Process Engine Reporting
Updated
Sprinklr’s Process Engine Analytics Data Source provides detailed visibility into the execution of voice case workflows. This data source powers reporting on how processes traverse nodes, the success or failure of each execution, and the time taken for execution, helping you optimize and troubleshoot voice workflows, IVRs, After Call Workflows (ACWs), Guided Workflows, and more.
This guide outlines the key dimensions and metrics available in this data source for building effective reports in Sprinklr Reporting.
Use Cases
Monitor the performance of IVR and ACW workflows
Analyze node-level execution times for optimization
Troubleshoot failed workflow executions
Audit the progression of a case through automated workflows
Dimensions
The following dimensions are available for slicing and filtering your workflow execution data:
Dimension | Description |
Execution History Asset ID | Unique identifier of the asset on which the process workflow was triggered. |
Execution History Asset Class | The base entity (e.g., Case, Voice Conversation, Task, Profile) on which the workflow ran. Examples:
|
Execution History Detailed Error | Detailed error message indicating why a node failed to execute. Useful for debugging specific nodes. |
Execution History Duration | Time taken to complete the execution of a node. Especially useful for time-sensitive nodes like API calls. |
Execution History End Time | Timestamp marking the completion of node execution. |
Execution History Error | Short summary of why node execution failed. |
Execution History Node ID | Unique identifier for the node executed in the workflow. |
Execution History Process Definition ID | Unique identifier of the workflow (process definition) executed. |
Execution History Process Definition Name | Name of the process workflow that was executed. |
Execution History Start Date | Timestamp when the execution of the node started. |
Execution History Token ID | Unique execution ID for each instance of workflow execution. |
Status | Status of node execution — typically Success or Failed. |
Metrics
These metrics provide quantitative measurements for process performance tracking:
Metric | Description |
Execution History Duration (Measurement) | Total time taken to execute a specific node. Important for measuring the responsiveness of API and logic-heavy nodes. |
Execution History Total Process Executions | Total number of unique workflow executions. Derived from the count of unique Execution History Token ID values. |
Best Practices for Reporting
Break down by Asset Class to segment workflows based on entity type (e.g., Voice Conversation vs. Case).
Use Start and End Timestamps to track delays or bottlenecks in node execution.
Monitor Failure Rates using the Status and Execution History Error dimensions to quickly identify problematic nodes.
Leverage Execution Duration to highlight nodes that may benefit from optimization or redesign.
Sample Use Case: Identifying Bottlenecks in IVR Workflows
To identify slow or failing IVR nodes:
Filter Execution History Asset Class = Voice Conversation.
Add Execution History Node ID, Execution History Duration, and Status as report fields.
Sort by Execution History Duration in descending order.
Investigate nodes with longer durations or frequent failures.
Troubleshooting Tips
Issue | Resolution |
Execution shows as Failed | Check Execution History Error and Detailed Error for specifics. |
No data returned | Verify the correct entity (Asset Class) and time range is selected. |
Node Duration is zero | May indicate skipped execution or instantaneous logic. Cross-verify with node configuration. |
Process Engine Reporting equips Sprinklr users with in-depth analytics on workflow performance. By using the right combination of dimensions and metrics, organizations can pinpoint issues, optimize execution, and ensure seamless workflow automation across voice interactions.