaign, inviting available agents to log on.
The Trap: Assuming that adherence targets update instantly. There is a propagation delay in Genesys Cloud between the API update and the UI reflection. Additionally, agents may not see the updated targets immediately if their client is not refreshed. Implement a notification system (e.g., email, SMS, or in-app message) to inform supervisors and agents of the change.
Validation, Edge Cases & Troubleshooting
Edge Case 1: Zero Volume Intervals
If the actual volume for a period is zero, the deviation factor becomes undefined (division by zero). This can occur if the system goes down or if there is a complete lack of inbound traffic.
Root Cause: The engine attempts to calculate the deviation factor without checking for zero denominators.
Solution: Implement a guard clause. If the total predicted volume to date is zero, do not calculate a deviation factor. Instead, flag the interval as an anomaly and log it for manual review. Alternatively, use a small epsilon value (e.g., 0.001) to prevent division by zero, but this is less accurate than manual review.
Edge Case 2: Extreme Deviations
If the actual volume is significantly higher or lower than predicted (e.g., >50% deviation), applying the deviation factor linearly may result in unrealistic forecasts.
Root Cause: The model assumes that the deviation is due to a consistent shift in volume, rather than a one-time event.
Solution: Implement a saturation limit. Cap the deviation factor at a maximum and minimum value (e.g., 1.5 and 0.5). If the calculated deviation exceeds these limits, cap it at the limit and trigger an alert for manual intervention. This prevents the engine from overreacting to outliers.
Edge Case 3: Time Zone Mismatches
Genesys Cloud APIs return timestamps in UTC. If your business operates in a different time zone, you must convert the timestamps correctly when calculating the “current time” and “start of day.”
Root Cause: The engine uses local time for calculations but compares it with UTC timestamps from the API, leading to off-by-one-hour errors.
Solution: Always convert local business hours to UTC before making API calls. Use a library like moment-timezone (Node.js) or pytz (Python) to handle time zone conversions accurately. Ensure that the date parameter in the API request is in UTC.