Architecting Multi-Channel Evaluation Forms Supporting Voice, Chat, and Email Assessment

How-To-Articles
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Architecting Multi-Channel Evaluation Forms Supporting Voice, Chat, and Email Assessment

What This Guide Covers

This guide details the architectural pattern for implementing unified Quality Management (QM) evaluation forms that dynamically adapt to Voice, Chat, and Email interactions within Genesys Cloud CX. You will configure a single, polymorphic evaluation form that utilizes conditional visibility and cross-channel data mapping to assess agent performance across disparate media types, ensuring consistent quality scoring logic while respecting the distinct behavioral nuances of each channel.

Prerequisites, Roles & Licensing

  • Licensing: Genesys Cloud CX 2 or CX 3 license with the Workforce Engagement Management (WEM) add-on. Specifically, the Quality Management module must be enabled.
  • Permissions:
    • quality > evaluation form > view
    • quality > evaluation form > edit
    • interaction > interaction:view (for troubleshooting data mapping)
  • External Dependencies:
    • Active recording storage (for Voice).
    • Chat/Email transcript generation enabled in the interaction settings.
    • A defined set of Quality Rules that trigger evaluation creation for Voice, Chat, and Email.

The Implementation Deep-Dive

1. Foundation: The Polymorphic Evaluation Form Structure

The core architectural challenge in multi-channel QM is not the creation of separate forms for each channel, but the maintenance of a unified form that prevents cognitive dissonance for evaluators. If an evaluator sees a question about “hold time” while reviewing a Chat transcript, the interface becomes noisy and prone to error. We solve this by leveraging Section Visibility Conditions and Dynamic Data Binding.

We do not create three separate forms. We create one form with three distinct sections: Voice Specific, Digital Specific (Chat/Email), and Universal.

Step 1.1: Define the Universal Section

Create a new Evaluation Form. Add a Section titled Universal Interaction Quality. This section will contain questions applicable to all channels, such as “Did the agent verify customer identity?” or “Was the tone professional?”.

The Trap: Using static text for universal questions that assume a specific channel. For example, asking “Did the agent greet the caller?” is invalid for Email or Async Chat.
The Fix: Use generic phrasing: “Did the agent initiate the interaction with a professional greeting?”

Step 1.2: Define the Voice Section

Add a Section titled Voice Interaction Metrics. Add questions specific to telephony:

  • “Did the agent minimize hold time?”
  • “Was the transfer handled smoothly without dropping the call?”
  • “Did the agent use proper call closing protocols?”

Critical Configuration:
In the Section settings, locate the Visibility Condition. You must set this condition to evaluate the interaction type.

  • Condition Type: Interaction Data
  • Field: Type
  • Operator: Equals
  • Value: CALL

This ensures that when the evaluator opens an evaluation for a Voice interaction, this section appears. For Chat or Email, it remains hidden.

Step 1.3: Define the Digital Section (Chat & Email)

Add a Section titled Digital Interaction Metrics. Add questions specific to text-based media:

  • “Did the agent use appropriate formatting and grammar?”
  • “Was the response time within SLA?”
  • “Did the agent utilize macros/templates effectively?”

Critical Configuration:
Set the Visibility Condition for this section.

  • Condition Type: Interaction Data
  • Field: Type
  • Operator: In
  • Value: CHAT, EMAIL

The Trap: Combining Chat and Email into one section without distinguishing their async vs. sync nature. While they share many metrics, Email often requires different evaluation criteria regarding response latency compared to real-time Chat. If your business logic requires distinct scoring for Email latency, you must split this into two sections with conditions Type Equals CHAT and Type Equals EMAIL. For this guide, we assume unified Digital metrics.

2. Advanced Data Binding: Dynamic Contextual Questions

A static form is insufficient for high-maturity QM programs. You must inject dynamic data from the interaction into the evaluation form to provide context without forcing the evaluator to search through transcripts or recordings.

Step 2.1: Injecting Transcript Snippets into Voice Evaluations

For Voice interactions, evaluators often need to reference specific moments. While Genesys Cloud provides the recording player, injecting key metadata improves efficiency.

Create a Text Field in the Universal Interaction Quality section.

  • Label: Customer Issue Summary
  • Type: Text
  • Read-Only: True
  • Default Value: Use the Expression Builder.

Expression Construction:
You cannot directly pull the full transcript into a text field for Voice due to character limits and performance. Instead, pull the Summary field if populated by Speech Analytics, or the Queue Name.

  • Expression: interaction.queue.name

If you have Speech Analytics enabled, you can map the speech_analytics.summary field.

  • Expression: interaction.speech_analytics.summary

The Trap: Attempting to map the entire interaction.transcript field for Voice calls. Voice interactions do not have a transcript field in the same structure as Chat. This will result in a null value or a mapping error. Always verify the data model for the specific interaction type.

Step 2.2: Injecting Chat History into Digital Evaluations

For Chat and Email, the transcript is the primary artifact. You can inject the first few messages to provide immediate context.

Create a Text Field in the Digital Interaction Metrics section.

  • Label: Initial Customer Message
  • Type: Text
  • Read-Only: True

Expression Construction:

  • Expression: interaction.transcript[0].text

This pulls the very first message in the transcript. If the first message is from the agent (e.g., an automated greeting), you may want to filter for the first customer message. This requires more complex expression logic or a pre-processing step, but for most implementations, the first message is sufficient context.

The Trap: Using interaction.transcript without indexing. The transcript field is an array. Referencing it directly returns the array object string, which is unreadable. You must index it, e.g., [0] for the first message, [1] for the second.

3. Conditional Logic Based on Interaction Attributes

Beyond channel type, your evaluation form should adapt to the content of the interaction. This is achieved through Attribute-Based Visibility.

Step 3.1: Mapping Custom Attributes

Assume your IVR or Digital Engagement flow sets a custom attribute interaction.attributes.issue_type with values like Billing, Technical, or General.

Add a Section titled Billing Specific Compliance.

  • Visibility Condition:
    • Condition Type: Interaction Data
    • Field: Attributes.issue_type
    • Operator: Equals
    • Value: Billing

Inside this section, add questions:

  • “Did the agent verify PCI compliance by not recording full credit card numbers?”
  • “Was the refund policy explained accurately?”

The Trap: Relying solely on Queue-based routing for attribute mapping. If an agent transfers a call from Technical to Billing, the issue_type attribute might not update if the Architect flow does not explicitly re-set it. Ensure your Architect flows update interaction.attributes.issue_type at every major routing decision point.

Step 3.2: Dynamic Scoring Weights

Genesys Cloud does not natively support dynamic weighting of questions based on conditions within a single form. However, you can simulate this by using Section Weights or by creating separate forms for high-stakes scenarios.

For a unified form, it is recommended to keep question weights static but use the visibility conditions to ensure that only relevant questions are scored. If a section is hidden, its questions do not contribute to the total score, nor do they penalize the agent. This is a critical distinction: Hidden questions are ignored, not scored zero.

The Trap: Assuming hidden questions are scored as zero. They are excluded from the calculation entirely. If you have 10 questions visible and 5 hidden, the score is calculated out of 10. If you want to penalize an agent for missing a critical step that is hidden, you must use a different architectural pattern, such as a mandatory “Pass/Fail” gate question that is always visible but contextually relevant.

4. Integration with Speech Analytics and Digital Analytics

To elevate the evaluation from subjective to objective, integrate automated analytics.

Step 4.1: Voice - Speech Analytics Sentiment

Add a Number Field in the Voice Interaction Metrics section.

  • Label: Customer Sentiment Score
  • Type: Number
  • Read-Only: True
  • Default Value: interaction.speech_analytics.sentiment_score

This pulls the aggregated sentiment score from the Speech Analytics engine. The evaluator can see if the customer was angry, neutral, or happy, providing context for the agent’s performance.

Step 4.2: Digital - Digital Analytics Keywords

For Chat/Email, you can map detected keywords or topics.

  • Label: Detected Topics
  • Type: Text
  • Read-Only: True
  • Default Value: interaction.digital_analytics.topics

Note: The exact field path for Digital Analytics may vary based on your specific integration and data model version. Consult the Genesys Cloud API documentation for the latest digital_analytics schema.

The Trap: Over-reliance on automated scores. Sentiment scores can be misleading in sarcasm or complex technical explanations. Always include a disclaimer in the form or training: “Automated scores are for context only; final evaluation is at the evaluator’s discretion.”

Validation, Edge Cases & Troubleshooting

Edge Case 1: The “Null Transcript” Error in Chat Evaluations

The Failure Condition:
An evaluator opens a Chat evaluation form. The Initial Customer Message field displays “null” or is blank, even though the chat occurred successfully.

The Root Cause:
This typically occurs when the Chat interaction was terminated abruptly (e.g., customer closed the browser) before the transcript was fully committed to the interaction object, or when the chat was handled by a bot that does not populate the transcript array in the standard format. Additionally, some legacy chat integrations may store transcripts in a different data structure.

The Solution:

  1. Verify the interaction data using the Genesys Cloud API Explorer. Endpoint: GET /api/v2/interactions/{id}.
  2. Inspect the transcript array. If it is empty, the issue is upstream in the Engagement flow.
  3. If the transcript exists but the form shows null, check the Expression Builder syntax. Ensure you are handling potential nulls. You can use a conditional expression:
    if(interaction.transcript.length > 0, interaction.transcript[0].text, "No transcript available")
    This prevents the “null” display and provides a clear indicator to the evaluator.

Edge Case 2: Mixed-Channel Interactions (Voice to Chat Transfer)

The Failure Condition:
An agent takes a Voice call, then transfers the customer to a Chat session (or vice versa). The evaluation form for the Voice interaction shows Digital metrics, or the Digital evaluation shows Voice metrics.

The Root Cause:
Genesys Cloud treats Voice and Chat as separate interaction IDs, even if they are part of the same customer journey. However, if you are using Unified Interaction features or if the Quality Rule is set to evaluate “Agent” rather than “Interaction”, the system may attempt to apply the form to the wrong context. More commonly, this issue arises when an agent handles a Voice call that includes a screen share or co-browse feature, which might be logged as a separate digital interaction linked to the same session.

The Solution:

  1. Ensure your Quality Rules are strictly defined by Interaction Type. Do not create a rule that evaluates “All interactions for Agent X”. Instead, create separate rules:
    • Rule 1: Trigger on Interaction Type = CALL, Form = Unified Multi-Channel Form.
    • Rule 2: Trigger on Interaction Type = CHAT, Form = Unified Multi-Channel Form.
  2. The form itself handles the visibility. As long as the rule triggers on the correct interaction type, the visibility conditions within the form will correctly show/hide sections.
  3. If you are evaluating a “Session” that spans multiple interactions, you must use Workforce Engagement Management’s Session-level evaluations. This is a more advanced feature that requires aggregating multiple interaction IDs. For standard QM, stick to interaction-level evaluations.

Edge Case 3: Attribute Mapping Latency

The Failure Condition:
The Billing Specific Compliance section does not appear for a Voice call, even though the IVR clearly set the issue_type to Billing.

The Root Cause:
Custom attributes set in the IVR (Architect flow) may not be immediately available to the Quality Management module if the evaluation is created before the attribute is fully persisted. This is a race condition between the interaction completion and the evaluation trigger.

The Solution:

  1. Delay the evaluation trigger. In the Quality Rule settings, set a Delay of 5-10 minutes after the interaction ends. This allows time for all attributes, analytics, and transcripts to be fully processed and stored.
  2. Verify the attribute persistence. Use the API to check if interaction.attributes.issue_type exists immediately after call completion. If it does not, your Architect flow is not setting the attribute correctly on the interaction object, but rather on the dialog only. Ensure you use the Set Interaction Attributes block in Architect.

Official References