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The By Hold Etiquette metric automatically evaluates how agents manage customer holds during voice interactions, ensuring consistent adherence to hold standards across all interactions. The metric assesses three aspects:
AspectRequiredDescription
Pre-hold NotificationMandatoryVerifies that agents inform customers before placing them on hold
Hold Duration ComplianceOptionalChecks whether hold times stay within acceptable limits
Call Resumption BehaviorOptionalEvaluates how agents reconnect with customers after a hold
The metric integrates with telephony systems to detect hold events and can evaluate multiple hold instances in a single conversation.

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Key Benefits

  • Evaluate 100% of hold interactions automatically.
  • Identify coaching opportunities and compliance gaps.
  • Improve customer experience during hold scenarios.
  • Maintain regulatory compliance across all agent conversations.
This metric is available for voice channel interactions only. One Hold Etiquette metric is allowed per evaluation form. Telephony integration is required to detect hold events accurately.

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Create By Hold Etiquette Metric

  1. Navigate to Quality AI > Configure > Evaluation Forms > Evaluation Metrics.
  2. Select + New Evaluation Metric.
  3. From the Evaluation Metrics Measurement Type dropdown, select By Hold Etiquette. By Hold Etiquette Metric
  4. Enter a descriptive metric Name (for example, “Holding Customer for a Long Time”).
  5. Select the applicable Languages for this metric. You must select at least one. Languages can be added or removed later.
  6. Enter an evaluation Question describing what the metric measures (for example, “Did the agent inform the customer before placing them on hold?”). Basic Configuration
  7. Configure Hold Notification — verifies whether agents properly informed customers before placing them on hold. Select an Evaluation Method:

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    Gen AI-Based Method

    Uses generative AI (LLM) to evaluate whether the agent’s speech meets defined etiquette criteria.
    • In Description, enter the expected behavior (for example, “The agent must inform the customer before placing them on hold using courteous language”).
    • Set the Resumption Behavior Evaluation Window — time period after a hold ends within which resumption behavior is assessed. Gen AI Evaluation Method
    • Configure Advanced Settings — define the detection window for hold notification:
      OptionDescription
      Last utterance before hold initiationEvaluates only the final statement before the hold. Silences and dead air are excluded.
      Configured window before hold initiationSet a custom detection window (1–100 seconds) before the hold. Utterances within this window are valid for compliance.
      Advanced Settings

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    Deterministic ML Method

    Uses BGE-m3 embeddings to compare agent utterances against sample phrases.
    This method uses BGE-m3 embeddings to compare agent utterances to sample phrases with configurable similarity thresholds.
    • In Answer, add sample utterances (for example, “I need a moment to get the details. May I put you on hold?”).
      • Add multiple utterances per configured language. Each language has its own set.
      • Check/uncheck utterances to include or exclude them.
    • Set the Similarity threshold (0–100%, default 60%):
      • Orange = below threshold (0–60%)
      • Green = above threshold (60–100%)
      Hold Notification Deterministic
  8. Configure Sub-Criteria (optional settings to assess hold-related behaviors): Hold Duration Compliance:
    • Toggle on to enable duration-based evaluation.
    • Enter the Maximum Acceptable Hold Duration (1–300 seconds, default: 30 seconds).
    Call Resumption Assessment:
    • Toggle on to evaluate how effectively agents resume the conversation after a hold.
    • Evaluates whether the agent acknowledges the delay, reconnects context, and proceeds smoothly.
    Sub-Criteria Configuration
  9. When Call Resumption Assessment is enabled, select an Evaluation Method: Gen AI:
    • In Description, define expected resumption behavior (for example, “Agent smoothly resumes the conversation, acknowledges the wait time, and proceeds with relevant information”).
    • Set the Resumption Behavior Evaluation Window (1–120 seconds, default: 10 seconds). Gen AI Call Resumption
    • Assign Sub-Criteria Weightage — weights must total 100%:
      Sub-CriterionDescription
      Hold NotificationWeight for courtesy and pre-hold notification compliance
      Hold DurationWeight for compliance with hold time limits
      Call ResumptionWeight for smooth and contextual post-hold resumption
      Gen AI Sub-Criteria Weightage
    Deterministic ML:
    • Add sample utterances agents should use after resuming from hold.
    • Set the Similarity threshold (0–100%).
    • Set the Resumption Behavior Evaluation Window (1–120 seconds, default: 10 seconds). Call Resumption Deterministic ML
  10. Select Create.
The Create button stays disabled until all sub-criterion weights total exactly 100%. Once saved, this metric becomes active and appears in the Evaluation Metrics dashboard.

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Scoring Logic

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Sub-Criterion Weightage Example

Sub-CriterionExample WeightDescription
Hold Notification40%Courtesy and pre-hold notification compliance
Hold Duration30%Compliance with the maximum allowed hold time
Call Resumption30%Smoothness and clarity of post-hold interaction
Instance score formula: (Hold Notification Result × 40%) + (Hold Duration Result × 30%) + (Call Resumption Result × 30%)

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Deterministic ML (Binary)

  • Pass (1) — duration ≤ configured threshold or utterance matches sample
  • Fail (0) — duration > threshold or no utterance match found
Example: Hold Notification (40%): Pass, Call Resumption (30%): Pass, Hold Duration (30%): Fail → Total Score = 70%

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GenAI (Continuous)

Each sub-criterion produces a semantic outcome (Yes/No or graded response). The outcome is multiplied by its assigned weight to generate a continuous, non-binary score.