Anyone free to help troubleshoot this score calculation mismatch in Genesys Cloud Quality Management. We are migrating our quality evaluation forms from Zendesk Sunshine Conversations, where scoring was based on simple point totals, to Genesys Cloud’s weighted rubric system. The goal is to maintain parity in agent performance metrics during the transition phase.
The issue arises when importing evaluation criteria via the REST API. The endpoint /api/v2/quality/evaluations/forms accepts the payload without error, but the resulting scores in the dashboard do not match our historical Zendesk data. Specifically, we are seeing a 15% variance in weighted averages for identical interaction samples.
Here is the reproduction sequence:
- Create a new Quality Evaluation Form in Genesys Cloud with five criteria, each assigned a weight of 20%.
- Import a CSV of historical Zendesk evaluations where each criterion was scored on a 1-5 scale (total max 25).
- Map the Zendesk 1-5 scores to Genesys Cloud’s 0-100 scale using a linear multiplier (x20).
- Submit the evaluations via the API endpoint
/api/v2/quality/evaluations.
- Compare the calculated total score in Genesys Cloud against the expected Zendesk equivalent.
The error is not a hard failure. There is no 400 Bad Request or 500 Internal Server Error. The API returns a 201 Created status. However, the score field in the response body shows a value that is consistently lower than the calculated expectation. For example, a perfect 5/5 on all Zendesk criteria should map to 100% in Genesys, but we are seeing 85-90%.
Is there a hidden normalization factor in Genesys Cloud Quality Management that adjusts for criterion difficulty or response distribution? In Zendesk, the math was strictly additive. We need to understand if Genesys applies a statistical adjustment or if our weight configuration in the JSON payload is being interpreted differently than expected. Any insights on aligning these scoring models would be appreciated.
If I remember correctly, this scoring mismatch often stems from how the API handles float precision versus integer rounding during bulk imports. When migrating from simple point totals to weighted rubrics, the weight field in the evaluation criteria is critical. If the weights do not sum exactly to 1.0 (or 100.0 depending on your form configuration), Genesys Cloud may apply default normalization that skews the final score.
Cause: The REST endpoint /api/v2/quality/evaluations/forms expects precise decimal values for weights. Zendesk’s Sunshine Conversations might have used integer-based scoring, but Genesys Cloud calculates the final score as (score / max_score) * weight. If your JMeter test or migration script truncates these decimals, the aggregate score will deviate from the expected parity. Additionally, check if you are hitting API rate limits during the import. High concurrency can cause partial updates, leading to inconsistent weight assignments across form sections.
Solution: Verify the JSON payload structure before sending. Ensure all weight fields are explicit floats. Here is a corrected snippet for a single criterion:
{
"criteria": [
{
"id": "criteria_123",
"weight": 0.25,
"scoring": "numeric",
"min_value": 0,
"max_value": 100
}
]
}
Run a small batch test with 10 concurrent threads first to validate the score calculation logic. Monitor the response body for any validation warnings. If the scores still drift, check the form’s calculation method setting. Switching to weighted_average explicitly in the form definition can help ensure parity with the old system. Also, ensure your load test script respects the 10 requests per second limit for the Quality API to avoid 429 errors that might interrupt the weight application process.
What’s probably happening here is that related to how the payload structure handles the weight field versus the points field during the migration process. When moving from Zendesk’s flat scoring to Genesys Cloud’s weighted system, the API expects explicit normalization. If the weights do not sum to exactly 1.0, the platform applies an automatic normalization factor that can drastically alter the final score calculation.
In our experience building integrations for multi-org environments, we often see this discrepancy when the source data retains integer-based point values while the target form expects decimal weights. The /api/v2/quality/evaluations/forms endpoint requires that each criterion object includes both a points value (the max score for that item) and a weight (the relative importance).
To resolve this, ensure your import script calculates the weight as a float between 0 and 1, where the sum of all weights equals 1.0. Here is a sample payload structure that prevents normalization errors:
{
"criteria": [
{
"name": "Greeting",
"points": 10,
"weight": 0.2,
"type": "numeric"
},
{
"name": "Resolution",
"points": 40,
"weight": 0.8,
"type": "numeric"
}
]
}
If you are pulling data from Zendesk where total points might be 100, you must divide each criterion’s point value by the total to derive the correct weight. Failing to do this will cause Genesys Cloud to treat the weights as raw values, leading to the score inflation or deflation you are observing. Always validate the sum of weights before pushing to the API. For detailed schema requirements, refer to the Quality Evaluation Forms API Documentation.