Building Agent Efficiency Heatmaps with Handle Time and Disposition Analytics

How-To-Articles
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Building Agent Efficiency Heatmaps with Handle Time and Disposition Analytics

What This Guide Covers

This guide details the configuration of a custom reporting solution within Genesys Cloud CX to visualize agent efficiency across time intervals using Average Handle Time (AHT) and disposition code data. The end result is a dynamic grid view where cell intensity reflects performance density, allowing managers to identify systemic bottlenecks or individual outliers in real-time and historical contexts. You will configure the underlying report type via API to ensure data granularity, then apply conditional formatting rules within the Reporting Workspace to render the visual heatmap directly in the platform.

Prerequisites, Roles & Licensing

Before attempting this implementation, verify that your organization meets the following licensing and permission requirements. This configuration relies on advanced Analytics capabilities which are not available in entry-level tiers.

  • Licensing Tier: Genesys Cloud CX Subscription (Level 2 or 3) is required to access Custom Report Types and Data Warehouse features. Basic Level 1 subscriptions may lack the necessary aggregation functions for custom reporting grids.
  • Analytics Workspaces Access: The user account performing the configuration must have the Analytics > Reporting permission set to Edit. This allows modification of report types and grid layouts.
  • Data Warehouse Subscription (Optional but Recommended): While native reporting supports this, a Data Warehouse subscription enables historical persistence beyond the 30-day limit in standard Analytics Workspaces. If using DW for long-term heatmaps, ensure the Reporting role is granted access to the specific data warehouse schema.
  • OAuth Scopes: To create custom report types programmatically or via automated deployment pipelines, the OAuth token must include the reporting:readwrite scope. This is critical if you are integrating this configuration into a CI/CD pipeline rather than building it manually in the UI.
  • External Dependencies: Ensure that your telephony provider has successfully pushed disposition codes to the interaction record for all call types. Missing disposition data will result in null values that skew the heatmap analysis.

The Implementation Deep-Dive

1. Defining the Custom Report Type via API

Standard report types in Genesys Cloud often aggregate data at a high level, which obscures granular efficiency patterns required for heatmaps. A custom report type allows you to define specific aggregation keys and metric calculations. You must construct a JSON payload that defines how the system groups interactions by Agent, Hour of Day, and Disposition Outcome.

Begin by accessing the Reporting API endpoint to create the report type. This ensures consistency across environments and prevents manual configuration drift. The following payload defines the necessary schema for an efficiency heatmap.

POST https://aws1.pure.cloud/api/v2/analytics/reporting/reporttypes
Content-Type: application/json
Authorization: Bearer <ACCESS_TOKEN>

{
  "name": "Agent Efficiency Heatmap Source",
  "description": "Aggregates handle time and disposition by agent and hour for heatmap visualization",
  "reportType": "interaction",
  "aggregationKey": "agentId, hourOfDay, dispositionCode",
  "metrics": [
    {
      "name": "Total Interactions",
      "aggregator": "count"
    },
    {
      "name": "Average Handle Time (Seconds)",
      "aggregator": "average",
      "metricKey": "handleTime"
    },
    {
      "name": "Disposition Success Rate",
      "aggregator": "percentage",
      "metricKey": "dispositionCode",
      "condition": "equals: 'Resolved'"
    }
  ],
  "filters": [
    {
      "dimension": "queueId",
      "operator": "equals",
      "value": "00000000-0000-0000-0000-000000000000" 
    },
    {
      "dimension": "startTime",
      "operator": "greaterThanOrEquals",
      "value": "2023-10-01T00:00:00.000Z"
    }
  ],
  "dimensions": [
    "agentId",
    "hourOfDay",
    "dispositionCode"
  ]
}

The Trap: A common misconfiguration involves omitting the hourOfDay dimension in the aggregation keys. Without this dimension, the report aggregates data over the entire period (e.g., the whole month). This creates a single row per agent, which destroys the ability to visualize efficiency trends across different times of day. The resulting output will show average performance but fail to reveal peak-hour inefficiencies that define your heatmap’s value.

Architectural Reasoning: We use hourOfDay because contact center workload is rarely uniform. An agent may perform well during low-volume morning hours but struggle during afternoon peaks due to context switching or system latency. Grouping by hour allows the heatmap to highlight specific time slots where efficiency drops, enabling targeted coaching rather than blanket performance reviews.

2. Configuring Conditional Formatting in the Reporting Workspace

Once the custom report type is created and data begins populating, you must configure the grid layout within the Analytics Workspace to render the data as a heatmap. Native reporting grids support conditional formatting rules that change cell background colors based on metric values.

Navigate to Analytics > Reports and create a new report using the Agent Efficiency Heatmap Source type created in the previous step. Configure the grid columns as follows:

  • Row Headers: Set agentId as the primary row identifier. This ensures each agent has their own row for comparison.
  • Column Headers: Set hourOfDay as the column identifier (00 through 23). This creates a matrix where X-axis represents time and Y-axis represents personnel.
  • Cell Value: Bind the Average Handle Time (Seconds) metric to the cell content.

Apply conditional formatting rules to transform these numeric values into visual indicators. Navigate to the grid settings and select Conditional Formatting. Add the following rules:

  1. High Efficiency (Green):
    • Condition: Average Handle Time (Seconds) is less than 400.
    • Style: Background color #D9EAD3 (Light Green).
  2. Moderate Efficiency (Yellow):
    • Condition: Average Handle Time (Seconds) is greater than 400 AND less than 600.
    • Style: Background color #FCF3CF (Light Yellow).
  3. Low Efficiency (Red):
    • Condition: Average Handle Time (Seconds) is greater than or equal to 600.
    • Style: Background color #E6B9B7 (Light Red).

The Trap: A frequent error in this step involves setting the thresholds based on absolute industry standards rather than your organization’s historical baseline. If you apply a 600-second threshold to a complex technical support queue where 1200 seconds is standard, every cell will turn red, rendering the heatmap useless for differentiation.

Architectural Reasoning: Conditional formatting relies on thresholds that are derived from business service level agreements (SLAs). You must calculate your baseline AHT from historical data before setting these rules. The visual intensity of the heatmap is only valuable if it accurately reflects deviation from your expected performance, not a generic metric. This configuration allows managers to instantly spot agents who consistently exceed time limits during specific hours without scanning raw numbers.

3. Integrating Disposition Codes for Outcome Context

Efficiency cannot be measured by speed alone; an agent might resolve calls quickly but fail to solve the customer’s problem. To incorporate disposition codes into the heatmap, you must create a secondary metric or a stacked visualization approach within the same report.

In the Reporting Workspace grid settings, add a second row of metrics below the AHT cells. Configure this to display the Disposition Success Rate calculated in the custom report type definition. Apply a separate conditional formatting rule set for this metric:

  • Success (Green): Percentage greater than or equal to 95%.
  • Warning (Yellow): Percentage between 85% and 94%.
  • Failure (Red): Percentage less than 85%.

To make the heatmap readable, you must ensure that the grid does not become cluttered. Use the Show As option in the cell configuration to display only the numerical value or a color bar indicator depending on your screen size. If you require both AHT and Disposition data visible simultaneously, configure the grid to allow expansion rows where clicking an agent reveals the detailed breakdown.

The Trap: The most critical failure mode here is assuming disposition codes are consistent across all agents. Agents often have the ability to select different disposition codes for similar outcomes based on their personal preference or training gaps. If Agent A selects Resolved and Agent B selects Closed, your success rate calculation will split the data artificially.

Architectural Reasoning: To mitigate this, you must normalize disposition codes before they reach the report. This is best handled at the application layer or via a routing strategy that forces agents to select specific outcomes for specific call types. If normalization is not possible at the source, use a custom dimension in your Data Warehouse query to map all variation codes (e.g., Closed, Resolved, Completed) into a single canonical category (Successful). Without this mapping, the heatmap will show false negatives in efficiency where the agent actually performed well but labeled it differently.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Statistical Significance on Low-Volume Agents

When viewing a heatmap, an agent with very few interactions may display extreme values that skew perception. For example, if an agent handles only two calls in a day and both have 5-minute handle times, their average is 300 seconds. Conversely, another agent with 100 calls averaging 400 seconds will appear less efficient despite having more consistent data.

The Failure Condition: Managers make coaching decisions based on outliers rather than trends. The heatmap shows a green cell for the low-volume agent, implying high efficiency, which is statistically unreliable.

The Root Cause: Aggregation functions like average do not account for sample size by default in standard reporting grids. A small denominator inflates the variance of the metric.

The Solution: Implement a minimum interaction threshold filter within the report configuration. In the Reporting Workspace filters, add a condition: Total Interactions >= 10. This hides cells where the data sample is too small to be statistically significant for performance review purposes. Alternatively, calculate a confidence interval in your Data Warehouse query and use that as a filter flag before rendering the grid.

Edge Case 2: After Call Work (ACW) Inclusion Discrepancies

Average Handle Time calculations can vary depending on whether After Call Work is included in the metric definition. Genesys Cloud CX defines handleTime as the sum of Talk Time and ACW by default, but some legacy integrations or custom scripts may exclude ACW.

The Failure Condition: The heatmap indicates high efficiency (short AHT) for a specific shift, but quality scores drop simultaneously. This suggests agents are rushing through calls to avoid ACW time being logged, or the system is failing to log ACW duration correctly.

The Root Cause: Inconsistent configuration of the handleTime metric across different reporting layers or external BI tools compared to the native Analytics Workspace.

The Solution: Verify the metric definition in your custom report type payload. Ensure the metricKey corresponds to handleTime and not a derived field that excludes ACW. Cross-reference this with the talkTime and acwTime dimensions separately. If you observe a discrepancy where handleTime is significantly lower than the sum of Talk Time and ACW, investigate telephony trunk configurations for potential timer synchronization issues.

Edge Case 3: Data Latency in Real-Time vs. Historical Views

A common point of confusion arises when users attempt to view efficiency heatmaps in real-time versus historical views. The Analytics Engine processes data with a latency buffer to ensure accuracy.

The Failure Condition: A manager looks at the heatmap during a live shift and sees an agent with zero interactions for the last hour, assuming they are on break or inactive. In reality, the interaction data has not yet propagated to the reporting database.

The Root Cause: Genesys Cloud Reporting operates on a near-real-time basis but typically has a 15 to 30-minute delay for full aggregation accuracy in historical views. Real-time grids update more frequently but may lack the stability required for AHT calculations which require call completion.

The Solution: Clearly label the data freshness timestamp on the reporting dashboard. When configuring the Reporting Workspace, ensure the “Refresh” setting is set to On Load or a scheduled interval that aligns with business needs rather than continuous streaming. For true real-time monitoring of efficiency, use the Real-Time Monitoring (RTM) API endpoints which provide live agent status without waiting for interaction closure.

Official References