- What is the standard approach to map Zendesk SLA buckets to Genesys Cloud Predictive Routing scores during a migration from Zendesk Talk to Genesys Cloud?
- We are migrating a high-volume support queue where Zendesk used priority tags to route tickets. In Genesys Cloud, we are trying to replicate this using Predictive Routing (PR) with a custom model.
- The goal is to ensure that interactions mapped from high-priority Zendesk tickets receive higher PR scores to be routed to agents with specific skills.
- We are using the Genesys Cloud SDK for Python (v5.0.12) to sync data. When we trigger the score update via POST /api/v2/predictiverouting/score, we receive a 400 Bad Request error.
- The error response indicates that the ‘interactionId’ format is invalid, even though we are passing the Zendesk ticket ID directly. We assumed a 1:1 mapping was possible.
- In Zendesk, the SLA policy was straightforward: if a ticket was unresolved for >15 minutes, it jumped to the top of the queue. We want the PR score to reflect this urgency.
- We have created a custom attribute in Genesys Cloud called ‘zendesk_priority’ and are trying to map the Zendesk priority field to this attribute before scoring.
- The Architect flow is set to capture this attribute, but the Predictive Routing engine seems to ignore it or fail to update the score in real-time.
- Is there a specific format required for the interactionId when mapping external Zendesk tickets? Or do we need to create a synthetic interaction ID in Genesys Cloud first?
- We noticed that the GET /api/v2/predictiverouting/score endpoint returns null for these interactions, suggesting the scoring engine is not recognizing them.
- Timezone settings are aligned to Europe/Paris, so time-based SLA triggers should match, but the scoring delay is causing agents to miss critical tickets.
- Any advice on how to properly bridge Zendesk’s static priority system with Genesys Cloud’s dynamic Predictive Routing scoring would be appreciated.
- We want to avoid a full custom integration if possible and stick to standard API endpoints.
According to the docs, they say that Predictive Routing scores are internal confidence metrics, not direct equivalents to Zendesk SLA buckets. You cannot map them 1:1 because PR scores represent the likelihood of a successful interaction, while Zendesk tags represent static priority levels. For legal discovery, this distinction is critical. The metadata payload in your bulk export jobs will show the routing_prediction_score as a float between 0 and 1, which does not correlate to the integer-based priority tags used in Zendesk.
To replicate the routing logic, you need to adjust your Architect flow instead. Use the incoming interaction attributes to set a custom variable based on the original Zendesk tag. Then, use this variable as a filter in your queue configuration or as an input feature for your custom PR model. Do not try to force the PR score to match the Zendesk priority. This approach preserves the chain of custody for audit trails, as the original priority data remains intact in the interaction metadata rather than being overwritten by a dynamic score.